strategic multiscalel white book 2012
TRANSCRIPT
White BookWhite BookWhite BookWhite Book
“Strategic Multiscale:
A New Frontier For
R&D and Engineering
Alessandro Formica
March 2012
Alessandro Formica – March 2012 All rights reserved
2
TABLE OF CONTENTS
1. Introduction………………………………………………………………………………... pag. 3
2. Strategic Multiscale Framework…………………………………………………………. pag. 5
2.1 R&D and Engineering Scenario: From Computational To Strategic Multiscale………….. pag. 5
2.2 Strategic Multiscale Framework Architecture………………………………………………… pag. 9
2.3 Strategic Multiscale Framework Goals…………………………………………………………. pag. 10 3. Integrated Multiscale Science - Engineering Framework…………………………….. pag. 12
3.1 Architecture………………………………………………………………………………………. pag. 12
3.2 Multiscale Data, Information and Knowledge Analysis and Management System………pag. 13
3.3 Multiscale Science – Engineering Information Space………………………………………. pag. 20
3.4 Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers…………. pag. 25
3.5 Multiscale Multiresolution Experimentation, Testing and Sensing………………………. pag. 29
3.6 Methodologically Integrated Multiscale Science – Engineering Strategies……………… pag. 34 3.6.1 The Information – Driven Concept………………………………………………………………... pag. 34
3.6.2 Methodological Integration Schemes, Maps and Strategies……………………………………… pag. 37
3.6.3 Multiscale Knowledge – Based Virtual Prototyping and Testing………………………………… pag. 42
3.7 Designing the R&D and Engineering Process………………………………………………. pag. 43 3.7.1 R&D and Engineering Analysis and Design Process Architecture………………………………. pag. 43
3.7.2 R&D and Engineering Analysis and Design Strategy Management System…………………… pag. 44
3.7.3 Integrated Multiscale Science – Engineering Analysis Strategies………………………….. pag. 46
3.8 Applications…………………………………………………………………………………………pag. 50 3.8.1 Multiscale Systems Engineering………………………………………………………………… pag. 50
3.8.2 Multiscale Processing and Manufacturing……………………………………………………….. pag. 54
4. Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-TPPD) Framework……………………………………………………….. pag. 64
4.1 Overview and Architecture………………………………………………………………………… pag. 64
4.2 Computer Aided R&D and Engineering/Processing (CARDE) Framework…………….. pag. 68
4.3 Innovative Technology and System Development Analysis and Planning Framework…… pag. 69
4.4 Multiscale Science – Engineering Knowledge Integrator and Multiplier (KIM) and.
Computing Information Communication Infrastructural Framework………………………....... pag. 73
About the Author…………………………………………………………………………….. pag. 76
Contacts……………………………………………………………………………………….. pag. 78
Alessandro Formica – March 2012 All rights reserved
3
1. Introduction
Relationships between science and engineering, basic and applied research, technology development,
engineering and manufacturing are deeply changing. At the same time, dramatic advances in Computing
Information and Communication (CIC) technologies are reshaping the Research, Industry Scenario and
Cooperative Environments. Accordingly, a new language and theoretical framework to understand and
manage this complex process and drive technology innovation and complex systems design well into the
21st century, is a reasonable need. However, significant methodological advances are needed to take full
advantage of the Computing, Information and Communication (CIC) technological “Revolution” and
effectively cope with educational, industrial, economic, environmental and societal challenges. A new
Integrated Multiscale Multidisciplinary Science - Engineering approach can be regarded as a strategic goal.
The fundamental thesis of this White Book is that, to meet 21st century innovative technology development
and complex systems engineering analysis and design challenges, we need important improvements in
Methodology and the way Information is dealt with inside R&D and Engineering. This development process
can be started by implementing what we call a “Strategic” view of the Multiscale concept and method.
Computational Multiscale is today widely regarded as a “New Frontier” for Computational Science and
Engineering. “Strategic Multiscale” can be a “New Frontier” for R&D and Engineering/Manufacturing
Strategies and Organization.
Strategic Multiscale is not only a new methodology, but a unifying paradigm to enable integration of
science and engineering as it was defined by Villermaux, Ka, Ng, Formica, in the mid of nineties. Central
elements of the Strategic Vision of Multiscale are a new concept of Modeling and Simulation as “Knowledge
Integrators and Multipliers” and “Unifying Paradigm” for Scientific and Engineering Knowledge Domains
and Methodologies and a new set of Multiscale Science - Engineering Data Information and Knowledge
Schemes and Strategies. This Vision directly leads to the extension of the multiscale concept to the
experimental, testing and sensing worlds and a comprehensive integration of a full spectrum of multiscale
computational, experimental, testing and sensing methodologies and related knowledge domains. The
ultimate goal is to define more general “Methodologically Integrated Multiscale Multidisciplinary R&D and
Engineering Strategies”.
The “Strategic Multiscale” Vision embodies three Frameworks:
� “Integrated Multiscale Science – Engineering Framework” which represents the theoretical,
conceptual and methodological basis (Chapter 3)
� “Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-TPPD) Framework” (Chapter 4) which is constituted by:
− a set of Software Environments that implement theories, methods and concepts described in the
previously quoted Framework (Paragraphs 4.2 and 4.3)
− The “Multiscale Knowledge Integrator And Multiplier Computing, Information and Communication
(CIC) Infrastructural Environment” (Paragraph 4.4)
� “ Multiscale Science – Based Education, Information and Communication “Language” and Framework” which describes the application of the Strategic Multiscale concepts and methods to the
Education, Information and Communication Areas. Multiscale “Language” and Framework are
described in the “Multiscale Science – Based Education, Information and Communication Framework”
White Book.
Alessandro Formica – March 2012 All rights reserved
4
General References
David L. McDowell, Jitesh H. Panchal, Hae-Jin Choi. Carolyn Conner Seepersad, Janet K. Allen, Farrokh
Mistree, 2010. Integrated Design of Multiscale, Multifunctional Materials and Products - Published by
Elsevier .
Oden , J.T. , Belytschko , T. , Fish , J. , Hughes , T.J.R. , Johnson , C. , Keyes , D. , Laub , A. , Petzold , L. ,
Srolovitz , D. , Yip , S. , 2006 . Simulation-based engineering science: Revolutionizing engineering science
through simulation . In : A Report of the National Science Foundation Blue Ribbon Panel on Simulation-
Based Engineering Science . National Science Foundation : Arlington, VA .
Olson , G.B. 1997 . Computational design of hierarchically structured materials . Science, 277 ( 5330 ) ,
1237 – 1242 .
Alessandro Formica. Fundamental R&D Trends in Academia and Research Centres and their Integration
into Industrial Engineering Report drafted on behalf of European Space Agency, July 2000
Alessandro Formica, Multiscale Science – Engineering Integration A New Frontier for Aeronautics, Space
and Defense, Italian Association of Aeronautics and Astronautics (AIDAA), March 2003
Alessandro Formica – March 2012 All rights reserved
5
2. Strategic Multiscale Framework
2.1 R&D and Engineering Scenario: From Computational to Strategic Multiscale
In mid of nineties several researchers in the Chemical Engineering field (Sapre and Katzer, Lerou and Ng,
and Villermaux) and the author of this White Book (Alessandro Formica) highlighted the need of a
comprehensive multiscale approach as a key strategic step to establish a new “Unifying Paradigm” to enable
a better correlation between scientific and engineering advances and knowledge domains. This vision was highlighted, at the 5
th World Congress of Chemical Engineering (1996), San Diego, CA,
USA, by the late lamented Prof. Jacques Villermaux (at that time Vice President of European Federation of
Chemical Engineering) Later on, Prof. Charpentier, past European Federation of Chemical Engineering
President, illustrated similar concepts:
Taking advantage of these conceptual advances, in the White Book “Multiscale Science – Engineering
Integration – A New Frontier for Aeronautics, Space and Defense (May 2003) sponsored and published by
Italian Association of Aeronautics and Astronautics (AIDAA), Formica outlined the concept of “Strategic
Multiscale” and he described a first version of the related Integrated Framework.
Strategic Multiscale is the theoretical and methodological basis to change R&D and Engineering
organization, structure and strategies correlating, inside a coherent Framework, science and engineering
analytical, computational and experimental, testing and sensing methodologies and techniques. New
Frameworks and new Data, Information and Knowledge Management Systems, based on the multiscale
science - engineering integration concept, can contribute to organize scientific knowledge in such a way as
to make it directly applicable Technology Development and Engineering/Manufacturing/Processing and
redefine knowledge transfer along the whole chain: basic research, applied research, technology
development and integrate R&D, engineering, manufacturing and operational testing.
Multiscale as “Unifying Paradigm for Chemical Engineering
Prof. Charpentier, past European Federation of Chemical Engineering (EFCE) President, at the 6th
World Congress of Chemical Engineering - Melbourne 2001, described his Vision of Multiscale as
“Strategic Paradigm” for Chemical Engineering.
We report his words :
“One key to survival in globalization of trade and competition, including needs and challenges, is the
ability of chemical engineering to cope with the society and economic problems encountered in the
chemical and related process industries. It appears that the necessary progress will be achieved via a
multidisciplinary and time and length multiscale integrated approach to satisfy both the market
requirements for specific end use properties and the environmental and society constraints of the
industrial processes and the associated services.
This concerns four main objectives for engineers and researchers:
(a) total multiscale control of the process (or procedure) to increase selectivity and productivity,
(b) design of novel equipment based on scientific principles and new methods of production: process
intensification,
(c) manufacturing end-use properties for product design: the triplet ‘processus-product-process’
engineering,
(d) implementation of multiscale application of computational modeling and simulation to real-life
situations: from the molecular scale to the overall complex production scale.”
Alessandro Formica – March 2012 All rights reserved
6
The drivers for a new vision of Multiscale come from some specific features which characterize modern
R&D and Engineering/Manufacturing Scenario. Three key issues, in particular, play a key role:
a) Integration of Science and Engineering (nanotechnology is only the most evident sign of this process)
b) Performance and Optimization pushed to the limits
d) Growing complexity of engineering systems and of the related R&D and Engineering Processes
These issues heavily condition innovative technology and system development times, costs and risks and
programs organization, structure and management.
a) Science-Engineering Integration: Science has become a key Technological and Engineering
Variable Science – Engineering Integration means that Scientific knowledge is increasingly at the root of new
technology developments in key fields such as materials, materials processing, electronics, communication,
information processing, computing, optics, propulsion, clean by design solutions,…..
Technological Systems integrate a wide spectrum of sub-systems, components and devices working not only
at the classical engineering scales (macro and meso), but, also, at scientific space and time scales (micro and
nano). Nano and Micro technologies are territories where science and engineering meet together. Nano
technologies open the way to the definition of a new generation of “inherently hierarchical multiscale
materials, devices and systems”. We are seeing the birth of new fields: “Quantum Engineering” and
“Hierarchical Multiscale Nano To Macro Engineering”
b) Performance, Requirements and Optimization pushed to the limits Pushing performance, requirements and optimization to the limits means that understanding, predicting, and
controlling systems dynamics is increasingly dependent on understanding, predicting, and controlling a
hierarchy of physical (chemical and biological) mutually interacting phenomena occurring at a wide range of
space and time scales. In this context, small phenomena at the lowest scales can have a major impact on the
behaviour of a macro system. Accordingly, classical homogenization and averaging procedures, as well as
semi empirical or simplified formulations, which worked well in the past, are, in many cases, no longer up to
the challenge
c) Complexity “Complexity” is a very general, if not generic at all, term. It is possible to relate “Complexity” to our
capability of understanding, predicting and controlling the dynamics of a “System. In the context of this
White Book, we consider five interrelated types of complexity :
� Physics Complexity (directly related to multiscale and the science-engineering integration issue) Multiscale Multiphysics hierarchies of physical phenomena and processes underlie the behaviour of
systems, sub-systems, components, devices and states of matter (materials, fluids, plasmas).
� Requirements, Functional and Operational Complexity Widening range of functions to be performed
by systems, widening operational envelope and widening spectrum of requirements to be met (energy
efficiency, environmental compliance, safety, development and operational costs, life – cycle issues,…)
� System Complexity A technological System is constituted by a full hierarchy (from macro to nano) of
subsystems, components and devices which use a wide range of different technologies (mechanical, bio,
info, electronics, optoelectronics, fluidics,…) and operate across a widening range of scales. That
implies a network of interactions among the full hierarchy of subsystems, components and devices which
span a wide spectrum of different space and time scales and, globally, condition the macro life – cycle
performance of the system
� R&D and Engineering Process Complexity This kind of complexity can be identified as: the
“Fragmentation Issue” for R&D and Engineering. Said in more specific terms, it refers to the always
continuously growing spectrum of models, methods, data, and information characterizing R&D and
Engineering Processes. The “Fragmentation Issue” heavily condition architecture, organization, and
structure of the R&D and Engineering processes associated with innovative technology and system
development.
Alessandro Formica – March 2012 All rights reserved
7
� Uncertainties Management Complexity Uncertainties are related to any aspect of the R&D and
Engineering process. The lack of a comprehensive and rigorous strategy to deal with uncertainties, in a
systematic way, inside the R&D and Engineering process, seriously limits our capability of reliably
predicting systems behavior, selecting alternative technological and engineering solutions, validating
computational and experimental, testing and sensing methods/techniques, defining the right mix among
theory, modeling & simulation, and experimentation & testing. Uncertainty is a function of physics
(scales and disciplines), systems and process complexity. Sources of Uncertainties (the list is not
exhaustive):
− Physics: If you do not know physics it is very difficult to assess model reliability, predictive
capabilities and applicability conditions.
− Geometry: The continuous reduction of the dimension of devices, components, and subsystems
makes even small geometric errors ever more critical
− Manufacturing: The ever growing relevance of even very small structural and compositional
variations on properties and performance of processed/manufactured materials and parts.
− Modeling: Uncertainty characterizes modeling hypotheses, input data, initial and boundary
conditions.
− Operational Environment (Loading Conditions) : Interactions between an high-tech system and its
operational environment involve highly complex physical phenomena and a multitude of different
nominal and off-nominal cases and situations.
− System Complexity: Interactions among devices, component, and subsystems making up a high-tech
systems involve a highly entangled pattern of multiscale, multimedia, multidisciplinary phenomena
which is practically impossible to characterize in a deterministic way.
− Information Uncertainty. Last but not least, we take into account what we can call the “Information
Uncertainty Challenge”. An often neglected uncertainty is linked to the determination of what
information is needed in critical tasks of the R&D and engineering process, what is the needed level
of accuracy, what is the range of validity and reliability level of (computational and experimental &
testing) models, what is the right mix between modeling & simulation and experimentation & testing
to accomplish tasks.
Two particularly critical issues are : “known unknowns” (unknown solutions to known problems) and the so
called “unknown unknowns” (unknown sources of uncertainty). Both have a critical relevance for R&D and
Engineering.
Fig.1 from “Modeling and Simulation In Support of T&E and Acquisition” Dr. Frank Mello, OSD/DOT&E
Presented at: The International Congress & Exhibition on Defense Test and Evaluation and Acquisition:
The Global Marketplace Vancouver, British Columbia, Canada
Alessandro Formica – March 2012 All rights reserved
8
Multiscale Development Stages
It is possible to identify four fundamental stages in the development of Multiscale and Science – Engineering
Integration:
a) Computational Multiscale methods to address specific R&D and Engineering issues. It is the basic
development stage. There is an increasing activity to improving existing computational multiscale methods
and develop new schemes and strategies (adaptive, concurrent, hierarchical,….)
b) Integrated Computational Multiscale Framework to address complex R&D and Engineering tasks.
Materials and Biology are key application fields. It can be considered as the “State of the Art”.
c) Extension of the Multiscale approach to the Experimentation, Testing and Sensing fields. Activities are
underway
d) “ Strategic Multiscale”, based upon the “Strategic Vision of Multiscale”, which could be a starting point to
change organization and structure of the R&D and Engineering landscape. A first sketch of a possible
structure of this kind of Frameworks and related application fields is outlined in this White Book.
The theoretical and methodological basis of the “Strategic Multiscale” Vision is constituted by the following
key elements:
� The Multiscale concept and method as basic theoretical and methodological element, extended, in this
context, to the experimental, testing and sensing fields
� A new “Vision” of Multiscale Modeling & Simulation as “Knowledge Integrators and Multipliers” and
“Unifying Paradigm” for Scientific and Engineering Methodologies and Knowledge Domains. In this
perspective “Modeling & Simulation” integrate the full spectrum of science and engineering
methodological approaches and knowledge environments.
� The “Multiscale Science-Engineering Information Space” concept to integrate data, information and
knowledge from computational models and methods and experimental, testing and sensing models and
techniques
� The “Information – Driven Analysis” concept and scheme which, together with the Science –
Engineering Information Space” concept is a key element to shape Multiscale Methodologically
Integrated R&D and Engineering Analysis Strategies
� New Multiscale Science – Engineering Data, Information and Knowledge Management Systems based
upon the Multiscale Maps concept
� New methods to “Design” the R&D and Engineering Process
The “Integrated Multiscale Science-Engineering Framework” represents the basis to perform the transition:
���� From traditional CAD, CAE, CAM systems to Multiscale Science - Engineering CAD, CAE, CAM
systems
���� From “Integrated Product and Process Development (IPPD)” Frameworks to a new generation of
Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-
TPPD)”Frameworks
It is of fundamental importance to highlight that the definition of an “Integrated Multiscale Science-
Engineering Framework” does not mean that specific aspects and peculiarities of Basic Research, Applied
Research, Technology Development and Engineering should be canceled and/or neglected and that all the
activities in these different Scientific and Engineering domains should be tightly correlated and inserted
inside global rigid schemes. In this White Book “Multiscale Science – Engineering Integration” means that :
− Scientific Knowledge should be structured in such a way as to make it directly applicable inside
Engineering processes and domains (Science – Driven Engineering)
− Requirements, performance, properties can be propagated along the Technology Readiness Level (TRL)
development scheme following a two – way approach (from TRL 0 to TRL 9 and from TRL 9 to TRL 0)
− Requirements, performance, properties can be linked over the full spectrum of scales and the hierarchy
of system architectural elements for complex systems.
Note: Multiscale is a general term, it incorporates, as a special case, classical single scale methods and
models. Multiscale stands for Multiscale Multiresolution Multiphysics in the most general case.
Alessandro Formica – March 2012 All rights reserved
9
2.2 Strategic Multiscale Framework Architecture
The overall “Strategic Multiscale Framework” is constituted by three specific interrelated Frameworks:
A. “ Integrated Multiscale Science - Engineering Framework” which describes the fundamental concepts
and methods to develop upon new R&D and Engineering Strategies and related SW Environments
(Chapter 3)
B. “Integrated Multiscale Science – Engineering Technology, Product and Process (IMSE-TPPD)
Framework” which represents the Integrated Product and Process Development (IPPD) Frameworks next
Generation (Chapter 4)
− Computer Aided R&D and Engineering (CARDE) Framework
− Innovative Technology and System Development Analysis and Planning Framework
− “Multiscale Knowledge Integrator and Multiplier Computing, Information and
Communication Infrastructural” Environment
C. “Multiscale Science – Based Education, Information and Communication Framework” described in
the homonymous White Book
Alessandro Formica – March 2012 All rights reserved
10
2.3 Strategic Multiscale Framework Goals
As quoted in the Introduction, the fundamental goal of the Strategic Multiscale is to change in a qualitative
way, organization, structure and strategies of the R&D and Engineering landscape, catalyze and foster a
spectrum of innovation trends:
Innovation for the Computing, Information and Communication (CIC) Fields
� Fostering the design, development and application of a new generation of CIC HW Systems based
upon Multiscale Nano To Macro Engineering Architectures and Technologies
� Fostering the design, development and application of a new generation of Integrated SW Frameworks which realize:
− a comprehensive Multiscale Science – Engineering Integration [already on the way]
− a comprehensive Methodological Integration (multiscale computation, experimentation, testing and
sensing) [to a large extent to be still comprehensively developed]
and that incorporate new Multiscale Science – Engineering Data, Information and Knowledge Analysis,
Integration and Management Schemes
� Catalyzing the development of new SW Frameworks for the “Modeling” of complex Integrated R&D
and Engineering Processes (Designing the R&D and Engineering Processes)
� Catalyzing the development of new SW Frameworks for the Modeling of Complex Systems for the
full Life Cycle and the whole spectrum of Operational Conditions including the extreme and accident
ones
Innovation for Computing, Information and Communication (CIC) Infrastructures
� A New Generation of Computational Centers (Chapter 4) The Vision of “Modeling and Simulation as “Knowledge Integrators and Multipliers” and “Unifying
Paradigm” for the full spectrum of R&D and Engineering Methodologies (Experimentation, Testing and
Sensing) open the way to the creation of a New Generation of “Multiscale Multidisciplinary Science – Engineering Knowledge Integrator and Multiplier” Centers. In this new context Computational
Centers integrate themselves with Experimental and Testing Facilities and Field Monitoring Systems
operating over a full range of scientific and engineering scales.
� A new Generation of Cyberinfrastructures (Chapter 4) The new generation of Cyberinfrastructures which can be referred to as “Multiscale Multidisciplinary
Knowledge Integrator and Multiplier Cyberinfrastructural Environments” foresee a comprehensive on-
line integration of the full spectrum of Scientific and Engineering Theoretical, Computational,
Experimental, Testing and Sensing Teams and the related Facilities. The Unifying Conceptual Context is
offered by the new Modeling and Simulation Vision (Modeling and Simulation as Knowledge Integrators
and Multipliers) and the related Knowledge Management schemes.
Innovation For Technology and Engineering
� Promoting and Easing the development of New Fields for R&D and Engineering: Multiscale
Nano To Macro Experimentation, Testing and Sensing (Paragraph 3.5) Advances in computational modeling and technological fields have created the opportunity to extend
the multiscale approach from the computational world to the experimental, testing and sensing ones.
Activities are already started in Europe (Max Planck, and European Synchrotron Research Facility,
for instance), US and Japan. What is needed today are integrated large scale and scope initiatives.
This new field entails the development of new multiscale experimental and testing characterization
protocols, technologies and operational modes and new multiscale sensing network architecture and
operational methodologies. Methodologically Integrated Multiscale Science – Engineering Strategies
described in this document allow to take full advantage, in a synergistic way, of progress in both the
fields: Modeling & Simulation and Experimentation, Testing and Sensing, to define a real new way
to do Research, Technology Innovation and Engineering.
Alessandro Formica – March 2012 All rights reserved
11
� New Integrated Multiscale Nano To Macro Technological and Engineering Solutions (Multiscale Nano To Macro Technology and Engineering: From Multiscale Analysis to Multiscale Design)
New Methodologically Integrated Analysis and Design Strategies and New Integrated Data, Information
and Knowledge Analysis and Management Frameworks put the bases to design “inherently”
Hierarchical Multiscale Nano To Macro Systems (atoms, materials, structures components and
products) which is a fundamental condition to fully exploit in the industrial environment the
potentialities of Nano and Micro Technologies. “Multiscale Systems”, are. systems organized
following a Hierarchical strategy where structures at the different scales interact in a synergistic way
to determine an extended spectrum of functionalities and performance.
The EU NMP (Sixth Framework) Integrated Multiscale Process Units Locally Structured Elements
(IMPULSE 2005 – 2009) Program is a very interesting example of this trend. IMPULSE is Europe’s
flagship R&D initiative for radical innovation in chemical production technologies. In the Materials
Field, the Hierarchic Engineering of Industrial Materials (HERO-M) Center has been recently set up at
the Royal Institute of Technology, Stockholm
Innovation For Research, Technology Development and Engineering Process Organization, Structure and Strategies � Knowledge Transfer along the R&D and Engineering Technology Readiness Levels Scale
Strategic Multiscale Strategies and Frameworks allow to Organize and Structure Scientific Knowledge is
such a way to make it directly applicable to Analyze, Design and Manufacturing Innovative Technology
and Industrial Systems. New Data, Information and Knowledge Management Systems (Paragraph 3.2),
based upon the multiscale science-engineering integration concept, can contribute to redefine knowledge
transfer along the whole chain: basic research, applied research, technology development and
integration, engineering, manufacturing, operational testing. That leads to accelerate the pace of the
insertion of research achievements inside technology development and engineering design and improve
effectiveness and efficiency of the whole process. Multiscale means “Multiscale Multiphysics”.
Multiscale is intrinsically Multidisciplinary and Interdisciplinary as to become a very powerful integrator
of knowledge.
� Development of Methodologically Integrated Multiscale R&D and Engineering Strategies and Frameworks
The concept of Modeling & Simulation as “Knowledge Integrators and Multipliers”, the “Multiscale
Data, Information and Knowledge Analysis and Management System and the development of Multiscale
Experimentation and Testing Strategies open the way to the definition of “Methodologically Integrated
Multiscale Strategies and Frameworks” (Paragraph 3.6) to implement a full integration of different
Multiscale Multilevel Computational, Experimental, Testing and Sensing methods and techniques not
only in the computational models development and validation phases, but, also, in the application one
� Development of Methodologies and Frameworks to “Design” The R&D and Engineering Process Growing complexity of the R&D and Engineering Processes calls for the definition of more formal
methods to structure and organize this kind of Processes. New Strategies are described in the Paragraph.
3.7
Alessandro Formica – March 2012 All rights reserved
12
3. Integrated Multiscale Science - Engineering Framework
3.1 Architecture
Main elements of the Conceptual and Methodological Framework are:
� Multiscale Science - Engineering Data, Information and Knowledge Analysis and Management System
� Multiscale Science – Engineering Information Space
� Modeling & Simulation as “Knowledge Integrators and Multipliers” and Unifying Paradigm for
Scientific and Engineering Methodologies and Knowledge Domains
The role of Multiscale as “Unifying Paradigm and Language” for Science and Engineering was discussed
by Alessandro Formica, some years ago in the book - Computational Stochastic Mechanics In a Meta-
Computing Perspective – December 1997 - Edited by J. Marczyk – pag. 29 – Article: A Science Based
Multiscale Approach to Engineering Stochastic Simulations.
� Information – Driven Multiscale Science – Engineering Analysis Concept and Schemes
� Methodologically Integrated Multiscale Science – Engineering Methodologies
� New Methods, Tools and Strategies to Design the R&D and Engineering Process
� Integrated Multiscale R&D and Engineering Analysis Strategies
Alessandro Formica – March 2012 All rights reserved
13
3.2 Multiscale Science – Engineering Data, Information and Knowledge Management System
A critical issue for a wide diffusion of the science – based engineering analysis and design approach in the
industrial field is the availability of Software Environments (CAD/CAE/CAM) specifically conceived for
multiscale science – engineering strategies and applications. Today, notwithstanding the growing diffusion
of multiscale inside university, research, and even industry, software environments (CAD/CAE/CAM)
specifically conceived to implement multiscale science-engineering integration visions and strategies are in
their starting phase. The lack of software environments specifically conceived to implement a multiscale
science-engineering integration strategy represents a “fundamental” hurdle to a large scale implementation
of multiscale inside innovative technology development and engineering/manufacturing/processing fields.
The new Data, Information and Knowledge Management System proposed in this White Book rests on the
concept of “Multiscale Multiresolution Multi Abstraction Level Map. The Multiscale Multiresolution
Multi Abstraction Levels Map concept here described is an extension of the “Map” concept discussed by
Formica in the Multiscale Science – Engineering Integration: A new Frontier for Aeronautics, Space and
Defense White Book published on March 2003 by Italian Association of Aeronautics and Astronautics,.
Definition: Multiscale Multiresolution Multi Abstraction Level Maps are “ Multiscale Multiresolution
Multi Level Information and Knowledge Structures” describing complex networks of relationships and
interdependencies between a large spectrum of “Information Variables” characterizing “Systems Structure
and Dynamics”. Relationships and interdependencies between “Information Variables” are worked out
applying several mathematical techniques such as multivariate analyses and neural networks to raw data
coming from a wide range of “Data Sources” (analytical and computational models, data bases,
experimentation, testing and sensing). covering the full spectrum of scales (from atomistic to macro) and the
full spectrum of disciplines. “Multiscale Maps” structure Data and Information and, accordingly, they
represent a step to turn Information into Knowledge. Representations can be static and dynamic. Multi
Abstraction means that Maps can be set up and integrated applying several aggregation and clustering
schemes. A cluster of Multiscale Maps aggregated following a specific aggregation scheme can define what
can be called a “Knowledge Domain”. “Knowledge Domains” can be organized in a “Hierarchical Way”.
Maps are organized in a hierarchical way. For instance: a “Physical Knowledge Domain” linked to a
specific Process (Hypervelocity Impact, Combustion or Explosion, for instance) can be constructed by
assembling a range of Multiscale Physical Maps describing more elementary physical (chemical and
biochemical) phenomena (fracture, fragmentation, phase change,..) related to a specific material or
component of a System.
Multiscale Maps are built integrating/fusing (statistical methods, neural networks,…) data from a wide
range of sources:
− a spectrum of scientific and engineering teams,
− a wide range of methodologies,
− a spectrum of analysis and design tasks in the different stages of the whole Technology Development
and Engineering process.
Multiscale Maps incorporate error analyses and uncertainty quantification methods.
“Multiscale Maps” allow for an effective insertion and management of the more fundamental knowledge
(basic and applied research) inside Technology Development and Engineering phases. At each phase,
Multiscale Maps are built taking full advantage of the knowledge get in the previous phase.
Alessandro Formica – March 2012 All rights reserved
14
Several typologies of Maps are foreseen which describe relationships between:
− Multiscale Analysis and Design Variable Maps tracking relationships between Analysis and Design
Variables . Multiscale Analysis and Design Variable Maps are built applying statistical analysis schemes
(multivariate, PCA) or other techniques like neural networks to data coming from several sources: data
bases, computation, analytical theories, experimentation, testing, sensing. Data integration and fusion
techniques are applied to reconcile and integrate data coming from different sources characterized by a
range of accuracy and reliability degrees. Multiscale Analysis and Design Variable Maps describe
relationships between variables and parameters used to characterize “Systems Behaviour” over a full
range of space and time scales.
− Multiscale Physics Maps describing relationships between Physical, Chemical and Biochemical
Phenomena and Processes
− Multiscale Architectural/Structural Maps describing relationships between the hierarchy of Sub-
Systems, Components, Devices, Materials and Elementary Structures constituting a System (or System
of Systems) of arbitrary level of complexity.
− Multiscale Monitoring and Control Maps describing the networks of Monitoring anc Control devices
and Systems
− Multiscale Functional Maps describing relationships between System Architectural/Structural Elements
and Functions performed
− Multiscale Requirements - Performance – Property – Structure Maps describing relationships between
Requirements, Performance, Structural Elements and related Properties over the whole scales and
resolution levels. .
− Multiscale Performance – Property – Structure - Processing Maps describing the impact of
Processing techniques over the network of Performance, Structure - Property relationships over the
whole scales and resolution levels.
Multiscale Maps represents a key element of a new Multiscale Computer Aided Research, Development
and Engineering (CARDE) Systems.
Main objectives:
� Developing new schemes allowing for a more in-depth analysis and structuring of data, information
and knowledge and related correlations and interdependencies
� Integrating the full spectrum of “Data Sources” (Data Bases, Analytical Theories, Computational
Models, Experimentation , Testing and Sensing). The “Information Space” and the “Modeling and
Simulation as Knowledge Integrators and Multipliers” concepts and methods ease this kind of
Integration
� Developing new CAD/CAE Environments specifically conceived to Design new Hierarchical
Multiscale Nano To Macro Multifunctional Systems in the context of an Integrated Science –
Engineering Approach
� Developing new Integrated Science – Engineering CAD/CAE Environments able to be applied
inside the whole R&D and Engineering Process.
Multiscale Maps are indexed and related to specific R&D and Engineering Tasks and Phases and Design
Hypotheses and Decisions
The Multiscale Science – Engineering Data, Information and Knowledge Management System records,
organizes and manages all the previously defined Maps. Each Map is characterized by a set of Tags which
link it to a specific task and phase inside the R&D and Engineering Analysis and Design Process.
Alessandro Formica – March 2012 All rights reserved
15
Fig. 2 Physics Map Example (from “Overview of the Fusion Materials Sciences Program Presented by S.J.
Zinkle, Oak Ridge National Lab Fusion Energy Sciences Advisory Committee Meeting February 27, 2001
Gaithersburg”)
This figure depicts a “ Information Structure” like the proposed Multiscale Physics Maps. In this case the
Multiscale Physics Map describes relationships between physical phenomena and chemical/physical
structural transformations linked to Radiation Damage Process for Metals
A cluster of Multiscale Physics Maps, linked to specific physics phenomena or processes, can define what
can be called a “Physical (Chemical and Biochemical) Phenomena and Processes Knowledge Domain”.
“Knowledge Domains” aggregated following horizontal and vertical ways.
Knowledge Domains are managed by the Multiscale Science – Engineering Data, Information and
Knowledge Management System.
Alessandro Formica – March 2012 All rights reserved
16
Multiscale Multiresolution Multilevel Architectural and Structural Maps
Any “System” of arbitrary degree of complexity (an air transportation system, an energy production system,
an aerospace vehicle, a chemical plant, a structure, a nanotechnology device, a nanostructured material), can
be recursively broken down in a set of simpler (macro, meso, micro, nano and atomistic) “Architectural and
Structural Elements”. We distinguish two kinds of interconnected Systems: Technological Systems and
Natural Systems where the Technological System (or System of Systems) operates.
Fig. 3 Two dimensional multilevel multiscale view of an aircraft. (from the “Validation Pyramid and the
failure of the A-380 wing” Presentation given by I. Babuska (ICES, The University of Texas at Austin), F.
Nobile (MOX, Politecnico di Milano, Italy), R. Tempone (SCS and Dep. of Mathematics’, Florida State
University, Tallahassee) in the context of the Workshop “Mathematical Methods for V&V SANDIA ,
Albuquerque, August 14-16, 2007
Three new features distinguish this kind of Maps and related Multiscale Multilevel Science – Engineering
CAD Systems:
− Multiscale Multilevel Architectural/Structural Element Networks Analysis and Description. New CAD
Systems should describe the full set of multiscale multilevel (inside a single scale) Architectural and
Structural Elements of a System (or System of Systems) - including the “Operational Environment” -
and related interconnections. Interconnection Elements describe two – way interactions between
Elements. This feature is of particular importance if we like to assess the impact of the System upon the
environment where it operates and the effects of the Environment on the System for the whole Life Cycle
and the whole spectrum of operational conditions including extreme ones and accidents.
− Zooming and Selected Multilevel Multiscale view capabilities. Users should have the possibility to select
a full spectrum of views at different levels of resolution, scales and abstraction. Multiple views should be
visualized in order not to lose connections among different levels of abstraction, resolution and scales.
The zooming function should allow users to transition from a levels of abstraction, levels and scales in an
interactive way.
− Multi Abstraction Levels: we can select groups (clusters) of architectural/structural elements of different
typologies over a spectrum of scales and resolution levels as needed to carry out specific analyses and
design tasks.
Alessandro Formica – March 2012 All rights reserved
17
This kind of “Maps” gives a comprehensive picture of the:
– “Architectural and Structural Elements” which constitute a “system” and related interconnections: from
the System (or System of Systems) down to elementary structures (atoms/molecules, groups of atoms
and molecules)
– Materials, Energy, Chemical and Biochemical Substances Flow (pollutants emitted toward the Natural
System for instance,) among the “Elements” constituting the System or System of Systems
– Analysis and Design Variables their relationships and interdependencies and links between “ Analysis
and Design Variables” and Architectural and Structural Elements
– Properties of the full set of Architectural and Structural Elements
– Performance and Requirements for the full set of Architectural and Structural Elements. Performance are
calculated and/or measured during the R&D and Engineering Process while, Requirements are imposed
by designers.
Architectural and Structural Maps evolve along the Technology Development and Engineering Analysis and
Design Process thanks to Analysis and Design Modules and “Strategy Modules”. “Maps” are built using the
available knowledge; as analysis and design activities proceed, they are interactively modified. Different
Maps can be linked to different Architectural Hypotheses and Decisions for different purposes and tasks
during the R&D and Engineering Process. Maps are recorded, organized and managed in specific
“Architectural and Structural Map Data Bases”. Architectural and Structural Elements Maps are related
to:
− Functional Maps
− Monitoring and Control Maps
− Physics and Process Maps
Multiscale Monitoring and Control Maps
This kind of Maps gives a comprehensive picture of the Multiscale Multiresolution Networks of Monitoring
and Control Devices and Systems their interconnection schemes and their functionalities and operational
modes. Multiscale Monitoring and Control Maps are related to:
− Architectural and Structural Maps
− Physics Maps (effects of Control actions)
Multiscale Functional Maps
We define two types of Functional Maps.
− The first one, which can be called “Direct Functional Map”, describes “Functions” carried out by the
System and the full hierarchy of its Elements. Direct Functional Maps link Architectural/Structural
Elements to Functions and they describe what functions are performed by Architectural/Structural
Elements.
− The second one, which can be called “Inverse Functional Map” relates Functions to
Architectural/Structural Elements over the full spectrum of hierarchy levels
Functional Maps are linked to:
− Architectural and Structural Maps
− Physics and Processes Maps
“Functional Maps” defined during the Technology Development and Engineering Process are recorded,
organized and managed by specific “Functional Maps Data Bases”. Maps are indexed in such a way as to
relate them to specific R&D and Engineering Phases and Tasks.
Alessandro Formica – March 2012 All rights reserved
18
Multiscale Physics Maps
We use the term “Physics” to indicate a more or less complex cluster of elementary physical and biochemical
phenomena/processes occurring inside a scale or developing over a spectrum of scales,
Phenomena/processes are, for instance, failure, stress corrosion cracking erosion, phase
transformation,…… A Process can be broken down in a full hierarchy of more elementary Processes and
Phenomena. The distinction between “processes” and “phenomena” is, to some extent, arbitrary. It is a
matter of opportunity. Phenomena and Processes can concern more Architectural/Structural Elements.
Physics Maps are linked to:
− Architectural/ Structural and Functional Maps.
− Monitoring and Control Maps
− Requirements - Performance – Property – Structure Maps
− Performance – Property – Structure - Processing Maps
“Physics Maps” are “software environments” which describe :
� the full set of physical (biological and chemical, as needed) phenomena and processes which rule the
dynamics of Architectural/Structural Elements of a “System” under analysis/design for a specific
Task and their interactions inside a scale and over different scales.
� The full hierarchy of (geometrical, physical and bio- chemical) Architectural/Structural
transformations related to a specific set of Phenomena/Processes linked to a specific R&D and
Engineering Task .
� Relationships between the full hierarchy of processes, phenomena and Architectural/Structural
transformations for a specific Task
Maps are indexed in such a way as to relate them to specific R&D and Engineering Phases and Tasks.
Physics Maps are linked to Integration Strategy Maps described in the Paragraph 3.6. Integration Strategy
Maps describe what Computational Models, Experimentation, Testing and Sensing Techniques/Procedures
are applied to analyze specific physical phenomena/processes and their interconnection networks and
sequence of execution. Physics Maps are built using the available knowledge, as R&D and Engineering
proceed, they are interactively modified.
“Physics Maps” defined during the R&D and Engineering Process are recorded, organized and managed by
specific “Physics Maps Data Base”.
Integration of the previously defined Multiscale Maps allow to correlate:
− functions to physical phenomena and processes (linking Multiscale Functional Maps with Multiscale
Physics Maps
− Properties (Multiscale Architectural/Structural Maps) to Physics (Multiscale Physics Maps)
Alessandro Formica – March 2012 All rights reserved
19
Performance – Properties – Structure – Processing Maps
The definition of the Performance – Properties – Structure – Processing relationships has become a
cornerstone of the modern Materials Science and Engineering and R&D and Engineering at all.
Prof. Gregory Olson, Northwestern University has been one of the pioneers of this strategy. Prof. Olson
described this approach in a Science Magazine article: Vol. 277 (29 August 1997) pp. 1237-1242.
Fig. 4 (from Prof. Olson – Northwestern University) illustrates the application of a Performance – Properties
- Structure – Processing Map to the design of new alloys.
Performance – Properties –Structure - Processing Maps are indexed in such a way as to relate them to
specific R&D and Engineering, Phases and Tasks. Performance – Properties - Processing – Structure Maps
defined during the R&D and Engineering Process for different purposes and tasks are organized and
recorded in the “Performance – Properties - Structure - Processing Map Data Bases” The Multi
Abstraction Level feature of the Maps can be seen in the figure: each box is a specific abstraction level. Each
Box refer to a cluster of processes occurring over u spectrum of scales and resolution levels.
This kind of software environments contribute to characterize and manage relationships between processing
and manufacturing activities and the resulting architecture/structures
These Maps identify :
� defects (typology, physical and chemical characteristics, density and distribution : statistical and
deterministic analysis) linked to specific processes and manufacturing activities and steps
� bio – chemical and structural features and transformations linked to specific processes and manufacturing
conditions, procedures and technologies
This kind of Maps are related to Multiscale Physics Maps
Alessandro Formica – March 2012 All rights reserved
20
3.3 Multiscale Science - Engineering Information Space
This concept was presented by Alessandro Formica in the Report “Fundamental R&D Trends in Academia
and Research Centres and Their Integration into Industrial Engineering” (September 2000), drafted for
European Space Agency (ESA). The “Multiscale Science-Engineering Information Space” is associated to
any analytical, computational model/method, and experimental, testing and sensing procedure and technique
applied to a specific task. The “Multiscale Science-Engineering Information Space” defines:
− what spectrum of information about physical/biological/chemical phenomena and processes
− at what level of accuracy and reliability
can be get by a computational model or experimental/testing/sensing technique/procedure applied in a
specific context for a specific task.
A set of “model variables” characterize analytical and computational models. A set of “method variables”
characterize the specific method applied to perform simulations. A set of “system variables” characterizes
the system to be modeled and simulated or subjected to experimental, testing and sensing analyses. A set of
“experimental, testing and sensing variables” characterizes experimental, testing and sensing techniques and
procedures.
The ”Science – Engineering Information Space” also applies to cluster of computational models and
experimental/testing/sensing techniques/procedures linked through multiscale multiphysics coupling
schemes. In this case we can define “coupling scheme parameters” which describe the method used to
couple models and/or experimental/testing/sensing techniques/procedures.
− With the term “system” we refer to the system (materials, device, component,….) under analysis.. A set
of “variables” describe the geometrical, biological, chemical and physical structure of the system.
− With the term “Operational Environment”, we refer to “External Fields and Loading Conditions”
− With the term “model” we refer to the mathematical/computational representation of the “system” under
investigation. A set of “variables” characterize and describe the models (boundary conditions, external
fields, space and time dimensions, discretization techniques, particles number and typology,…….). In
the proposed framework we extend the concept of “Model” to the Experimental/Testing/Sensing world
as explained in the Paragraph 3.4
− With the term “method” we refer to the specific deterministic and statistical analytical and computational
method (Monte Carlo. Classical Molecular Dynamics, Quantum Molecular Dynamics, Density
Functional Theory, Dislocations Dynamics, Cellular Automata,…).
− With the term “experimental/testing/sensing technique and procedure variables” we refer to the
“variables” which describe technical characteristics of the experimental and testing apparatus and the
specific operational modes and conditions (globally referred to as “procedure”)
Information Space Construction To build the “Information Space” of a specific (single scale or multiscale) computational model with
reference to a specific system and analysis task (fracture, delamination, oxidation,…), we perform a set of
simulations, varying in a systematic way parameters/variables which characterize the physical (chemical and
biochemical) phenomena/processes of interest in the context of a specific task including external forces.
Then, we validate computational models using a set of experiments, tests and sensing measures to track the
“boundaries” of the Information Space and evaluate accuracy and reliability (Uncertainty Quantification –
UQ). Information Spaces can be built also for experimental, testing and sensing techniques and procedures.
In this case a “Cross Validation” strategy is applied which foresee the comparison of a spectrum of
experimentation, testing and sensing techniques.
Alessandro Formica – March 2012 All rights reserved
21
The “Information Space”, should also include Multiscale Analysis and Design Variable and Multiscale
Physics Maps worked out during the previously described construction process.
It is possible to apply different schemes to build the “Information Space” for a specific task. For instance:
− fixing model and methodology variables and varying external conditions and/or system variables
(typology and architecture of a material or device)
− fixing external conditions and system variables and varying model and/or methodology variables (for a
molecular dynamics model: simulation time, force fields typology, number of particles,…).
− any other possible combinations
The Information Space, for each specific computational model/method (or cluster of models: multiscale
multiphysics) applied to a specific task includes information about the computing resources needed to
perform simulations and the experimental, testing and sensing techniques used to validate it
Information Space Relevance
Three considerations underlie the definition of the “Multiscale Science – Engineering Information Space”
concept and method:
� rationally correlating advances for models/methods and multiscale multiphysics coupling schemes with
the capability of getting information thought to be important to carry out specific R&D and Engineering
tasks.
� rationally defining the role of models/methods and related multiscale multiphysics coupling schemes
inside a more general R&D and Engineering analysis and design process and the interdependencies
among different models, methods, techniques and coupling schemes.
� formally tracking and planning the development path (roadmap) for models, methods, techniques and
related coupling schemes as linked to specific R&D and Engineering analysis and design tasks, and
assessing the relative importance of the different models/methods and related coupling schemes to get
some Information at a specific level of accuracy and reliability.
We can consider an aerodynamic design task, for instance. The ability to run a 30/50-million grid points
Navier Stokes simulation in the same lapse of time, or less, as a 1-million grid points simulation, is surely an
important result from an engineering analysis and design point of view. But, what is the relative “weight”
between model dimension and physics (turbulence) modeling as function of a particular task (calculation of
aerodynamic coefficients, for instance) at a certain level of accuracy and reliability?
In this way, can we get more reliable and accurate information instrumental to reducing cost and
development time and introduce innovative technological solutions? The answer is not so straightforward.
Turbulence plays a key role in flow dynamics phenomena of critical importance for the design of a wide
range of systems. Suppose the biggest simulation model used the same turbulence model (or a slight
modification) as the one employed in the smallest one, what is the relationship among the number of grid
points, turbulence modeling (model variables) and the capacity of getting the needed engineering
information at the right level of accuracy (for instance : CP - CL or vortex dynamics – look at the V-22 vortex
ring state story) ? Is the number of grid points or the turbulence modeling the dominant knowledge factor
from a designer point of view?
The situation becomes even more critical when the physics and chemistry to be taken into account are highly
complex (aerothermodynamics and combustion, for example). It is sufficient to think at a combustion
chamber or an hypersonic vehicle. Several variables such as complex thermo chemical phenomena, the
interaction between turbulence and chemistry, multiphase and phase change phenomena, condition the
information space linked to a model.
We introduce, now, the “Range of Validity” concept for the “Multiscale Science-Engineering Information
Spaces” associated to models/methods and experimental, testing and sensing techniques and procedures.
Alessandro Formica – March 2012 All rights reserved
22
“Range of Validity” is the range of the “Multiscale Science-Engineering Information Space” inside which
we can get a set of information from specific models/methods and experimental, testing and sensing
procedures/techniques and possible coupling schemes at a certain level of accuracy and reliability
(uncertainty quantification).
It is of fundamental relevance to determine how the “Range of Validity” changes as model, method,
experimental & testing and coupling scheme variables change. The “range of validity” is a key element to
determine (for a specific task) :
� how “good” computational models and experimental, testing and sensing techniques and coupling
schemes should be to get Information we think to be needed to carry out a task at a predefined error and
uncertainty level.
� how to define the right mix of computational models/methods and experimental & testing
procedures/techniques and coupling schemes to get what we think to be the right information at the right
level of accuracy and uncertainty to perform a specific R&D and Engineering analysis and design task..
Fig. 5 (Center for Computational Materials Design – NSF) describes a framework to define in a formal way
the “Range of Validity (or Applicability Domain)” of a model
The “Multiscale Science-Engineering Information Space” formalizes what, today, is being performed in an
empirical and semi-empirical way. Such a formal procedure allows us to rigorously evaluate the relative
weight of the several “model/method/technique variables” as function of the “Information Space” and the
best research/development paths for computational models/methods and experimental & testing techniques
to address specific challenges.
The “Multiscale Science-Engineering Information Space” concept and method enables researchers and
designers to jointly define development roadmaps for computational models and experimental, testing
and sensing techniques.
Alessandro Formica – March 2012 All rights reserved
23
The need of defining the “Information Space” associated to computational method and experimental
techniques, in the context of the Verification & Validation process, has been analyzed, for instance, by Tim
Trucano in “Uncertainty in Verification and Validation: Recent Perspective Optimization and Uncertainty
Estimation, Sandia National Laboratories Albuquerque, NM 87185-0370 SIAM Conference on
Computational Science and Engineering, February 12-15, 2005, Orlando, Florida - SAND2005-0945C”.
Fig. 6 The figure (from the previously quoted document) illustrates the “Information Space concept
Thanks to the “Multiscale Science – Engineering Information Space” concept and method, it is possible to
define “Costs/Benefits Function” for models/methods and related coupling schemes as referred to different
Technology Development and Engineering tasks. “Benefits” are referred to the Information get and “Costs”
to the resources needed to develop, validate and apply models/methods/techniques/coupling schemes. This
kind of Function could be useful to Technology Development and Engineering Project Managers to better
manage and allocate human, organizational and financial resources.
The “Multiscale Science – Engineering Information Space” and the “Range of Validity” concepts can play a
role to develop new Verification and Validation (V&V) strategies and methods. Uncertainty Quantification
(UQ) is a key challenge for Computational Science and Engineering. We would like to underline this
Challenge concerns not only “Science”, but, also Engineering, taking into account the ever more strong
relationships between the two fields: Computational Engineering is increasingly built upon Computational
Science. Multiscale is a clear demonstration and application of this trend. UQ and “Quantification of Margin
of Uncertainty (QMU)” [performance (measured) vs. requirements (set)] , are becoming (have already
become) one of the new driver and objective for the Computational World. The Predictive Science
Academic Alliance Program (PSAAP) managed by US National Nuclear Security Agency (NNSA) is a clear
example of application of these statements.
The “Multiscale Science-Engineering Information Space” is of fundamental importance to define and
implement “Methodologically Integrated Multiscale Science-Engineering Strategies” which foresee the
simultaneous use of several different single and multiscale computational models and methods, and several
different single and multiscale experimental techniques working over a full range of scales.
The “Multiscale Science – Engineering Information Space is becoming of increasingly importance for
Science and Engineering because for a specific tasks is common using a spectrum of computational models
and a spectrum of experimental techniques and methods. Integration calls for rigorous methodologies to
determine what kind of Information can be get from computations and what from experimentation, testing
and sensing.
Alessandro Formica – March 2012 All rights reserved
24
According to the previous analysis, the “Multiscale Science-Engineering Information Space” concept and
method is instrumental to identify:
� shortcomings and limitations of computational models/methods and related multiscale multiphysics
coupling schemes for specific R&D and Engineering tasks
� development lines (roadmaps) for computational models and methods and multiscale coupling schemes
to achieve specific R&D and Engineering objectives
� shortcomings and limitations and development lines (roadmaps) for experimental, testing and sensing
techniques and procedures and related multiscale multiphysics coupling schemes
� integrated roadmaps for jointly developing multiscale multiphysics analytical, computational and
(multiscale) experimental, testing and sensing techniques to deal with specific R&D and Engineering
Tasks
� integrated strategies for jointly applying multiphysics multiscale analytical, computational and
(multiscale) experimental, testing and sensing techniques/procedures to deal with specific R&D and
Engineering Tasks
Alessandro Formica – March 2012 All rights reserved
25
3.4 Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains
The “Vision” of “Multiscale Modeling & Simulation” as “Knowledge Integrators and Multipliers” (KIM)
and “Unifying Paradigm” for Scientific and Engineering Knowledge Domains and (Experimentation,
Testing and Sensing) Methodologies characterizes the “Integrated Multiscale Science-Engineering
Framework” and it represents the conceptual context inside which the Framework is applied to R&D and
Engineering Processes. The KIM notion was presented by Alessandro Formica in the: “HPC and the
Progress of Technology : Hopes, Hype, and Reality” – RCI. Ltd Management White Paper – February 1995
“Multiscale Multiphysics Modeling and Simulation” can be regarded as “Knowledge Integrators and
Multipliers” (KIM) and “Unifying Paradigm” for Scientific and Engineering Knowledge Domains and
Methodologies because Multiscale Models are able to integrate and synthesize, in a coherent framework,
Data, information, and Knowledge from:
���� a number of disciplines,
���� a wide range of scientific and engineering time and space domains,
���� multiple scientific and engineering models (science-engineering integration) linked by a spectrum of
coupling schemes.
���� A wide spectrum of Computational, Experimentation, Testing and Sensing Multiscale Science –
Engineering Data and Information Spaces built during the development, validation, application and I
improvement phases of the same Multiscale Models
���� by several Maps generated by a wide range of methodologies (analytical theories, computation,
experimentation, testing and sensing) during the development, validation, application and improvement
phases of the same Multiscale Models
In this vision, we propose to extend the concept of “Model” to include not only its mathematical
formulation, but, also, Information Spaces and Maps linked to it for specific tasks.
Multiscale Information Spaces and Multiscale Maps embody and organize Data, Information and
Knowledge get by the full spectrum of analytical theories, a set models at different scales and the related
experiments, tests and sensing measures used to develop, validate and improve them.
It is to be highlighted that all the existing Modeling and Simulation concepts, application strategies and
methodologies, such as “Virtual Prototyping” , “Simulation - Based Design”, “Simulation - Based
Acquisition”, Simulation Based Engineering Science (SBES) and “Virtual Engineering”, can be considered
as particular cases of this more general concept and strategy.
We would like to highlight that the “KIM” concept puts Modeling and Simulation and, accordingly, HPC,
at the centre of the R&D and Engineering Process much more than the classical “Virtual Prototyping”
and “Simulation Based Engineering Science” concepts
The concept of “Model” as “Knowledge Integrator” is certainly not new. This view, in the mid of nineties,
was clearly described in the chemical engineering field by James H. Krieger, in the article “Process
Simulation Seen As Pivotal In Corporate Information Flow” - Chemical & Engineering News, March 27,
1995. The text reported the following statement of Irving G. Snyder Jr., director of process technology
development, Dow Chemical : "The model integrates the organization. It is the vehicle that conveys
knowledge from research all the way up to the business team, and it becomes a tool for the business to
explore different opportunities and to convey the resulting needs to manufacturing, engineering, and
research." . In the same article other companies such as BNFL and Du Pont expressed similar points of view.
Note: Continuous advances in computational modeling and computing power makes it possible to build
computational models which simulate the experimental or testing apparatus, the system to be probed and
related interactions. This kind of modeling is an interesting asset to plan experimentation, testing and
sensing and analyze results.
Alessandro Formica – March 2012 All rights reserved
26
Key element of the KIM Vision is the extension of the concept of “Model” to the Experimental, Testing and
Sensing World as detailed in the following:
Even if attention to the integration issue is positively increasing, particularly for models development and
verification and validation phases, there are still conceptual and methodological relationships not thoroughly
examined between challenges and advances in modeling and simulation, and progress and challenges in
experimental, testing and sensing techniques. Experience is showing us that ever more complex and large
scale computations call for increasingly sophisticated and expensive experimental techniques both in the
model development, validation and improvement phases. Advances in modeling and simulation are
intimately linked to progress in experimental, testing and sensing methods and techniques and vice versa. A
direct correlation and strong mutual dependencies, in the model development, validation and improvement
phases, exist between the two fields sometimes regarded as antithetic. It is important to take into account
that, if computational methods and computing technologies are continuously progressing, also experimental,
testing and sensing techniques are making continuous significant progress.
It is sufficient to think at the impact on materials research that the Scanning Tunneling Microscopy (STM)
and Atomic Force Microcopy (AFM) techniques have had.
It is important to highlight that Computational Development Strategies should be jointly conceived with
Experimentation, Testing and Sensing Development Strategies and vice versa.
Furthermore more and more complex and powerful 3D and 4D experimental, testing and sensing techniques
increasingly call for complex computational models to interpret, analyse and organize data and define
integrated measurement and characterization strategies.
The Concept of Experimental, Testing and Sensing “Model”
In the proposed theoretical and methodological framework it is necessary to extend the concept of
“Model” from the Computational to the Experimental, Testing and Sensing World. In the context of the
Experimental, Testing and Sensing World, for “Model”, as referred to a specific Experimental, Testing,
Sensing activity carried out with specific techniques, working in a specific operational mode and
probing a specific system for a specific task, we mean an “Information and Knowledge Structure” that
define:
− Characteristics (structure, composition, initial dynamics state, boundary conditions, external loadings)
of the System to be probed
− Characteristics of the equipment in terms of resolution, scale, physical and biochemical phenomena
which can be probed
− Characteristics of the specific Experimental, Testing and Sensing operational conditions and modes
applied for specific R&D and Engineering Tasks
− The “Multiscale Science – Engineering Information Space” related to it
− Multiscale Physics Maps .
As in the Computational World, it is easy to define the concept of “Multiscale Experimental, Testing and
Sensing Model”. In this case the “Information/Knowledge” Structure refers to a cluster of different
equipments and it embodies information about:
− Interaction schemes among the different equipments
− Data and Information Flow among the different equipments
Alessandro Formica – March 2012 All rights reserved
27
A priority target is to develop a unified conceptual context to synergistically take advantage of advances in both the fields and not only for the computational models development and validation phases, as it occurs today, but, also, in the application phase. All of that in the context of Integrated Frameworks and Strategies
An effective R&D and Engineering Strategy should find the way to synergistically take advantage of
advances in both the fields.
In several cases, today, advanced HPC/Modeling/Simulation and experimental/testing/sensing programs are
conceived and managed as separated realities. This situation can lead to costs increase and hamper and
limit the effectiveness of both the programs. The new Vision reconcile development streams and roadmaps in
the two fields.
In the R&D and Engineering Process, today and, more and more, in the future, we have to integrate a full
spectrum of (interdependent and interlinked) scientific and engineering models and codes with a wide
spectrum of experimental, testing and sensing (scientific and engineering) data with a full spectrum of
scientific and engineering analytical formulations. Data get from experimentation, testing and sensing covers
several physical and biochemical disciplines and domains and several different space and time scales. It is
clear that, increasingly, we have to deal with very complex interaction patterns “intra” the experimentation,
testing and sensing world, “intra” the computational modeling world and “inter” the experimentation,
testing, sensing and computational modeling worlds. “Multiscale Science – Engineering Information Spaces,
Multiscale Maps and the Kim vision can be a first step to realize this integration.
The KIM concept is a fundamental theoretical and methodological basis. Methodologically Integrated
Multiscale Science - Engineering Strategies are built upon it. The following example is a notable
demonstration of the possibility opened by Integrated Computational and Experimental Strategies
ORNL - neutrons and simulations reveal details of bioenergy barrier –
OAK RIDGE, Tenn., June 15, 2011 — A first of its kind combination of experiment and simulation at the Department of Energy's Oak
Ridge National Laboratory is providing a close-up look at the molecule that complicates next-
generation biofuels.
Lignin, a major component of plant cell walls, aggregates to form clumps, which cause problems
during the production of cellulosic ethanol. The exact shape and structure of the aggregates, however,
have remained largely unknown.
A team led by ORNL's Jeremy Smith revealed the surface structure of lignin aggregates down to 1
angstrom—the equivalent of a 10 billionth of a meter or smaller than the width of a carbon atom. The
team's findings were published in Physical Review E. "We've combined neutron scattering experiments
with large-scale simulations on ORNL's main supercomputer to reveal that pretreated softwood lignin
aggregates are characterized by a highly folded surface," said Smith, who directs ORNL's Center for
Molecular Biophysics and holds a Governor's Chair at University of Tennessee. Lignin clumps can
inhibit the conversion of biofuel feedstocks—for example, switchgrass—into ethanol, a renewable
substitute for gasoline. …..
The complementary techniques of simulation on ORNL's Jaguar supercomputer and neutron scattering
at the lab's High Flux Isotope Reactor enabled Smith's team to resolve lignin's structure at scales
ranging from 1 to 1,000 angstroms. Smith's project is the first to combine the two methods in biofuel
research. "This work illustrates how state-of-the-art neutron scattering and high-performance
supercomputing can be integrated to reveal structures of importance to the energy biosciences," Smith
said. The research was supported by DOE's Office of Science and used the resources of the Leadership
Computing Facility at ORNL under a DOE INCITE award. Team members include ORNL's Sai
Venkatesh Pingali, Volker Urban, William Heller, Hugh O'Neill and Marcus Foston and Arthur
Ragauskas from Georgia Institute of Technology.
Alessandro Formica – March 2012 All rights reserved
28
Classical Modeling & Simulation Application Strategies in the innovative technology development field are
significantly hampered and limited the following fundamental contradiction:
“when we develop innovative technologies and innovative engineering solutions, we often enter a territory
where theories are not well developed and reliable, and the availability of experimental and testing data is
fragmented or lacking at all. Accordingly, we face a fundamental and intrinsic problem: Modeling &
Simulation is the reference strategy to limit risks, costs, and development times by heavily reducing the
resort to complex and expensive experimental and testing activities. However, contrary to what happens in
the mature or evolutionary technology environment, we cannot adopt this strategy because we still need
very significant experimental and testing activities to develop and validate the needed computational
models.”
That is what is called a classical “Catch 22” situation: (i.e.) a situation which involves intrinsic
contradictions.”
This contradiction is certainly not ignored. In the presentation “Modeling and Simulation in the F-22
Program” held on 3 June 98, Bgen Michael Mushala, F-22 System Program Director, highlighted this issue.
We quote his exact words :
A Catch 22 :
>> Increased Reliance on Simulation Requires High Confidence in the Modeling
>> High Confidence in the Modeling Requires High Quality Flight Test Data
How to get out of this contradiction? We think that single scale and independent computational and
experimentation, testing and sensing science and engineering strategies are not up to the challenge, A partial
way forward can be the application of the new Vision of Modeling and Simulation and, in particular, of
some of its key constitutive elements:
� Multiscale Maps
� the “Multiscale Science – Engineering Information Space” concept. which enables the definition in a
formal way of what kind of information at what level of accuracy and reliability can be get by single
and multiscale computational, experimental, testing and sensing models and techniques.
� A new concept of computational model which include not only mathematical and physical (chemical
and biochemical, as needed) formulations, but, also, Data, Information and Knowledge (Multiscale
Maps) linked to it when applied to a specific task
� The extension of the “model” concept to the experimental, testing and sensing world
� Definition of the “Applicability Conditions” and “Predictability Criteria” for (single and multiscale)
Computational models which guide the application of Modeling and Simulation and their
integration with experimentation, testing and sensing (Methodologically Integrated Multiscale
Application Strategies) [Paragraph 3.6]
Alessandro Formica – March 2012 All rights reserved
29
3.5 Multiscale Multiresolution Multiphysics Experimentation, Testing and Sensing
− The term “Multiscale” means the ability to probe phenomena and processes occurring over a spectrum of
space and time scales
− The term “Multiresolution” refers to the analysis of phenomena and processes inside a single scale, but
with a range of different resolution degrees
− The term “Multiphysics” means the analysis of a spectrum of phenomena and processes referred to
different physical and biochemical domains, inside a specific scale and resolution degree or over a range
of scales.
The following issues are motivating the birth and they are driving the development of the Multiscale
Multiresolution, Multiphysics Experimental, Testing and Sensing fields:
� Just the continuous development of Computational Multiscale has put the basis and established the need
to extend, in a systematic way, the Multiscale Concept and Method to the experimental, testing and
sensing fields.
� The development and validation of ever more complex multiscale computational models and methods
increasingly call for the integration of data, information and knowledge from a wide range of
experimental, testing and sensing equipments working over an extended spectrum of space and time
scales and physical domains. We can state that a direct relationship between the Computational and
Experimental, Testing and Sensing Multiscale World exists. Advances in Multiscale Computational
Models and Methods is directly linked to advances in experimental, testing and sensing multiscale. The
development of Multiscale Multiresolution Experimentation, Testing and Sensing techniques open a
whole new Application World to Multiscale Modeling and Simulation.
� Hierarchical Multiscale Materials and Systems made up of a wide spectrum of sub-systems,
components, devices and basic structural elements call for Multiscale Integrated Experimental, Testing
and Sensing techniques and strategies to get an in-depth and Comprehensive understanding of their
dynamics.
� The behaviour of Materials, Devices and Systems inside widening operational envelopes and related
requirements for “Extreme Performance” levels (Extreme Engineering) is critically dependent upon a
full spectrum of multiscale physical and biochemical phenomena. Furthermore, the classical approach to
Life Cycle issues (damage, fracture, properties degradation, corrosion, failure..) is increasingly showing
specific limits. This situation makes a science – based (multiscale) experimental, testing and sensing
approach a specific target for Technology Development and Engineering.
� New and more powerful experimental, testing and sensing equipments are continuously developed.
Technology advances allow, today, to design experimental, testing and sensing equipments with inherent
capabilities to probe systems over an extended range of scales: Free Electron Laser and X Ray
Synchrotron, are two significant examples of this trend. Advances in wireless and wired sensor network
and the Integration of Distributed Processing and Sensing put the bases to the design of a new Generation
of Multiscale Sensor Networks. Technological Advances put the bases to design and implement
Multiscale Multidisciplinary Cyberinfrastructures which connect a wide spectrum of experimental,
testing and sensing systems working over a full spectrum of space and time scales.
Alessandro Formica – March 2012 All rights reserved
30
Fig. 7 (from the Presentation: “Dynamic Behavior of Materials”, G. Ravichandran, Graduate Aerospace
Laboratories (GALCIT) California Institute of Technology (Caltech), TST Meeting, May 20, 2011)
This figure illustrates the complex set of experimental resources needed to validate a Multiscale
Computational Model and Framework
The research has been carried out in the context of the Hypervelocity Impact (HVI) Program at Caltech
funded and managed by National Nuclear Security Agency (NNSA). Prof. Michael Ortiz is the Program
Director
There is a specific parallelism between developments in the Computational World and developments in the
Experimental/ Testing/Sensing one. In the Computational World we see a growing number of methods and
techniques able to model and simulate phenomena at an increasingly level of detail over an extended range
of space and time scales.
The same trend is characterizing experimentation, testing and sensing. As in the Computational context the
challenge, now, is to devise “integrated strategies” to fully exploit these new potentialities so, in the
Experimental, Testing and Sensing World, we have the same challenge: devising integrated strategies.
The next logical step is a full integration of the two Worlds as envisaged in this White Book to shape
Methodologically Integrated R&D and Engineering Strategies.
Alessandro Formica – March 2012 All rights reserved
31
For “Multiscale Experimental/Testing/Sensing Techniques” we mean:
���� Single Experimental/Testing/Sensing Equipments able to probe “Systems” over a range of space and
time scales.
���� Integration of multiple experimental/testing/sensing equipments. In this case data get from a set of
experimental/testing equipments is integrated to give a comprehensive picture of phenomena/processes.
Multiscale Maps can represent an interesting tool to accomplish this task. Integration can be
implemented over Cyberinfrastructures. .
Fig. 8 (from the “Engineering Microstructural Complexity in Ferroelectric Devices Project
(Multidisciplinary Research University Initiative US Army Research Office)) describes the combination of
experimental and testing equipments needed to characterize the Multiscale Dynamics of a Ferroelectric
Device.
Multiscale Experimentation, Testing and Sensing and related Modeling & Simulation and Data, Information
and Knowledge Analysis and Management Systems have the following objectives in the context of R&D and
Engineering, Materials Characterization, Development, Operational Testing and Monitoring of any kind of
Natural, Human and Technological System:
� Track relationships and interdependencies between phenomena, processes, properties, performance
over a wide range of scales and disciplinary domains (Multiscale Maps)
� Integrate and Fuse Data, Information and Knowledge (Maps) from a spectrum of Disciplinary Areas
(Horizontal Integration)
� Integrate and Fuse Data, Information and Knowledge from multiple Resolution Levels and Scales
(Vertical Integration)
� Integrate and Fuse Data, Information and Knowledge from a wide spectrum of techniques and
equipments: in several cases we can use, for instance, experimental data to complement and analyze
testing and sensing data (Methodological Integration)
Alessandro Formica – March 2012 All rights reserved
32
European Synchrotron Research Facility (ESRF) and Multiscale Experimental Research
The X-Ray Imaging (XRI) beamlines address a large variety of topics with a high scientific and societal
impact. Among these topics we can mention the biomedical research (imaging, radiotherapy, drug action,
metallic particles in biological materials), the multiscale investigation of the most important fossils, cultural
heritage studies, in-situ observation of the growth/failure of materials, porous or granular materials and
storage materials for nuclear waste. The multiscale approach is considered essential for the scientific success
of SR-based imaging: the projected XRI BLs will provide spatial resolutions over four orders of magnitude
(10-8
– 10-4
m), and a wide range of photon energies (2 to 150 keV, as well as infrared).
Synchrotron Radiation from European Synchrotron Radiation Facility (ESRF) has been already applied
several times in a multiscale mode. Scientists from Max Planck Institute (Germany) and the ESRF
discovered the way deformation at the nanoscale takes place in bones by studying it with synchrotron X-
rays.. A bone is made up of two different elements: half of it is a stretchable fibrous protein called collagen
and the other half is a brittle mineral phase called apatite.. In order to understand how this construction is
achieved and functions, scientists from the Max Planck Institute of Colloids and Interfaces in Potsdam
(Germany) ESRF used X-rays from ESRF to see for the first time the simultaneous re-arrangement of
organic and inorganic components at a micro and nanoscale level under tensile stress.
Scientists carried out experiments on ID2 beam line at the ESRF. They tracked the molecular and
supramolecular rearrangements in bone while they applied stress using the techniques of X-ray scattering
and diffraction in real time. The high brilliance of the X-ray source enabled the tracking of bone deformation
in real time. Researchers looked at two length scales: on one side they observed the 100 nanometers sized
fibres, and on the other, the crystallites embedded inside the fibre, which are not bigger than 2 to 4
nanometers. This Multiscale approach is relevant for the whole Biomaterials field.
Integration of Experimental Facilities working with different techniques and covering a spectrum of
phenomena and space and time scales.
ESRF was also involved, jointly with the Laboratoire de Physique et Mechanique des Materiaux CNRS, the
Institut Laue-Langevin and the Institute of Physics of the ASCR, v.v.i. Laboratory of Metals – Praha – Czech
Republic, in the Multiscale analysis by neutron and synchrotron X-ray diffraction of the mechanically-
induced martensitic transformation of a CuAlBe shape memory alloy. The objectives were to determine
stresses and orientations evolutions from macro to micro scale during a stress-induced martensitic
transformation
Multiscale Synchrotron Techniques For Environmental Sciences: nucleation and growth New material synthesis largely depends on novel synthetic routes which are often constrained by increased
environmental concerns. A way to overcome this issue is to perform chemical reactions in miniature volumes
(e.g. microfluidic devices). Scattering techniques offer interesting possibilities in green chemistry,
particularly to monitor reactions at the nanoscale, thereby allowing the conditions to be optimized. In
environmental sciences, control of aerosol growth resulting from the combustion of fossil fuels is a major
issue. Recently, synchrotron scattering experiments have been used to elucidate the mechanism of soot
formation and their multiscale structure in flames and at the exhaust of a diesel engine. These systems can be
classified as open non-equilibrium systems with complex self organisation and intermittent behaviour.
Multiscale Synchrotron Techniques for Pyrolysis Another example is the in situ spray pyrolysis used for the synthesis of nanomaterials. In all these cases, a
combination of SAXS and USAXS provide insight to intermittent structural development from a few
nanometres (nucleation) to micron scale (aggregates and agglomerates). Similar studies can be extended to
dusty plasmas to directly probe charged nanoparticles and their long-range correlations. However, the
systems mentioned above are in a highly non-equilibrium state and to probe the transient processes at large
length scales a long pinhole type USAXS instrument is required. Access to a very wide range of structural
levels from molecular to microscopic scales has potential applications in smart materials vigorously pursued
for addressing the grand challenges in energy research .
Alessandro Formica – March 2012 All rights reserved
33
Strategic Research Agenda for Multiscale Experimentation, Testing and Sensing:
� Development of new Experimental and Testing Systems which are, inherently, Multiscale
Multiresolution, i.e. able to operate over different scales and or at different resolution levels inside a
scale.
� New Techniques to integrate, fuse and analyze Data from a spectrum of Single and Multi Scale
Experimental, Testing and Sensing Systems
� The development of new Strategies and related Frameworks to “rationally” integrate a wide range
of Single and Multi Scale Experimental, Testing and Sensing Systems for specific R&D and
Engineering Tasks. The extension of the “Model” concept to the Experimental, Testing and Sensing
world and the “Information Space” concept can contribute to achieve this objective
� Development of new “two – way” Strategies and related Frameworks to integrate a wide range of
Single and Multi Scale Experimental and Testing Equipments for specific Technology Development
and Engineering Tasks
� The design of new Integrated Schemes and related Frameworks to realize a comprehensive two-way
integration between Multiscale Computational and Multiscale Experimental, Testing and Sensing
Methodologies, Strategies and Environments to define really “Methodologically Integrated
Multiscale R&D and Engineering” Strategies and Frameworks.
Multiscale Computational Modeling and Multiscale Experimentation Integration Materials Research Society Bulletin
An important recognition of the key strategic relevance of the development of multiscale experimental
techniques and their integration with multiscale computational modeling comes from the article “Three-
Dimensional Materials Science: An Intersection of Three-Dimensional Reconstructions and Simulations
(Katsuyo Thornton and Henning Friis Poulsen, Guest Editors), published in the Materials Research
Society (MRS) Bulletin June 2008.
“..For example, by combining a nondestructive experimental technique such as 3D x-ray imaging on a
coarse scale, FIB-based 3D reconstruction on a finer scale, and 3D atom probe microscopy at an even
finer scale, one has an opportunity to capture materials phenomena over six orders of magnitude in
length scale. This will bring materials researchers closer to the ultimate dream of a direct validation of
multiscale models, both component by component and ultimately as an integrated simulation tool. In
conjunction with the advances on the modeling side, such comprehensive experimental information is
seen as very promising for establishing a new generation of models in materials science based on first
principles…..”
Alessandro Formica – March 2012 All rights reserved
34
3.6 Methodologically Integrated Multiscale Science – Engineering Strategies
3.6.1 The Information – Driven Concept
The relevance of “Information”, as a key element to shape R&D and Engineering Strategies, is winning an
increasing attention. Several studies have been performed, for instance, by Jitesh H. Panchal, Janet K. Allen,
David L. McDowell and colleagues at Georgia Institute of Technology. Alessandro Formica highlighted the
role of Information to drive modeling and simulation strategies in the White Paper “HPC and the Progress of
Technology : Hopes, Hype and Reality” published in US by RCI Ltd on February, 1995. In this document
he discussed the concept of “Engineering Information Analysis”. The issue was also dealt with in the context
of the Accelerated Insertion of Materials (AIM) Program (1999) managed by US DARPA. The following
text is drawn from DARPA Proposer Information Pamphlet BAA 00-22 clearly describes the theme and
related challenges:
“The need for an “Information-Driven” strategy . “….There are many interrelated technical challenges
and issues that will need to be addressed in order to successfully develop new approaches for accelerated
insertion. These include, but are not limited to, the following:
The construction of the designer’s knowledge base: What information does the designer need and to what
fidelity? How does one coordinate models, simulations, and experiments to maximize information content?
What strategies does one use for design and use of models, computations, and experiments to yield useful
information? How can redundancies in the data be used to assess fidelity ? The development/use of models
and simulation: What models are required to be used and/or developed in the context of the designer
knowledge base? How can models of different time and length scales be linked to each other and to
experiments? How can the errors associated with model assumptions and calculations be quantified? How
can models be used synergistically with experimental data ?
The use of experiments: Are there new, more efficient experimental approaches that can be used to
accelerate the taking of data? How can experiments be used synergistically with models? How can legacy
data and other existing data base sources be used ?
The mathematical representation of materials: How can one develop a standardized mathematical language
to: describe fundamental materials phenomena and properties; formulate reliable, robust models and
computational strategies; bridge interfaces; and identify gaps between models, theory and experimental
materials science and engineering? How can this representation be used to develop hierarchical principles
for averaging the results of models or experiments while still capturing extremes ?……”
In the context of the “Integrated Multiscale Science – Engineering Framework”, “Information” is a key
element which, to a large extent, drives and shapes R&D and Engineering Strategies.
The term “Information – Driven” means that R&D and Engineering strategies have to address what can be
called “The Information Challenge for R&D and Engineering” :
– What Information at what level of accuracy and reliability (uncertainty quantification) is needed to
accomplish a task
– What Relationships and Interdependencies between analysis and design variables should be tracked over
a full range (as needed) of space and time scales to accomplish a task
– What kind of information sources (analytical, computational, experimental, testing and sensing
models/techniques) are needed and how they can be combined to get the previously identified
information
Alessandro Formica – March 2012 All rights reserved
35
Accordingly, the following key issues define the “The Information - Driven Analysis Scheme for R&D
and Engineering”
���� What Information at what level of accuracy and reliability is thought to be needed to accomplish a R&D
and Engineering task . “Thought to be needed” means that the process is iterative, we start with some
hypotheses and just Multiscale Science Engineering Strategies and related Data, information and
Knowledge Analysis schemes and tools give us the possibility to improve evaluation about the
Information needed to execute the task. Example : What Information (what physical and chemical
phenomena and processes related to materials, structures and chemically reacting flows and their
interactions) at what level of accuracy and uncertainty should we know to analyze the dynamics of a
Thermal Protection Systems of an Hypersonic Vehicle for a specific operational environment?
� What physical length scales and related physical and biochemical phenomena rule the dynamics of the
“system” under analysis, what is the relative weight, what are relationships and interdependencies
between phenomena and processes inside a scale and between different scales (to be described thanks to
Multiscale Maps).
� What Information at what level of accuracy and reliability can existing analytical, computational models
experimental, testing and sensing techniques and related coupling scheme give us (to be described using
the “Multiscale Science – Engineering Information Space”).
���� How good analytical and computational models, experimental, testing and sensing techniques and related
coupling schemes should be to get the previously identified information thought to be needed to
accomplish a task. How “good” means evaluating how much “physical realism” should be incorporated
into the models and what scales hierarchy has to be taken into account. Not in all the cases, of course,
we really need complex multiscale methodologies going down to the Schrödinger equations: simple
single scale models can be accurate and reliable enough.
Note: This kind of Information is critical to evaluate what new analytical and computational models and
what new experimental, testing and sensing procedures/techniques should be developed and integrated to
deal with a specific analysis task. It is important to identify not only what we know, but, in particular,
what we do not know, what we should know, how we should know it (what combination of scientific
and engineering methodologies and technologies should be needed). In this context, the “lack of
Knowledge” becomes and important element to guide Strategies.
���� What is the right combination and the right sequence of application (Integration Strategy: Designing the
Analysis and Design Processes) of single and multi scale analytical and computational, models/methods
and single and multi scale experimental, testing and sensing procedures/techniques to get Information
thought to be needed to accomplish a specific analysis/design task. A critical step for the “Rational
Design” of R&D and Engineering Processes is a proper selection, integration, and sequencing of
computational and analytical models and experimental/testing/sensing techniques and procedures with
varying degrees of complexity and resolution to deal with a specific “Task”. To do that we have to define
the “Multiscale Science-Engineering Information Space” associated to computational models and
experimental, testing and sensing procedure/technique and related coupling schemes. Application
Strategies defined in the Paragraph 3.6.2 and Integration Strategy Maps guide the Integration Strategy.
� Furthermore, another very critical issue is that we need a rational approach to link advances in the
different methods at the different scales with the new information we need to meet challenges in the
different tasks in the different stages of the R&D and engineering process. How do we effectively and
timely evaluate the impact of scientific methodological and information advances at an atomic,
molecular, and grain (for materials) level on new technological and engineering solutions if we do not
have conceptual and methodological (multiscale) frameworks to link methods and information at the
different scales: from atomic to continuum? The “Multiscale Science-Engineering Information Space”
can represent a first step to deal with these critical issues. If we like to shape new cooperative schemes
between industry, from one side, and academia and research, from the other side, we have to define
specific methodologies to evaluate the “industrial and technological value” of new scientific
methodological advances.
Alessandro Formica – March 2012 All rights reserved
36
Max-Planck Institut für Eisenforschung Düsseldorf, Germany
Methodologically Integrated Multiscale Strategies
Fig. 9 Multiscale Computational and Experimental/Characterization Integrated Strategy applied by the
Max-Planck Institut für Eisenforschung in the Materials field.
It is clear the central role played by the integration of the Computational Multiscale World with the World of
Multiscale Characterization/Experimentation
Alessandro Formica – March 2012 All rights reserved
37
3.6.2 Methodological Integration Schemes, Maps and Strategies The “Multiscale Science – Engineering Information Space” and the “Information – Driven” concept
(described in the paragraph 3.6.1) allow us to define new “Applicability Conditions” and “Predictability Criteria” for Computational Models to shape “Application Strategies” for Modeling and Simulation and
their integration with related “Experimentation, Testing and Sensing Application Strategies”
The final goal is the development of “Methodologically Integrated Multiscale Science - Engineering Strategies” which represent a very important element of the New Framework here described.
The definition of “Applicability Conditions” and related “Predictability Criteria” for computational models
implies the ability of establishing specific rules and schemes that allow researchers, designers, and planners
to evaluate, with a high degree of reliability, where, when, and to what extent, it is possible to safely
(quantifying in probabilistic terms risks and uncertainties) substitute modeling & simulation for
experimentation and testing and where, when, how and to what extent we need to integrate modeling &
simulation with experimentation, testing and sensing for specific tasks.
Applicability Conditions. Two basic conditions which rule the development and the implementation of
predictive models and their integration with experimental and testing techniques can be defined:
� researchers and engineers are able to formulate hypotheses about what Information is needed to
accomplish a R&D and Engineering task:
� what physical length scales and phenomena/processes and relationships/interdependencies are
important for specific R&D and Engineering tasks and purposes.
� at what level of accuracy and reliability phenomena/processes should be modeled and simulated
� researchers and engineers are able to define the range of validity of the models and, inside this range,
the degree of accuracy and reliability of the same models.
Applicability Conditions can also be applied to the Experimental, Testing and Sensing Fields. A detailed
comparison of the “Information” which can be get by the respective analyses with the “Information” we
think it is needed to accomplish a specific Task is an important element to shape “Methodologically
Integrated” Strategies
Predictability Criteria When we discuss about predictive capabilities of models in the R&D and Engineering context, we should
carefully take into account two critical issues: predictive consequence and confidence.
���� Predictive Consequence: what is the impact of errors/uncertainties for specific tasks? Errors/uncertainties
can be relatively large but their impact can be low. On the contrary, errors and uncertainties can be
limited but their impact can be very large.
���� Predictive Confidence: how to assess models errors and uncertainties in order to evaluate the level of
confidence? [Multiscale Science – Engineering Information Space and Verification & Validation
methods]
Application Conditions and Predictability Criteria are important “Guiding Principles” to define Multiscale
Modeling and Simulation Application Strategies and to shape “Methodologically Integrated Multiscale
Science – Engineering Strategies”.
The final objective is to define “Integration Strategy Maps” which describe:
� What analytical theories, single and multi scale computational models and what single and multi scale
experiments, tests and sensing systems and models have to be selected to deal with a specific task
� What is the order of execution and the overall Integration Scheme as shaped by the “Applicability
Conditions” and “Predictability Criteria” (Multilevel Network of Computational, Experimental, Testing
and Sensing Models/Methods and Techniques)
� What is the flow of input and output data and information/knowledge (Maps) among the full spectrum of
models and experiments/tests/sensing models and techniques.
Alessandro Formica – March 2012 All rights reserved
38
Several hypotheses can be taken into account and interactively changed during application.
For each specific task, “Integration Strategy Maps” describe:
� The full set of Analytical Theories/Formulations, Computational, Experimental, Testing and Sensing
Models/Methods/Techniques applied to deal with specific task
� The order of execution and Integration Scheme: Multilevel Network of Multiscale Analytical,
Computational, Experimental, Testing and Sensing Models and Techniques.
� Multiscale Science – Engineering Information Spaces
� Input and Output Data and the related Flow between “Models”
� Multiscale Maps
We consider three “Integration Areas”
1) Inside the Experimentation, Testing and Sensing World: Integration of a full set of single and
multiscale experiments, tests and sensing measurements (performed with a number of equipments)
applied to accomplish a specific R&D and Engineering tasks
2) Inside the Computational World: Integration of a full set of single and multiscale multiphysics
computational models applied to accomplish a specific R&D and Engineering task
3) Between the Experimental, Testing, Sensing and the Computational Word: Integration of a full set of
single and multiscale experimental , testing and sensing techniques with a full spectrum of
computational models experiments and tests to accomplish R&D and Engineering tasks.
“Integration Strategy Maps” are built applying the “Information – Driven Analysis Strategy”, the
“Multiscale Science – Engineering Information Space” concept and method, the “Applicability Conditions”
and the “Predictability Criteria”.
“Integration Strategy Maps” defined during the R&D and Engineering Process are recorded, organized and
managed by a specific Integration Strategy Maps Data Base
Alessandro Formica – March 2012 All rights reserved
39
Integration Strategy Maps can be linked to and integrated with Physics Maps, “Requirements, Property,
Structure, Performance” and “Processing, Property, Structure, Performance” Maps
Fig. 10 Integration Strategy Map (from US Department of Energy (DoE) Fusion Materials program: Aspects
of Multiscale Modeling Primary Damage and Rate Theory Models Presentation – R. E. Stoller – Metals and
Ceramics Division Oak Ridge National Laboratory)
This Figure describes a possible combination of the proposed Multiscale Map of Physics and Multiscale
“ Integration Strategy Map” of Computational Models and Experiments & Tests
The next page Box synthetically describes the key role and significance of a comprehensive integration
between Computation and Experimentation to address challenging R&D and Engineering issues. Integration
should be planned and applied not only inside the Computation Verification & Validation (V&V) process,
but, also, in the core of the R&D and Engineering activities. Multiscale Computation (Modeling &
Simulation) and Multiscale Experimentation Integration can deeply change structure and strategies of the
R&D and Engineering Process.
Alessandro Formica – March 2012 All rights reserved
40
Frontiers in Energy Research (US Department of Energy): January 2012
Experiment and Theory: The Perfect Marriage
Scientists combine measurements and calculations to explain energy’s most puzzling problems
Gareth S. Parkinson Experimentalists and theorists working together represent the best approaches to securing our energy
future. In establishing 46 Energy Frontier Research Centers, the U.S. Department of Energy moved to
expedite the rate of scientific discovery by encouraging teamwork in a community more accustomed to
relying on individual brilliance. This approach to science funding, which takes its lead from the
common proverb individuals play the game, but teams beat the odds, brings together scientists with
diverse backgrounds and skill sets to solve the most pertinent problems in energy research. The spirit of
collaboration is exemplified by the way in which theorists and experimentalists, often seen as different
breeds of scientist, are combining their very different approaches to attack the most highly complex
problems. Effective synergy between experimentalists and theoreticians is important in modern science
because the type of information provided by each method is fundamentally different, but
complementary. The experimentalists obtain precise measurements of the properties of a system, but
often struggle to explain complex phenomena without making assumptions about the system.
Theoreticians, on the other hand, use computer simulations to model the processes at work, but need
guidance from experiment to know if they are on the right track. While an integrated approach often
results in a deeper understanding of the system under study, bringing experimentalists and theorists
around the same table also greatly speeds up the discovery process by slashing the time between theory-
experiment iterations. In the absence of direct collaboration, experimental and theoretical groups
working on the same problem learn of each other’s progress through the scientific literature or at annual
meetings. Working together from the outset allows areas of agreement and disagreement to be quickly
identified, and a consensus can be more quickly reached.
There are many examples of the integrated experiment-theory approach bearing fruit in the EFRCs. For
instance, recent research into lithium batteries, conducted in the Nanostructures for Electrical Energy
Storage Center, combines experimental methods and calculations based on quantum mechanics to show
that coating lithium electrodes with an insulating aluminum oxide layer could significantly extend
lithium battery lifetimes. The results demonstrate that a fundamentally different electron flow process
occurs in the presence of the insulating alumina film, leading to significantly slower rates of electrolyte
decomposition inside the battery.
A second example, from the Center for Molecular Electro catalysis, combines experiment and theory to
better understand a promising method for converting hydrogen molecules into electricity, as would be
done in a fuel cell. Experimentalists measured the rate at which a novel nickel-containing catalyst
molecule is able to move protons that arise from the splitting of hydrogen. The theoreticians in the
project simulated how protons move within the molecule, and determined that the main bottleneck in
the process occurs when the catalyst molecule changes its shape.
Innovation central In addition to revolutionizing energy technologies, the EFRCs are tasked with the creation of a new
generation of tools for penetrating, understanding and manipulating matter on the atomic and
molecular scales. In the Center for Atomic-Level Catalyst Design, researchers are focused on
developing the experimental and theoretical tools required to understand how catalysts convert one
molecule into another, such as when carbon monoxide is converted to carbon dioxide in a modern car
exhaust. The key issue holding back significant progress in this area is that the current methods for
understanding catalytic reactions at the atomic scale can only handle extremely simplified model
systems that do not necessarily bear any resemblance to the real catalysts doing the job. Center director
James Spivey explains: “Typically only reactions on ideal catalyst surfaces can be simulated. Such
surfaces do not represent real catalysts. We are attacking this problem.”
In this issue of the EFRC newsletter, several excellent examples of experiment-theory collaborations
are highlighted. As will become clear on reading the articles, this integrated approach has yielded
success across the entire breadth of topics covered by the EFRCs, and represents one of the best
approaches currently available to achieve the rapid advancements required to secure our energy future.
Alessandro Formica – March 2012 All rights reserved
41
The Predictivity and Validation Issues
The National Nuclear Security Program (NNSA), in the context of the Advanced Simulation and
Computing (ASC) Initiative, established the Predictive Science Academic Alliance Program
(PSAAP) focusing on the emerging field of predictive science—the application of verified and
validated computational simulations to predict the behavior of complex systems where routine
experiments are not feasible. The goal of these emerging disciplines is to enable scientists to make
precise statements about the degree of confidence they have in their simulation-based predictions.
Five PSAAP Centers have been created:
California Institute of Technology: Center for the Predictive Modeling and Simulation of High-
Energy Density Dynamic Response of Materials; Purdue University: Center for Prediction of
Reliability, Integrity and Survivability of Microsystems (PRISM); Stanford University: Center for
Predictive Simulations of Multi-Physics Flow Phenomena with Application to Integrated Hypersonic
Systems; University of Michigan: Center for Radiative Shock Hydrodynamics (CRASH); University
of Texas at Austin: Center for Predictive Engineering and Computational Sciences (PECOS)
The following text, drawn from the Presentation “Can Complex Material Behavior be Predicted?
Given by Prof. Michael Ortiz, Caltech PSAAP Center Director, at the DoE NNSA Stockpile
Stewardship Graduate Fellowship Program Meeting Washington DC, July 14, 2009, illustrates
objectives and approach underlying the general PSAAP Strategy and Methodology concerning
Validation and Predictivity challenges:
PSAAP Caltech High-Energy-Density Dynamic Response of Materials (Hypervelocity Impact
Application Field) Center objective:
− rigorous certification of complex systems operating under extreme conditions. l
Overarching Center objectives:
− Develop a multidisciplinary Predictive Science methodology focusing on high-energy-density
dynamic response of materials
− Demonstrate Predictive Science by means of a concerted and highly integrated experimental,
computational, and analytical effort that focuses on an overarching ASC-class problem:
Hypervelocity normal and oblique impact at velocities up to 10km/s
Overarching approach:
− A rigorous and novel Quantification of Margin of Uncertainty (QMU) methodology will drive
and closely coordinate the experimental, computational, modeling, software development,
verification and validation efforts within a Yearly Assessment format
Two issues deserve to be highlighted:
− The central role of the “Uncertainty Quantification” and “Quantification of Margin of
Uncertainty” issues in the context of the Computational Models Validation effort to shape R&D
and Engineering activities. This vision can be, to some extent, related to the previously illustrated
concepts: Multiscale Science – Engineering Information Space, Range of Validity and
Information Driven R&D and Engineering Strategy
− The key role of Computational, Analytical and Experimental Efforts Integration. New
(multiscale) experimental techniques and analytical (theoretical) developments are fundamental to
develop and apply new and more powerful (predictive) computational models and strategies. The
Vision is in line with our “Methodologically Integrated R&D and Engineering” approach
Alessandro Formica – March 2012 All rights reserved
42
3.6.3 Multiscale Knowledge – Based Virtual Prototyping In the new conceptual and methodological context, classical “Virtual Prototyping” concept should be
complemented by a new concept which can be referred to as “Multiscale Science – Engineering Knowledge
– Based Virtual Prototyping”. Classical concepts can be regarded as a particular case of this more general
concept and strategy. Classical “Virtual Prototyping” approach is applied when “Applicability Conditions”
can be met and “Predictability Criteria” can be reliably evaluated also thanks to the “Knowledge” gained
with Multiscale Science - Engineering Maps and “Multiscale Science – Engineering” Information Space”
concept and method. For this reason we can add the term “Knowledge – Based”.
The previously described theoretical and methodological apparatus allows us to formulate rational
hypotheses about what experiments, tests and sensing measures are really needed to get the information we
think to be necessary to characterize the behaviour of a “System” at a predefined level of accuracy and
reliability, and, accordingly, assess the “risk” associated to replace experimentation, testing and sensing
with computation for a specific task. This a fundamental condition to replace in a “rational” way testing with
computation. In this new context, we can design and plan highly complex Multiscale Multilevel Testing
Strategies guided by “Multiscale Computational Models” and related Multiscale Maps and Multiscale
Information Spaces. Data, Information and Knowledge “flow” in a seamless and fully integrated way
between the Testing and Computational Worlds and vice versa. Not only, with the new theoretical and
methodological apparatus, we can easily integrate inside Testing Strategies even “Information Capabilities”
of several Experimental and Sensing Facilities. Multiscale Maps from Experimentation can also contribute to
understand possible Testing Anomalies and Problems. This kind of integrated analyses can, in turn, suggest
new Experimental activities.
An interesting application field for methods and tools described in this document is what can be called “Multiscale Science – Engineering System Testing”. A problem is to transfer, in a structured way,
information and knowledge get from testing at a scale to the higher scales of a System along the whole chain:
from testing carried out to characterize behaviour of materials (basic constituents of any System) [coupon
testing] to testing of devices, components, sub-systems and the global system. We should correlate
Multiscale Maps for all the scales and resolution levels. Correlation works in both the directions: bottom –
up and top – down:
− Bottom – Up: Multiscale Maps built from materials testing can be a useful basis to develop upon testing
strategies for devices. Multiscale Maps built from devices testing are applied to improve testing
strategies for components and so on along the scale.
− Top – Down: results from testing at a scale can be better analyzed taking advantage of Multiscale Maps
get from testing at lower scales
Alessandro Formica – March 2012 All rights reserved
43
3.7 Designing the R&D and Engineering Process
3.7.1 R&D and Engineering Analysis and Design Process Architecture
A first step to “Design the R&D and Engineering Process” is to identify the “elements” which characterize
its structure and track relationships and interdependencies:
���� R&D and Engineering Process Architecture Any R&D and Engineering Project/Process can be decomposed into Multilevel Networks of Phases
(Time Intervals inside which specific activities are accomplished) Each Phase can be subdivided into a
Multilevel Network of R&D and Engineering Strategy Modules.
���� R&D and Engineering Strategy Modules Architecture
The Architecture (Strategy) to carry out a generic “R&D and Engineering Project” is described by a
multilevel network of R&D and Engineering Strategy Modules
Any R&D and Engineering Strategy Module can be, recursively, decomposed into Multilevel Networks
of simpler R&D and Engineering Analysis and Design Modules. At the lowest level, R&D and
Engineering Analysis and Design Modules can be decomposed into Multilevel Networks of Tasks. At
the lowest Task Level, Integration Strategy Maps are defined. A specific R&D and Engineering
Strategy Management System manages and organizes all of that
���� System Architecture/Structure (detailed over the full set of levels/scales, as needed)
− Multilevel Multiscale Network of Architectural/Structural Elements: Systems (or System of
Systems) – Sub Systems – Components – Devices – Basic Structures (Materials, Fluids,
Plasmas)
���� R&D and Engineering variables (projected over the full set of System Architectural/Structural
Elements at all the levels and scales)
− Requirements
− Performance
− Properties
− Functions
− Requirement - Performance – Structure – Property Relationships
− Performance - Property -Structure – Processing Relationships
− Architectural/Structural Element – Function Relationships
− Analysis and Design Variables
Relationships and interdependencies are described by the full set of Multiscale Maps.
���� R&D and Engineering Project Performing Entities
All the activities needed to achieve objectives inside each R&D and Engineering Phase are accomplished
by a Multilevel Network of “Physical Entities” and “Human Entities:
� Human and Management Entities:
− Organizations/Institutions
− Teams
� Physical Entities
− Theoretical, Computational, Experimental, Testing, Sensing Centers and Facilities,
Manufacturing Facilities, Cyberinfrastructural Frameworks
The Multilevel Network of Entities defines what can be called a “Multiscale Science – Engineering
Collaboratory Framework”
Alessandro Formica – March 2012 All rights reserved
44
3.7.2 R&D and Engineering Analysis and Design Strategy Management System
Te following elements characterize the R&D and Engineering Analysis and Design Strategy Management
System:
� Strategy Modules
� Analysis Modules
� Design Modules
� Hypothesis and Decision Modules which keep track of the spectrum of Architectural/Structural,
Analysis and Design Hypotheses and Final Decisions adopted during the R&D and Engineering Process.
� Tasks
� Integration Strategy Maps described in the Paragraph 3.6.2 and applied inside the lowest Hierarchical
Analysis Tasks Level
The R&D and Engineering Analysis and Design Strategy Management System allows to track, organize and
manage of the previously identified elements and their relationships and interdependencies.
Strategy Modules
Strategy Modules are constituted by Multilevel Networks of R&D and Engineering Analysis and Design
Modules and Tasks.
− R&D and Engineering Analysis and Design Modules (each R&D and Engineering Analysis and
Design Module (linked to a specific Phase) can be decomposed into a multilevel network of more
elementary Analysis and Design Modules. At the lowest network level, R&D and Engineering Analyses
and Design Modules can be decomposed into a multilevel network of Tasks
− Tasks (each Task can be decomposed into a multilevel Network of more elementary Tasks)
− Hypothesis and Decision Modules
R&D and Engineering Design Modules
Any R&D and Engineering Design Module can be broken down in a multilevel network of lower level R&D
and Engineering Design Modules
R&D and Engineering Design Modules describe:
���� The full set and hierarchy of R&D and Engineering Design Modules and Tasks linked to them for each
Phase
���� The full set of Architectural/Structural and Functional Maps linked to them
���� R&D and Engineering Objectives, Analysis and Design Variables, and Analysis – Design Variable
relationships
���� The network of Analysis Modules linked to any R&D and Engineering Design Module
R&D and Engineering Design Modules are recorded and managed in a specific “R&D and Engineering
Design Modules Data Base”
R&D and Engineering Analysis Modules
R&D and Engineering Analysis Modules in each Phase are organized in a Multilevel Network of more
elementary Analysis Modules. Any Analysis Modules of high level can embody “Analysis Modules” of a
lower level. “Analysis Modules” can embody a multilevel network of “Analysis Tasks”.
At the lowest level, “Analysis Tasks” define what Analysis Strategies (multilevel network of analytical
formulations, computational models and experimental/testing/sensing Models/Techniques) are applied to
achieve Analysis Objectives. “Integration Strategy Maps”, described in the Paragraph 3.6.2, describe these
strategies. Analysis Modules and Tasks are linked to Design Modules and Tasks
It is possible to develop Analysis Modules tailored for specific issues and tasks such as durability or
producibility of composites or metallic or hybrid materials or structures, or the dynamical analysis of a sub-
system, a component or a device.
Alessandro Formica – March 2012 All rights reserved
45
Analysis Modules track and organize Data, Information and Knowledge inside and between the different
tasks in the different phases of the Technology Development and Engineering process and, for each task,
correlate data and information with information sources (experiments, tests, computations, analytical
formulations)
R&D and Engineering Analysis Modules and Tasks are recorded and managed in a specific “R&D and
Engineering Analysis Modules and Tasks Data Base”
Computational Code and Model Libraries
Libraries are software environments which allow to catalogue and manage a whole set of :
���� Computational codes which implement a spectrum of methods [Molecular Dynamics, Coarse Grained
MD, Monte Carlo, Density Functional Theory, Phase Fields, Dislocation Dynamics, Continuum Finite
Elements,….]
���� Computational Models and related links to Tasks where they are applied
���� Single and Multiscale Multiphysics coupling methods and schemes.
For each Computational Model (linked to a specific Task), Library describes:
− The specific Computational method applied
− Characteristic of the modeled “System”
− Model Dimension
− Boundary and Initial Conditions
− Maps
− Links to the tasks where it has been employed
− The specific coupling scheme(s) among a cluster of models in case of Multiscale Techniques
Experimental, Testing and Sensing Technique/Equipment and Model Libraries
Libraries are software environments which allow to catalogue and describe:
���� Experimental, Testing and Sensing Techniques (STM, AFM, TEM, SEM,…) and related specific
application methods which implement them
For each Experimental, Testing and Sensing Technique/Equipment, Libraries detail:
� Characteristics, applicability conditions and what kind of information can be get from them for specific
application domains and conditions
� Single and Multiscale coupling methods and schemes.
For each Experimental/Testing/Sensing Model linked to a specific Task, Library describes:
− The specific Experimental/Testing/Sensing technique applied
− Characteristic/State of the probed “System”
− Experimental/Testing/Sensing operational mode
− Maps
− Links to the tasks where it has been employed
− The specific coupling scheme(s) among a cluster of models in case of Multiscale Techniques
Alessandro Formica – March 2012 All rights reserved
46
3.7.3 “Integrated Multiscale Science – Engineering Analysis Strategies”
“Integrated Multiscale Science – Engineering Analysis Strategies” are implemented inside the “Analysis
Modules” described in the Paragraph 3.7.2 and they are a key element to support Design Strategies embodied
in the “Design Modules”.
Integrated Multiscale Science – Engineering Strategies are absolutely general Analysis Strategies, they can
be applied to any task, in any context for any purpose in any phase of the R&D and
Engineering/Manufacturing Process.
Analysis Strategies can be applied to analyze dynamics of:
���� any “System” and the interaction among all of its components (any level and scale)
���� interactions between the “System” and other “Systems” (System of Systems)
���� interactions between a “System” and the “Environment” where it operates for nominal and off-nominal
(accident included) situations.
We would like to state that, in this document, multiscale stands for “multiscale multiphysics” and that
multiscale is a general term and it embodies, as a special case, classical single scale models and analyses
which can, in turn, take advantage of “Reduced Order Models” built upon multiscale analysis schemes. The
term “Integrated” is used because R&D and Engineering Strategies are based upon a full integration of
computational and experimental, testing and sensing models, techniques and strategies.
It should be highlighted that for “Multiscale Computational Models” we mean not only “Classical
Computational Models”, but, also, “Multi and Single Scale Agent Based Models.
A key goal of Multiscale Information - Driven Strategies is to develop a hierarchy of Multiscale Multiresolution Multiphysics (Computational, Experimental, Testing and Sensing) Models. Each model should be characterized by the level of complexity thought to be needed to get the Information to accomplish specific tasks: no more no less. Citing Einstein: A model must be as simple as possible, but not simpler
“Integrated Multiscale Science – Engineering Analysis Strategies” synthesize and take advantage of all the
concepts and methods described in the previous paragraphs:
� Data, Information and Knowledge Structures and Analysis Schemes (Multiscale Knowledge Domains
and Multiscale Maps)
� The “Multiscale Science – Engineering Information Space” and “Information – Driven” concepts
� The “Modeling and Simulation” as “Knowledge Integrators and Multipliers”“ and “Unifying Paradigm
for “Scientific and Engineering Methodologies” and “Knowledge Domains” concept and the related
“Methodologically Integrated Multiscale Science – Engineering Strategies” .
Analysis Strategies take advantage of the full spectrum of Multiscale Methods (hierarchical, concurrent,
adaptive,..). The full spectrum of Multiscale schemes can be applied in an integrated way to achieve specific
objectives. The “Computational Materials Design Facility (CMDF), developed at Caltech and MIT,
introduced the term “Multi Paradigm” for this scheme. Top – Down Analyses are integrated, as needed, with
Bottom – Up analyses.
Four application schemes for Multiscale Analysis Strategies can be devised:
� Multiscale Scientific Analyses finalized to “Understand” Physical and Bio - Chemical Phenomena
and Processes and their Relationships A spectrum of multiscale computational, experimental, testing and sensing methods linked using a full
range of coupling schemes (multi paradigm approach) are applied to gain a unified understanding of
scientific and engineering phenomena/processes and elucidate relationships and interdependencies between
phenomena, processes and system architectural/structural elements inside a scale and across different scales.
Multiscale Maps give a coherent view of the network of relationships and interdependencies among
“System Dynamics” variables turning data from different sources into Knowledge
Alessandro Formica – March 2012 All rights reserved
47
� Multiscale Science – Engineering Analyses. Computational Scientific (Quantum – Atomistic – Micro) Models are directly coupled (On – Line Coupling)
with Meso and Macro (Engineering) Computational Models in order to have an Integrated Multiscale Nano
To Macro Analysis Framework . This kind of scheme has been already developed. A issue to be taken into
account in this case is that this Application Line and Approach is, normally, expensive from a computational
point of view.
� Reduced – Order Modeling, Sub – Grid Models and Constitutive Equations Development Reduced-Order Models, Sub – Grid Models and Constitutive Equations are built, taking advantage of
Knowledge get by Multiscale Scientific Analyses described in the previous item. Constitutive Equations and
Sub – Grid Models are inserted inside classical Engineering codes. This approach can also be referred to as
“Multiscale Off – Line Approach”. Multiscale Scientific Analyses are an important element to build
“Hierarchies of Multilevel Multiscale Computational, Experimental, Testing and Sensing
Models/Techniques. In this perspective, Reduced – Order Models and “Hierarchies” can be regarded as a
synthesis and integration of science and engineering. The fundamental objective is to improve reliability,
range of validity, and effectiveness of models applied in the different phases of the Research, Technology
Development and Engineering Process and for Systems and Life – Cycle Engineering issues. It is important
to highlight that Knowledge get from Multiscale Scientific Analyses is captured and organized not only by
reduced order modeling, but also by Multiscale Maps. This kind of strategy allows to directly insert
“Multiscale Knowledge” inside classical Engineering/Manufacturing/Processing models and codes. These
flexible integration strategies allow Engineering Teams to use, in a systematic way, scientific knowledge
without having to directly manage the complex modeling and simulation process of basic physical and
chemical phenomena. Such task would require highly specialized knowledge which is, normally, outside the
reach of designers.
� Integrated Multiscale R&D and Engineering Strategies The previously indicated approaches, with particular reference to the On and Off –Line Schemes, can be
integrated in an interactive way inside a more general strategy. Some tasks can be executed with the
Multiscale Scientific Analysis approach. Some other tasks can be carried out by applying Reduced – Order
Science Based Modeling. It is important to emphasize that the application of Multiscale Strategies demands
some not secondary modifications in the projects organization, structuring and management. In particular, a
fundamental element is the definition of “Integrated Multiscale Multidisciplinary Teams”.
Integration develops over three lines:
���� Disciplines: physics, chemistry, electronics, biology,
���� Scales: specialists who operate in various in Scientific and Engineering areas
���� Methodology: specialists who operate in the three methodological contexts: Theory, Computational,
Experimentation & Testing
A fundamental issue for all the Multiscale Strategies is to adopt an “Adaptive and Multi Step Selection of Details and Resolution”
Integrated Multiscale R&D and Engineering Analysis Strategies develop over the following phases and
steps:
1) Definition of Multiscale Analysis Process Architecture
– Definition of the “System” Architecture
– Identification of the reference scales of the “System” to be analyzed (the selection is linked to
specific analysis objectives and tasks)
– Definition of Functions to be performed by the “System” for the full hierarchy of its “Elements”
– Definition of the [Requirements - Performance – Properties – Architecture/Structure Relationships]
– Definition of the overall Architecture of the Analysis Process: Multilevel Network of R&D and
Engineering Phases, Analysis Modules and Tasks and related relationships and interdependencies.
More hypotheses can be worked out. Hypotheses are tuned and/or modified following Analysis
results.
Alessandro Formica – March 2012 All rights reserved
48
This first step is accomplished setting up some hypotheses built upon the knowledge available at the
starting time.
2) Analysis Strategies Definition Multiscale Maps, at the starting time, are built using existing information and knowledge and processing
available data (historical data bases). Then, Analyses deliver new data that allow to iteratively and
interactively modify first Map hypotheses.
For each Task of the previously identified Tasks Network:
– Identification of physical and bio - chemical structures over the selected scales which are thought
to be relevant for the “Objectives” of the Analysis [Architectural/Structural Maps] – first working
hypothesis
– Identification of bio - chemical and physical phenomena/processes and their interdependencies
underlying and characterizing the dynamics of a system and thought to be relevant to meet with
the “Objectives” of the Analysis for the full range of the selected scales [Physics Maps] - first
working hypothesis
– Definition of the “Requirements - Performance – Properties – Architecture/Structure” relationships
inside a scale and over the range of the selected scales [Requirements - Performance – Properties –
Architecture/Structure Map] - first working hypothesis
– Identification of “ Processing – Architecture/Structures” relationships (if it is needed in the analysis
process) inside a scale and over the range of the selected scales [Processing – Architecture/Structure
Map] - first working hypothesis
– Definition of what kind of Information is thought to be needed to achieve Analysis Objectives for
the Analysis Tasks. [“Thought to be needed” means that the process is iterative and interactive, we
start with some hypotheses and just Multiscale Science Engineering Analyses give us the possibility
of improving our evaluation]
– assessment of what Information at what level of accuracy and reliability can, existing analytical
theories, computational models, experimental testing and sensing techniques and related coupling
schemes, deliver (evaluation performed using the “Multiscale Science – Engineering Information
Space” and historical available Information).
– Definition of how good” (Multiscale Science – Engineering Information Analysis) analytical and
computational models, experimental, testing and sensing models and techniques and related
coupling schemes should be to get the previously identified information . That means evaluating if,
where, when and to what extent we have to take into account a hierarchy of scales and develop and
apply new multiscale models and new reduced order models instead of existing single and
multiscale models. Not in all the cases, of course, we should go down until Schrödinger equations
from the continuum. Don’t Model Bulldozers with quarks (Goldenfeld and Kadanoff, 1999)
– Identification of what new analytical and computational models and what new experimental,
testing and sensing techniques should be developed
[Note : A New Approach to Analysis. The “Multiscale Science – Engineering Information Space”
and the “Information – Driven Analysis” concepts and methods help us to identify not only what we
know, but in particular, what we do not know, what we should know, how we should know it (what
combination of new scientific and engineering methodologies and technologies should be needed)]
– Development (if it is needed) of new analytical theories, experimental, testing and sensing
techniques and computational (reduced order models included) models, definition of the related
coupling schemes inside a scale and between different scales.
– Identification of experimental, testing and sensing techniques needed to validate computational
models.
– Definition of Verification and Validation strategies for all the models and the coupling schemes
Alessandro Formica – March 2012 All rights reserved
49
− Definition of “Methodologically Integrated Strategies”: what is the right combination and the right
sequence of application of single (including reduced order models and analytical formulations) and
multiscale computational models and single and multi scale experimental, testing and sensing
models/techniques to get Information thought to be needed to accomplish specific analysis tasks
[Integration Strategy Map]
Note: from a general point of view, it can be advisable to adopt a “Multiscale Multiphysics Multilevel
Multistep Adaptive” Modeling and Experimental, Testing and Sensing Strategy. Multistep Adaptive means
that we start with some simple models, experiments and tests to get a first acquaintance of the dynamics of
the system. The analysis of data, information and knowledge (Multiscale Maps) get from a first run makes it
possible to adaptively increase complexity levels) of models, experiments and tests only as needed for
specific tasks.
3) Analysis Execution
For each Task:
���� A first run of Computations, Experimentations, Tests and Sensor measures is performed
���� in and out data and information flow linked to the selected computational models and experimental,
testing and sensing models/techniques is tracked
���� Data from Computations, Experimentations , Testing and Sensing is analyzed, correlated and organized
using Multiscale Maps
���� All Maps, and Strategy Modules are updated, as needed, following analysis results
���� New Multiscale Methodologically Integrated Strategies (if it is deemed to be necessary) are formulated
���� Evaluations of the impact of the results over Analysis and Design Hypotheses and the Architecture of
Technology Development and Engineering Analysis and Design Modules are carried out
The Process is iterative. We can have more iteration levels:
���� Inside each Analysis Task of a specific Analysis Module
���� Between Tasks inside a specific Analysis Module: results of a Task analysis can change analysis
conditions for the other Tasks
���� Inside the Multilevel Hierarchy of Modules (Architecture of the Multilevel Network of Modules can be
changed)
Specific Modules and Tasks can be devoted to develop Reduced – Order Models, Sub – Grid Models and
Constitutive Equations.
Alessandro Formica – March 2012 All rights reserved
50
3.8 Integrated Multiscale Science – Engineering Framework Applications
3.8.1 Multiscale Systems Engineering
This paragraph is devoted to a synthetic analysis of the application of the Integrated Multiscale Science –
Engineering Framework to a field of growing relevance for a wide range of Engineering fields such as
Chemical/Processing Engineering, Civil Engineering, Aerospace Engineering.
Integration among a wide range of technologies and a full spectrum of sub-systems, components, devices,
and materials is, today, a fundamental challenge in the analysis, development and design of high-tech
systems. In the future, the widening use of a full hierarchy of nano, micro, meso technologies, devices and
components will make this issue even more critical. System Engineering will, more and more become a
Hierarchical “Multiscale” Systems Engineering. Nanotechnology, Nano To Macro Integration and
Multiscale (Computational, Experimental, Testing and Sensing) will be the catalysts for this process
Because, today, it begins to be possible to analyze the dynamics of systems at multiple scales, the next step
is to use “Integrated Multiscale Science - Engineering Strategies” to design hierarchical systems at multiple
scales. That means being able to design systems in such a way as to make multiple “structures/elements” at
different scales cooperating to produce an increasingly wider spectrum of properties and functions and
higher performance levels.
Multiscale System Design This Figure, from MIT, clearly illustrates the “Multiscale System Design” Concept. This Concept and
Design Strategy allows to better meet with a widening spectrum of tighter and tighter Requirements
(Environmental Compliance, Efficiency, Safety, Security, Operational Flexibility,…) by increasing
Functionalities, Design Parameters and related Solutions, Architectures and Process variables.
Alessandro Formica – March 2012 All rights reserved
51
In the “Multiscale System Engineering” field, Analyses Challenges are linked to the following issues:
���� Analysis of Requirements over the full spectrum of scales (Multiscale Requirements Traceability)
���� Analysis of the “Requirements – Performance – Architecture/Structure – Property” relationships and
interdependencies over the full spectrum of scales
���� Analyzing Multiscale Interactions among different elements at the same scale
���� Analyzing Multiscale Interactions among “System Architectural Elements” working at different scales:
from Macro To Nano.
To address these challenges is fundamental to develop Hierarchies of Multiscale Multilevel “Variable
Fidelity” Computational Models and Experimental/Testing/Sensing Models and Techniques and efficient
and reliable coupling schemes between codes/models based upon a range of physico mathematical
representations and principles.
New technological solutions (micro and nanotechnologies) and tighter and tighter requirements pose specific
challenges which change in a qualitative, not quantitative, way the approach to analysis, simulation and
design in the Hierarchical Multiscale Nano To Macro Systems Engineering scenario:
���� From a general point of view, the overall performance and operating behavior of systems will be more
and more determined by how multiscale and multi-physics phenomena interact in multi-component and
multimedia environments. The general trend towards miniaturization (micro and nano technologies)
makes it necessary for CAD/CAE/CAM systems to take into account, inside a fully integrated context,
an ever wider range of geometric and physical scales Separated single scale models have only a partial
validity if we like to predict in a correct way the overall behavior of a complex system under real
operating conditions, in particular when side effects, extreme and off-nominal conditions occur. Many
side effects stem from small-scale geometric details and media interactions that are not comprehensively
and adequately modeled by constitutive (engineering scale) equations. All that makes simulations using
classical engineering codes and separated sets of engineering and scientific codes, very difficult and of
limited reliability.
���� Off-nominal physical behaviour, such as fatigue, fracture, damage and corrosion a s well as off-nominal
dynamics of components, sub-systems and systems (or system of systems) in extreme operational
conditions (accidents included) occur at multiple space and time scales. The problem is classically
addressed by resorting to expensive physical prototypes for sub-systems and components, and setting up
lengthy, and not in all the cases really exhaustive and conclusive, testing activities. In most of the cases
Unified Multiscale and Multilevel variable fidelity hierarchies of models exist that describe behavior
across this huge scale range are applied. Instead, we have separate and non-communicating models at
the different scales and fidelity levels.
The development, using “Integrated Multiscale Science – Engineering Frameworks” of “Integrated
Hierarchies of Multiscale Multilevel Computational Models and Experimental, Testing and Sensing models/techniques” can be considered as a key target.
A multiscale system design approach calls for, but, at the same time, opens the way to new strategies for
complex systems monitoring and control. A combination of a new generation of multiscale sensors and
distributed computing systems, can lead to innovative monitoring and control schemes. New multiscale
sensors will be able to deliver not only "averaged" data and information, as in the past, about space and time
variations of key physical and technological variables (pressure, temperature, chemical composition,....) but
the detailed map of local values and rates at different levels of resolution and time and space scales. This
kind of information can be used to develop and validate off-line physical models no longer based on an
empirical and semi-empirical (averaged) knowledge but on a first principles understanding of the physical
reality. Highly detailed real-time models to control technological systems will grow out of this new level of
understanding and will run on an array of distributed computing systems.
Alessandro Formica – March 2012 All rights reserved
52
Complex Materials, in particular Nanostructured Materials, can be regarded as “Multiscale Hierarchical
Systems”. Biological and Bio – Inspired Materials are significant examples of this consideration.
Fig. 11 (from Raabe, Sachs, Romano, Acta Mater. (53, 2005, 4281) and Sachs, Fabritius, Raabe,
Journal of Structural Biology (161, 2008, 120) )
Fig. 12 (from Raabe, Sachs, Romano, Acta Mater. (53, 2005, 4281) and Sachs, Fabritius, Raabe,
Journal of Structural Biology (161, 2008, 120) )
Both the figures are related to the research carried out at the Max Planck Institut for Eisenforschung,
Germany and they show the multiscale hierarchical structure of chitin compounds.
Biological and Bio-Inspired Materials and Structures as “Multiscale Hierarchical
Systems”
Alessandro Formica – March 2012 All rights reserved
53
Research about the Multiscale Hierarchical Structure of Biological Materials and Advances in Multiscale
Computational Modeling and Multiscale Characterization techniques led to the birth of a new Field referred
to as “Materiomics”.
“Materiomics is an emerging field of science that provides a basis for multiscale material system
characterization, inspired in part by natural, for example, protein-based materials.
Materiomics is defined as the study of the material properties of natural and synthetic materials by
examining fundamental links between processes, structures and properties at multiple scales, from nano to
macro, by using systematic experimental, theoretical or computational methods. This term has been coined
in analogy to genomics, the study of an organism's entire genome. Similarly, materiomics refers to the study
of processes, structures and properties of materials from a fundamental, systematic perspective by
incorporating all relevant scales, from nano to macro, in the synthesis and function of materials and
structures. The integrated view of these interactions at all scales is referred to as a material's materiome.
The broader field of materiomics encompasses the study of a broad range of materials, which includes
biological materials and tissues, metals, ceramics and polymers. Among others, materiomics finds
applications in elucidating the biological role of materials in biology, for instance in the progression and
diagnosis or the treatment of diseases. It also includes the transfer of biological material principles in
biomimetic and bioinspired applications, and the study of interfaces between living and non-living systems”
“By incorporating concepts from structural engineering, materials science and biology our lab's research
has identified the core principles that link the fundamental atomistic-scale chemical structures to functional
scales by understanding how biological materials achieve superior mechanical properties through the
formation of hierarchical structures, via a merger of the concepts of structure and material. Our work has
demonstrated that the chemical composition of biology's construction materials plays a minor role in
achieving functional properties. Rather, the way components are connected at distinct scales defines what
material properties can be achieved, how they can be altered to meet functional requirements, and how they
fail in disease states….”.
[From the Laboratory for Atomistic and Molecular Mechanics, PI: Markus J. Buehler, Ph.D. Esther and
Harold E. Edgerton Associate Professor of Civil and Environmental Engineering, Center for Materials
Science and Engineering, Center for Computational Engineering, Massachusetts Institute of Technology
website]
Fig. 13 Multiscale Modeling and Multiscale Characterization Techniques (Markus J. Buehler)
Alessandro Formica – March 2012 All rights reserved
54
3.8.2 Multiscale Processing and Manufacturing The advent of Nano Engineering, Nano Manufacturing and 3D (Nano)Manufacturing technologies put the
bases for the development of Hierarchical Multiscale Manufacturing and Processing. The whole “Integrated
Multiscale Science – Engineering Framework” theoretical and methodological apparatus can be directly
applied in these fields.
It is interesting to highlight that the Processing area has been and it is one of the most sensitive and reactive
field to the multiscale challenge. For this reason we dedicate a special attention to this area illustrating some
of the applications of the multiscale strategy.
The “Strategic View of Multiscale” is, to a large extent, born in the Chemical Engineering field in the mid of
nineties (Multiscale as “Unifying Paradigm for Science and Engineering).. Even the concept of “Science –
Based Industry” was born the Chemical field.
6th World Congress of Chemical Engineering - Melbourne 2001 The Triplet "Processus-Product-Process" Engineering: The Future of
Chemical Engineering
Prof. Jean-Claude Charpentier – Key note Speaker Dept. Chem. Eng/CNRS Ecole Supérieure de Chimie Physique Electronique de Lyon
“Industry used to be king, now the customer is. In year 2001, to adapt the chemical engineering
approach to the needs of process industries and meet market demands, the offer is technological.
Being a key to survival in globalization of trade and competition, including needs and challenges, the
evolution of chemical engineering is received and its ability to cope with the problems encountered
by chemical and related process industries is appraised.
It appears that the necessary progress is coming via a multidisciplinary and time and length multi-scale approach that will allow us to satisfy both the market requirements for specific end-use properties and the environmental and social constraints of the industrial processes.
This will be obtained with breakthroughs in molecular modeling, scientific instrumentation and
powerful computational tools. This concerns four main objectives for engineers and researchers:
� to increase productivity and selectivity through intelligent operations, intensification and
multiscale control of processes;
� to design novel equipment based on scientific principles and new methods of production;
� to extend chemical engineering methodology to product-oriented engineering, i.e.
manufacturing end-use properties: the triplet "processus-product-process" engineering
� to implement multi-scale application of computational chemical engineering modeling and
simulation to real-life situations: from the molecular scale to the overall complex product scale
in order to analyze and optimize the supply chains. “
Alessandro Formica – March 2012 All rights reserved
55
Fig. 14 The Chemical Supply Chain
(from Prof Charpentier presentation given in year 2007 at the ESCAPE 17 Conference)
This figure synthetically describes the new approach envisaged by Prof. Charpentier in year 2001. This
strategy is now referred to as “Process Intensification”.
The limits of the classical approach to Chemical Engineering Design were well described, some years ago,
by the words of Dr. Irving G. Snyder Jr., director of process technology development at Dow Chemical. He
highlighted : "In the chemical engineering field we often know that A plus B makes C but, in many cases, we
do not know the transient intermediates that A and B go through in producing C; the reaction mechanisms of
all the by-product reactions; which of all the steps in the reaction mechanism are kinetically controlled,
which mass-transfer controlled, and which heat transfer controlled; if the reaction is homogeneous, what
takes place at every point in the reactor at every point in time; and if the reaction is heterogeneous, the
diffusion characteristics of raw materials to the catalyst surface or into the catalyst, as well as the reaction,
reaction mechanism, and by-product reactions within the catalyst, the diffusion characteristics of products
away from the catalyst, and the nature of heat transfer around the catalyst particle".
Intelligent operations and multiscale control of processes. The implementation of multiscale modeling jointly with the use of computer-based control schemes and
array of advanced multiscale sensors would allow to control events not only at the classical macro scale but
at the microscale level (detailed local temperature and composition control) and also at the
nanoscale/molecular levels. New multiscale models for predictive control and science-based technologies
will make possible a very accurate control of reaction conditions with respect to mixing, quenching, and
temperature profile. This science-based scheme significantly differs from the classical one that imposes
boundary conditions and lets a system operate under spontaneous reaction and transfer processes. The
multiscale control of processes solution would lead to an increased productivity and selectivity and opens the
way to a "smart chemical engineering" to meet, at the same time, tight economic and environmental
requirements.
Alessandro Formica – March 2012 All rights reserved
56
In systems in which process variables at different scales are highly coupled, controlling any single variable
will generally require the development of an integrated multiscale vision and the controller to use multiple
sensor inputs, and multiple actuator outputs. Innovative (multiscale) sensing and control strategies can be
derived from descriptions based on fundamental principles and simulations linking different phenomena at
a wide range of scales. There are clearly opportunities for new mathematical algorithmic research as well as
new sensor design and development. The full conceptual and methodological apparatus described in the
“Integrated Multiscale Science-Engineering Framework” can be applied to these issues and problems.
Specific Application fields:
� Analysis, modeling, design and characterization of processing and manufacturing units and design of
innovative processes and processing/manufacturing units and systems
� Analysis, modeling and characterization of structural and physico-chemical transformations which
characterize the full materials - manufacturing – assembly chain
� Analysis, modeling and characterization of interactions between processing and manufacturing units and
the environment for all the nominal and off-nominal operational conditions, including accidents. (this
issue is becoming more and more critical and conditioning)
Design of New Equipments Based on Scientific Knowledge and New Modes of Production The development of an integrated conceptual framework, which links basic scientific understanding to
engineering and technological issues, makes it possible to conceive innovative equipments based on first
principles. The design of new operating modes in chemical engineering and manufacturing can be linked to
a science-based approach. Innovative engineering applications of reversed flow, cyclic processes, unsteady
operations, extreme conditions, high-pressure technologies, and supercritical media, are largely dependent
on:
− The ability to couple scientific and engineering knowledge and models.
− The development of integrated science – engineering model based predictive control schemes
− The development and application of new micro and nano sensors and Integrated Sensor Processing
devices
− The development of new devices (MEMS and NEMS, micro-reactors, micro separators, and micro
analyzers) is, making possible accurate control of reaction conditions with respect to mixing, quenching,
and temperature profile.
Intelligent Processing of Materials (IPM) Intelligent Processing of Materials (IPM) is widely considered as the reference methodology for advanced
materials and manufacturing processes. IPM is closely linked to a multiscale understanding of materials
physics and biochemistry and its space-time transformations.
The strategy which underlies IPM is to model micro and meso (and more and more also nano, taking into
account the development of the Nanomanufacturing field) structural evolution during processing, sense
micro/nano structural changes in real time, and use a model-based control strategy to achieve the desired
micro, meso and nano structure in the finished product. A key objective is to develop advanced physical-
based models for relating the fundamental laws that govern the processes controlling the evolution of
microstructures and nanostructures and the resulting physico-chemical properties. Despite continuous
advances in control technologies in the materials and manufacturing processes, manufactured parts and
components contains several defects at a wide range of scales. These defects have a major impact on the
engineering properties of materials and structures due to increasingly tight design constraints and
requirements.
Multiscale Performance – Properties – Structure – Processing Maps are a key objective of the Integrated
Multiscale Science – Engineering Framework.
Alessandro Formica – March 2012 All rights reserved
57
Multiscale Sustainable Manufacturing Analysis
The following text is drawn from the Article: Multiscale Characterization of Automotive Surface Coating
Formation for Sustainable Manufacturing - Chinese Journal of Chemical Engineering, 16(3) 416-423 (2008)
- Jie XIAO, Jia LI, Cristina Piluso and Yinlun HUANG, Department of Chemical Engineering and Materials
Science, Wayne State University, Detroit, MI 48202, USA)
Note: The text illustrate the potentialities of Integrated Multiscale Analyses to asses performance of
Manufacturing Processes, their efficiency and environmental impact. Methodology employed holds a general
value and it can be applied to a wide range of Manufacturing issues and fields.
INTRODUCTION In automotive coating manufacturing, paints of different types are applied on a vehicle’s surface to generate
a basecoat and a clear coat (together called topcoat, 10-60 micrometers). Each layer is commonly developed
in two consecutive operational steps: paint spray and film curing. In operation, vehicle bodies are moved
steadily one by one by a conveyor through a spray booth and then an oven. In paint spray, the emulsified
paint particles (1-10 � micrometers in size) fly at a high speed (initial speed at 70-80 ms-1) from the spray
devices to the target vehicle panels . The particles missing the panels will be swept away by the air to the
drain through the grids on the floor. In production, material transfer efficiency, wet film topology, and
volatile organic compound (VOC) emissions are the main economic, quality, and environmental concerns.
The oven for film curing is usually divided into a number of zones to allow for the use of different heating
mechanisms (i.e., radiation from the oven walls and hot air convection) under different conditions. In the
end, the cured coating with the desired properties is developed on the vehicle surface; hopefully, it is defect
free (at any length scales). Besides, energy consumption and VOC emission are expected to be at a
minimum.
Paint spray and coating curing experience various technical challenges. First, high productivity requires
vehicle bodies to continuously move on a conveyor while being sprayed and baked. This makes it extremely
difficult to ensure coating thickness uniformity and a defect free finish. Second, almost all product
performance variables and a number of key process variables are not measurable during manufacturing. This
has forced the current quality control practice to rely only on post-process sampling, which causes various
quality problems, leads to inefficient energy and material utilization, and generates excessive amounts of
waste and pollutants. Third, which is probably the most challenging issue, is the knowledge disconnection
between macroscale bulk production, finer-scale material properties, and product behavior. In production,
operational settings (at the macroscopic level) are always adjusted based on experience; hence the true
manufacturing optimality cannot be realized in reality. In this article, a definition of the sustainable
manufacturing of paint-based automotive coatings is presented first. An integrated multiscale modeling and
simulation methodology is then introduced for characterizing paint spray and film curing. The resulting
multiscale system models can describe simultaneously product and process dynamic behaviors at the
different spatial and temporal scales, thus enabling comprehensive and deep analyses on the multiple-stage
coating manufacturing. Model-based simulation has revealed various usually inconceivable opportunities for
sustainable manufacturing. A paint material application for automotive surface coating manufacturing is
sustainable if the resulting products meet quality specifications (on appearance, durability, and styling), and
the manufacturing consumes the minimum amount of materials and energy, and has the minimum adverse
environmental impact (i.e., minimum VOC emission and paint-containing wastes).
Integrated multiscale paint spray model The following scheme shows a general model structure where two sets of models exist: (1) the macroscale
spray-booth air flow and electric field models, and (2) the mesoscale particle flying and collision models. In
addition, three following multiscale integration approaches are needed (see the rectangular box with one
corner cut):
(1) an approach for developing a (macro) coating topology from a static spray pattern, (2) an approach for
coupling continuous (macro) booth condition models with discrete (meso) particle models, and (3) an
approach for creating (meso) surface roughness from a static spray pattern.
Alessandro Formica – March 2012 All rights reserved
58
Integrated multiscale coating curing model The detailed curing model structure is shown in next scheme, where there is a macro-scale oven model set, a
mesoscale film physical behavior model set, and a micro-scale chemical behavior model (all in rectangular
box). In addition, three model integration approaches are needed to generate the multiple information
necessary for studying coating behavior and manufacturing performance (see those rectangular boxes with
one corner cut).
Alessandro Formica – March 2012 All rights reserved
59
Performance assessment models The economic, environmental, and social impacts of the coating manufacturing are assessed based on
production cost, productivity, energy and material use efficiencies, waste reduction performance, and
product quality (i.e., end users’ satisfaction). Figs. 2 and 3 include the performance assessment models (see
the rectangular boxes with rounded edges), each of which takes the information from the macro, meso,
and/or microscale models. For example, for oven curing, an energy model needs the information of energy
used for maintaining the required oven wall temperature, convection air temperature and velocity. An
environmental quality model can be used to calculate VOC emission due to solvent evaporation. The product
quality models are for quantifying macro-to-microscale coating physical/chemical properties, appearance,
and durability.
MULTISCALE INFORMATION UTILIZATION The models listed above can describe various types of phenomena occurring in product manufacturing at
different time/length scales. In this section, two major integration tasks are described to explain the
approaches for handling the information generated from the paint spray and coating curing model sets.
Coating topology generation A real paint application process is very complicated, where each vehicle body moves at a certain line speed
and the spray devices move in different ways (e.g., the spray bells above the vehicle roof move side by side
at a certain frequency and amplitude). For a given spray device, the number of paint particles from each bell
is on the order of 109 per second. Thus, for example, to paint a 1.3-1.8 m2 roof panel with three bells within
27 s, the total number of particles sprayed can be on the order of 1011. In model-based simulation, particle
collision and spray bell oscillation must be considered in order to have a better approximation of real spray
operations.
Due to these complexities, it is impractical to simulate particles on the order of 1011 directly for a multiple-
bell operation when studying the generation of a coating layer of about 70 m on a panel. On the other hand, it
should be reasonable to use the paint-spray (mesoscopic) information obtained from a static spray pattern
(i.e., the simulation based on the fixed locations of bells and receiving panels) repeatedly in a constructive
way to generate a coating layer (macroscopic) on the panel. This allows the use of a superposition approach
to add the static spray patterns in the pathway that a bell movement follows.
Macro (curing environment)–micro (network structure formation) coupling It is reasonable to assume that the heat generated/ consumed by cross-linking reactions can be neglected in
the initial study, which means that a coupling between the curing environment and the network structure
formation is only one-way. Note that film temperature dynamics in curing can be derived by a CFD solver.
By compromising solution resolution and computational expense, the total vehicle surface area is divided
into 20 zones in this work. In each zone, the average temperature is passed for one MC simulation. It is
assumed that in each zone, those homogeneously mixed chemical species (i.e., the polymers and crosslinkers
in this case) are reacted at the same film temperature. Using periodic boundary conditions, the crosslinked
network structure formed in each zone can be predicted by using only a few thousand molecules. In this
manner, a total of 20 MC simulations can generate a network structure in the coating that covers the
complete vehicle surface.
INTEGRATED ANALYSIS OF PROCESS AND PRODUCT PERFORMANCE The comprehensive analyses on paint spray and coating curing have revealed various opportunities for
achieving sustainable coating manufacturing. Part of the results is briefly presented below.
Paint spray system analysis Four cases with different downdraft settings are studied. It is found that downdraft mainly affects both booth
air quality and energy consumption. Increasing downdraft will decrease VOC emissions in the spray booth
but consume more energy. Also, three cases are investigated to assess the effect of different initial
distributions of particle sizes on the performance of product and process. It reveals quantitatively that smaller
sized particles can produce a better coating topology, but result in lower material efficiency and worse
environmental quality. Thus, the initial particle size distribution should be properly controlled to achieve a
better tradeoff between product performance and process performance
Alessandro Formica – March 2012 All rights reserved
60
Coating curing system analysis Three paint materials having different initial number average molecular weight (MW) are investigated. The
study has revealed that under the same curing condition, a decrease of the initial resin MW can lead to a
decrease of the final effective crosslink density. It means that a lower MW resin requires a higher curing
temperature or a longer curing time. Consequently, a decrease of the resin MW will consume more energy in
curing, although the amount of emissions can be reduced. This information will be valuable for identifying
the most desirable material formulation with well acceptable material application conditions.
CONCLUSIONS Polymeric coating manufacturing on vehicle surfaces is one of the most sophisticated and expensive steps in
automotive assembly. Most known studies on coating quality through paint spray and coating curing have
focused on the product’s macroscopic behavior, and for those lab-based studies, the operation simulated has
been limited to the use of many ideal operational settings. Thus, many important issues in production, such
as energy consumption, material use efficiency, and work-zone environmental quality, which are key
indicators of manufacturing sustainability, can hardly be addressed. This article has illustrated that all the
major process and product issues in paint spray and coating curing can be simultaneously addressed properly
by means of a multiscale system modeling and analysis approach. The integrated process and product
performance analysis introduced in this article illustrates its potential in generating important product and
process information that is critical for achieving sustainable manufacturing in automotive surface coating
applications.
Alessandro Formica – March 2012 All rights reserved
61
Multiscale Manufacturing of Three-Dimensional Polymer-Based Nanocomposite Structures - Louis Laberge Lebel and Daniel Therriault - École Polytechnique of Montreal, Canada
A multiscale approach Due to the several orders of magnitude involved in the fabrication of nanocomposite devices, an efficient
manufacturing technique must address the challenges at the nano-, micro- and macroscales. Figure shows
this multiscale concept for the creation of a 3D scaffold structure using a single-walled carbon nanotube
(SWNT) and a polymer nanocomposite. At the nanoscale, the dispersion of SWNTs should respond to the
targeted usage of the nano-reinforcement. Individualization of the nanoparticles, so the particles are in
contact with the matrix only, might be desirable when nanoscale properties are to be present in the final
product. Conversely, slight contact between the nanoparticles is needed when the percolation phenomena
through the entire domain are needed. In both cases, the interaction with the host polymer must be
controlled. At the microscale, the production of nanocomposite structures must allow a control over the
orientation of high aspect-ratio nanoparticles such as SWNTs. The arrangement of the nanocomposite
microscale structures in 3D permits the localization and orientation of the nano-reinforcement in a
macroscale product.
Nanoscale The nanoscale poses important manufacturing challenges such as the dispersion of the nanoparticles and
the close interaction of the nanoparticles with the host polymer matrix. In a dispersed state, the distance
between particles in the matrix must be controlled to achieve the targeted properties. For mechanical
reinforcement, every nanoparticle should be separated from each other to maximize the interface between
the matrix and the nano-reinforcement, thus enhancing the available area for stress transfer to the nano-
reinforcement. Aggregated nanoparticles are often responsible for underperformances
Microscale The arrangement of nanocomposite material in structures typically at the micron to several hundred
micron range offers several advantages. First, the material needed is reduced when the cost is an issue. In
addition, the microstructures manufacturing techniques allow a better control on the nanoparticle
disposition due to their microscale confinement. For example, high aspect ratio nanoparticles, such as
CNTs, can align themselves along the flow direction with the help of the high shear achievable in small-
scale manufacturing. Several techniques are used to produce nanocomposite at the microscale.
Microinjection molding (MIM) is an emerging method to manufacture microscale devices from polymer
nanocomposites
Macroscale Different techniques exist to manufacture nanocomposite products at the macroscale. A polymer
nanocomposite can be simply molded in a shape before hardening either by cooling or by the effect of
curing reaction. This relatively simple technique could find applications in traditional fiber reinforced
composites by modifying the matrix-dominated properties.
Conclusion The fabrication of high-performance nanocomposite materials and complex 3D structures must overcome
the different challenges at the nano-, micro-, and macroscale. Dispersion and interaction with the polymer
matrix are of paramount importance at the nanoscale. The microscale manufacturing techniques should
provide a control over the orientation of high aspect-ratio nanoparticles such as carbon nanotubes. Finally,
proper assembly technique of microstructures should be developed to create functional devices at the
macroscale. The manufacturing techniques explained in this chapter, i.e. the infiltration of 3D
microfluidic networks and UV-assisted direct writing, represent new avenues for the creation of 3D
reinforced micro- and macrostructures that could find applications in organic electronics, polymer-based
MEMS, sensors, tissue engineering scaffolds and aerospace structures.
Alessandro Formica – March 2012 All rights reserved
62
Environmental Engineering
The “Environmental Issue” has emerged as one of the critical challenges facing the industrial world. Efforts
to reduce the pollution generated by industrial activities rely in several cases on the "end-of-the-pipe"
control strategy. In this context, green chemistry means essentially pollution cleanup and waste management
technologies. A more radical and innovative approach, which can be defined "clean by design", entails the
re-design of chemical and manufacturing processes and units to eliminate at the root the formation of
pollutants and toxic by-products.
The “Integrated Multiscale Science – Engineering Framework” and “Integrated Multiscale Science –
Engineering Cyber-infrastructures” can be applied to the Environmental fields in the following areas:
���� Study of the multiscale spectrum of physical and biochemical phenomena/processes and the complex
pattern of relationships and interdependencies between them which rule the dynamics of “Environmental
and Climatological Systems”. This kind of analysis are instrumental to design multiscale monitoring
infrastructures and related data analysis schemes
���� Study of the multiscale (space and time) spectrum of physical and bio-chemical processes and the
complex pattern of relationships and interdependencies between them which underlie dynamics of civil,
infrastructural and industrial units and plants for the whole Life – Cycle and for nominal and off –
nominal conditions, accidents included. This is an important precondition to:
−−−− implement the "clean by design" approach aimed at eliminating at the root (atomic and molecular
level) the formation of pollutants and toxic by-products.
−−−− develop innovative technology and system engineering solutions.
���� Study of interaction dynamics of different industrial, civil, infrastructural systems and the environment
(System of Systems Analysis and Design)
���� Study of multiscale two - way interactions between industrial systems and the environment (humans
included) for nominal and extreme conditions (Safety, Security, Design of “Inherently Resilient and
Green Systems).
���� Designing new multiscale environmental monitoring systems able to integrate and interpret data
(Multiscale Knowledge Maps) from a wide range of sensors working over a full spectrum of space and
time scales.
Alessandro Formica – March 2012 All rights reserved
63
Integrated Hierarchical Multiscale Nano To Macro Monitoring Systems: From Space to Atoms and Molecules
An important goal of the Strategic Multiscale is the design of Integrated Multiscale Monitoring (and
Control) Systems which take full advantage of new Nano and Micro Sensor and Integrated Sensor &
Processing (ISP) technologies. The following Box is related to a National Institute of Standards and
Technologies (NIST) Report which highlight this need and challenge as a very critical issue: Greenhouse
Gases Multiscale Multiresolution Monitoring is becoming an increasingly important issue for the modern
Society in order to reliably assess the impact (footprint) of all the Human and Industrial activities over
Ecosystems.
The term “Integrated” means three Integration Streams:
� Multiscale Multiresolution Space Integration
� Multiscale Multiresolution Time Integration
� “Sensor Systems - Experimental Facilities” Integration and “Sensor Systems – Experimental Facilities –
Computational Centers” Integration. The growing complexity of the Networks of Physical and
Biochemical Phenomena and Processes to be Monitored and Analyzed calls for Integrated Data Analysis
and Interpretation Strategies which can be carried out by Multiscale Multidisciplinary Computational
Models acting as “Knowledge Integrators and Multipliers”. Multiscale Multidisciplinary Models
integrate and fuse data from a wide range of sources (Sensors and Laboratory Facilities) to turn a
“Tsunami” of Data into useful Knowledge. It should be taken into account that more Data does not
necessarily means more Information and Knowledge.
Greenhouse gases: The measurement challenge
The continuing increase in the level of carbon dioxide and other "greenhouse gases" in the Earth's
atmosphere has been identified as a cause for serious concern because it may radically accelerate
changes in the Earth's climate. Developing an effective strategy for managing the planet's greenhouse
gases is complicated by the many and varied sources of such gases, some natural, some man-made, as
well as the mechanisms that capture and "sequester" the gases. A new report sponsored by the National
Institute of Standards and Technology (NIST) focuses on one of the key challenges: defining and
developing the technology needed to better quantify greenhouse gas emissions. The new report,
"Advancing Technologies and Strategies for Greenhouse Gas Emissions Quantification," is the result of
a special workshop in the NIST Foundations for Innovation series, convened in June 2010, to bring
together greenhouse gas experts from government, industry , academia and the scientific community to
address the technology and measurement science challenges in monitoring greenhouse gases. A wide
variety of techniques are used for measuring greenhouse gas emissions and, to a lesser extent, the
effectiveness of "sinks"—things like the ocean and forests that absorb greenhouse gases and sequester
the carbon.
The problem is that developing an effective global strategy for managing greenhouse gases requires
a breadth of measurement technologies and standards covering not only complex chemical and
physical phenomena, but also huge differences in scale. These range from point sources at electric
power plants to distributed sources, such as large agricultural and ranching concerns, to large -scale
sinks such as forests and seas.
Satellite - based systems, useful for atmospheric monitoring, must be reconciled with ground-based
measurements. Reliable, accepted international standards are necessary so governments can compare
data with confidence, requiring a lot of individual links to forge an open and verifiable chain of
measurement results accepted by all.
Alessandro Formica – March 2012 All rights reserved
64
4. Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-TPPD) Framework
4.1 Overview and Architecture
Running programs in Europe, US and Japan are putting the bases for the definition and implementation of a
new generation of “Integrated Product and Process Development” (IPPD) Frameworks which can be termed:
“Integrated Multiscale Science – Engineering Technology, Product and Process Development” (IMSE-
TPPD) Frameworks. We add the term “Technology” because, in the context of “Science – Engineering
Integration”, we would like to stress links between science, technology development, engineering and
manufacturing.
An interesting example of this strategic direction has been the “Integrated Computational Materials
Engineering” (ICME) Initiative promoted by US National Academy of Sciences and TMS. ICME is
supported by Universities, Research Centers and Industry. In Europe several EURATOM programs are
pursuing similar objectives. Outside, the Materials and Processing Area, the EU “Virtual Physiological
Human” is a noteworthy initiative which foresees the development of a large scale and scope “Integrated
Multiscale Framework” for the Biomedical field.
Materials and Nanostructured Devices and Systems are, more and more, inherently, “Multiscale Systems”,
i.e. systems organized following a hierarchical strategy where structures at the different scales interact in a
synergistic way to give an extended spectrum of functionalities and performance. The development of new
Multiscale Frameworks can give the birth of a new field: Multiscale Technology, Engineering and
Processing/Manufacturing..
This issue is fundamental to meet with an extended range of requirements (efficiency, safety and
environmental compliance). A very interesting example of this strategic approach has been the EU NMP
(Sixth Framework) Integrated Multiscale Process Units Locally Structured Elements (IMPULSE 2005 –
2009) Program. IMPULSE is Europe’s flagship R&D initiative for radical innovation in chemical
production technologies. Created in the framework of the SUSTECH program of CEFIC, IMPULSE was a
specifically targeted program aimed at creating a totally new strategy for the design and operation of
production systems for the chemical (and related) process industries.
IMPULSE aimed at developing a new approach to competitive and eco-efficient chemicals production: Structured Multiscale Design.
The multiscale design approach of IMPULSE provides intensification locally only in those parts of a
process and on the time and length scale where it is truly needed and can produce the greatest benefit.
IMPULSE aimed at the integration of innovative process equipment such as microreactors, compact heat
exchangers, thin-film devices and other micro and/or meso-structured components, to attain radical
performance enhancement for whole process systems in chemical and pharmaceutical production.
Alessandro Formica – March 2012 All rights reserved
65
“Multiscale Multidisciplinary Science – Engineering Cyber Knowledge Integrator and
Multiplier Extended Enterprise”.
The classical “Integrated Product and Process Development (IPPD)“ Framework is linked to the “Extended
Enterprise” concept. The new IMSE-TPPD Framework, proposed in this document, can be related to a new
industrial, economic and societal scenario which can be called “Multiscale Multidisciplinary Science –
Engineering Cyber Extended Enterprise”.
− Multiscale Multidisciplinary Science-Engineering means that “Integrated Multiscale Science-
Engineering Frameworks” shape R&D and Engineering, Planning, Operation and Management
activities and that Civil, Industrial, Environmental and Societal Infrastructures are organized
applying Integrated Multiscale Hierarchical Nano To Macro Engineering Architectures
− Cyber Knowledge Integrator and Multiplier means that the “Multiscale Science-Based Enterprise”
concept is implemented over “Multiscale Science – Engineering Knowledge Integrators and
Multipliers Cyberinfrastructural Environments (on line connection among Computational,
Experimental, Testing, Sensing and Theoretical Centers and Facilities)”
− Extended Enterprise means that the IMSE-TPPD Framework shape a new “University – Research
– Industry – Society Cooperative Environment”. This new kind of “Cooperation Contexts” enables
researchers, designers, public and private managers and politicians to synthesize a wide spectrum of
different resources, methods and operational schemes and define comprehensive strategies to meet
common objectives and goals. Multiscale Frameworks can be instrumental to improve correlation
between operational requirements, engineering requirements and technological and scientific
advances promoting accelerating in such a way technological and engineering innovation
Fig. 15 “Multiscale Multidisciplinary Science – Engineering Cyber Extended Enterprise”. (from the
Presentation : Opportunities and Barrier Issues in Carbon Nanocomposites - R. Byron Pipes, NAE, IVA
Goodyear Endowed Professor - University of Akron - National Science Foundation Composites Workshop -
June 9-10, 2004)
Alessandro Formica – March 2012 All rights reserved
66
The IMSE-TPPD Framework
� Enables a long term, systematic, organic, and effective involvement of the scientific community inside
real operational innovation technology programs with specific tasks, responsibilities, and profits
� Compels industry to redefine the whole technology development and engineering process taking full and
systematic advantage of science knowledge and progress
The “Multiscale Multidisciplinary Science – Engineering Cyber Knowledge Integrator and Multiplier Extended Enterprise” concept can offer scientists, researchers, public and private managers and politicians
a “unified context” to better understand the complex pattern of relationships and interdependencies among
the wide range of different aspects and issues which characterize the research and technological innovation
world and, accordingly, synthesize widely scattered efforts and forge more effective “unified strategies” to
deal with problems of increasing complexities.
The IMSE-TPPD Framework (this one described in this document is only a first proposal not the ultimate
solution) allows to take into account, inside a unified context, all the phenomena, from atomic and
molecular scales to the engineering and operational ones which rule materials design, processing and
application including life-cycle and sustainability issues. Integration of atomic/molecular scales with the
micro, meso and macro worlds is a fundamental challenge for a wide industrial application of the most
innovative nanotechnologies in the materials, engineering and processing areas. Multiscale Collaborative
Frameworks realize a real “two-way” science-engineering integration from an industrial point of view and
put the bases to create a new “Multiscale Quantum - based Engineering World”.
The IMSE-TPPD Framework deals with the following areas:
���� Multiscale Research and Technology Development Processes
���� Multiscale Engineering Analysis and Design: the design of “Inherently” Hierarchical Multiscale
Technological and Engineering Devices, Components and Systems is a key target
– Mission and Scenario Analysis
– (Multiscale) Requirements Definition
– Design (Conceptual, Preliminary and Detailed Design)
– System/subsystem model or prototype demonstration in a relevant environment
– System prototype demonstration in an operational environment
– Full System completed and “qualified” through test and demonstration
���� Multiscale System Engineering
���� Multiscale Monitoring and Control
���� Multiscale Life – Cycle Engineering
���� Multiscale Safety and Security Engineering
���� Multiscale Manufacturing and Processing
���� Multiscale Environmental R&D and Engineering (Green Engineering and Analysis of the Impact of
Product and Processes on the Environment for nominal and off nominal operating conditions, accidents
included)
� Multiscale d Testing
���� Multiscale Innovative Technology and Systems Development Planning
Alessandro Formica – March 2012 All rights reserved
67
Integrated Multiscale Science – Engineering Technology, Product and Process Development
(IMSE-TPPD) Framework Architecture
Key Architectural Elements are:
���� Computer Aided R&D and Engineering (CARDE) Framework which implements the Integrated
Multiscale Science – Engineering Framework (Paragraph 4.2)
� Innovative Technology and System Development Analysis and Planning Framework or “Virtual
Multi Space and Time Scale R&D and Engineering Machine” (Paragraph 4.3)
� Multiscale “Knowledge Integrator and Multiplier” Cyberinfrastructural [Computing, Information
and Communication (CIC)] Environment (Paragraph 4.4)
Alessandro Formica – March 2012 All rights reserved
68
4.2 Computer Aided R&D and Engineering (CARDE) Framework
The Computer Aided R&D and Engineering (CARDE) Framework(CAD and CAE Frameworks next
generation) implements the full spectrum of concepts and methodologies described in the Chapter 3
“Integrated Multiscale Science Engineering Framework”. Key Elements:
� Multiscale Science – Engineering Data, Information and Knowledge Analysis and Management
System
� Multiscale Multiphysics Computer Aided Design (CAD) System (based upon Architectural and
Functional Maps)
� R&D and Engineering Process Strategy Definition System (Designing the R&D and Engineering Process)
� Methodologically Integrated Multiscale Science – Engineering Analysis Environments
� Application Specific Modules (Life – Cycle, Safety & Security, Manufacturing and Processing,
Environmental Impact,…)
� Multiscale Visualization Modules
Software Environments run over Multiscale “Knowledge Integrator and Multiplier”
Cyberinfrastructural [Computing, Information and Communication (CIC)] Environments
Alessandro Formica – March 2012 All rights reserved
69
4.3 Innovative Technology and System Development Analysis and Planning Framework or “Virtual Multi Space and Time Scale R&D and Engineering Machine”
Fig. 16 NASA Technology Readiness Level (TRL) Scale. This scale describes the several phases of an
“Innovative Technology and Systems Development Process”.
The previous representation has a general value. It can be applied to any technological and engineering
sector
Any R&D and Engineering Process can be seen as a “Multi Space and Time Scale Process” and it can be
modeled and simulated by Multiscale Computational Frameworks. The fundamental goals of new
planning processes based upon the “Integrated Multiscale Science-Engineering Framework” are to :
� improve effectiveness and reliability of alternative “System Architectures” selection process by
identifying in a more comprehensive and reliable way problems linked to interactions between the
“System” and the Operational Environment and among a wide spectrum of subsystems, components and
devices which constitute the overall “System Architecture” which is, increasingly an inherent Multiscale
Multilevel Architecture..
� improve evaluation of the impact of advances in fundamental scientific knowledge over the development
of innovative technology solutions and systems architectures (Bottom – Up approach)
� improve assessment of how “System Requirements” propagate down the TRL chain following a “Top
Down” approach In this prospect, the definition of performance levels and operational requirements for
the system, enables, following Multiscale Multilevel analysis schemes, the identification of what are the
needed features and performance of sub-systems, components, and devices and their relationships.
Alessandro Formica – March 2012 All rights reserved
70
� improve assessment of the Science-Engineering Information/Knowledge needed to accomplish each step
(from TRL 1 to TRL 9) and to transition in a successful way from a step to the next one
� improve assessments of what information can be get by using existing analytical theories, computational
models and experimental & testing techniques, and what not
� improve identification of the needed development paths in analytical theories, computational models,
and experimental & testing techniques and what mix of resources (theories, modeling & simulation,
experimental & testing techniques) are needed to develop the envisaged sub-system or component.
� Improve Organization of Information/Knowledge inside each TRL Phase in such a way as to make it
directly and comprehensively usable and applicable in the next one along the scale.
Two development lines can be followed:
a) Bottom – Up Approach: the starting point is progress in innovative technologies and devices and
components (advances can be real or hypothesized)
b) Top – Down Approach: more ambitious operational and performance requirements to be met represent
the starting point
To accomplish the previously quoted tasks, we can use the full methodological and theoretical apparatus of
the “Integrated Multiscale Science-Engineering Framework” to build a “Virtual Multiscale Space-Time
Machine” or “Virtual R&D and Engineering Systems Development Analysis and Planning Framework”. The term “Virtual” means that in both the cases:
� A model of the planned system and the operational environment (and of the hierarchy of sub-systems,
components, devices and materials) is developed using available data, information and knowledge that
are being organized using the “Multiscale Science – Engineering Data, Information and Knowledge
Management System”. Information are being progressively updated and improved as we transition from
one phase to another one in an incremental way. As data and information, along the TRL chain, become
available from computation, experimentation, testing and sensing, they are inserted into the models by
taking full advantage of the KIM concept and method.
� A model of the R&D and Engineering Process (Designing the R&D and Engineering Process) is built.
The Model is progressively updated. At the starting date, it is possible to use simplified models
“Virtual Analyses” can proceed following two strategies:
� “top-down” (from a complex structure and operational environment to its constituents) : requirements
are set for a system at a certain scale (access scale) and the analysis is performed for the hypothetic
system (several hypothesis are taken into account and modeled) considering the scales which are under
the one for which requirements (and accordingly levels of performance) are being set
� “bottom-up” (from fundamentals to a complex structure and its operational environment): hypotheses
are formulated about the architecture of the system for scales over the initial scale taken into account.
Models are developed. The analysis proceeds by evaluating how and to what extent performance and
properties calculated and or measured at a certain scale (nano scale, for instance) influence dynamics and
architecture/structure at the scale immediately higher (micro scale, for instance) and so on. This kind of
approach is instrumental to build technology roadmaps and innovative technology development plans
The approaches can be interactively and iteratively combined. Several different scenarios can be taken into
account and evaluated (What if Strategy). Sensitivity Analyses can also be carried out.
Alessandro Formica – March 2012 All rights reserved
71
In this “Virtual Context”, “Multiscale Science-Engineering Information-driven Strategy” is critical to
identify what kind of Information at what level of accuracy and fidelity should be needed to characterize the
complex technological dynamics of the set of subsystems, components and devices and their interactions.
Fig. 17 (from NASA) clearly illustrates the concept of Virtual Multiscale Machine in the Aerospace field.
Fig. 18 (from Georgia Institute of Technology) well describes the Multiscale Technology and Engineering
Development and Integration Scenario for Complex Systems: From Atoms to Assembly, Product , Industrial
System and Ecosystems
Alessandro Formica – March 2012 All rights reserved
72
It is important to highlight that even if we are not able to develop highly detailed multiscale models, the new
proposed Framework can represent a valuable tool. Simplified models can allow researchers and engineers
to jointly perform simple, but still meaningful analyses to identify critical items in the Innovative
Technology Development process and shape more effective cooperation between scientists and engineers.
A critical issue is that present innovative technology development strategies are, in several cases, not fully
able to assess the (multiscale science-engineering) information needed to develop, validate, and integrate
key technologies in more complex systems. Present innovative technology development strategies are not
fully “information-driven” or, to better say, Multiscale Science-Engineering Information-driven.
The “Science-Engineering Information Space” and the Modeling & Simulation as “Knowledge Integrators
and Multipliers” concepts and methods allow us to define, besides the classical Technology Roadmaps, new
Integrated Multiscale Science-Engineering Information-Driven Theoretical and Methodological (computational, experimental, testing and sensing) Roadmaps which enable researchers, designers and
managers to jointly identify critical scientific and engineering resources needed to develop innovative
technological systems and shape more effective university-research-industry cooperative scenarios inside a
unified and coherent conceptual context.
Roadmaps of computational methodologies are being already drafted, but they are not fully “Information-
Driven” and, normally, not comprehensively integrated with experimental, testing and sensing roadmaps.
Computational and experimental & testing roadmaps are drafted separately without well defined links and
interdependencies.
Said in other words, roadmaps do not comprehensively identify and specify what information at what level
of accuracy and fidelity is needed to reach new engineering and technological achievements and what
information at what level of accuracy and fidelity we can get from the new outlined models and methods. Or,
at least that is accomplished only or mainly at a qualitative level.
The “Strategic Value of the “Integrated Multiscale Science-Engineering Framework” is that this kind of
approach enables a more in-depth and timely identification of the “Scientific and Engineering Critical
Issues and Domains and their relationships and interdependencies” in such a way as to allow for the
definition of timely integrated science-based (or science-engineering) strategies.
Alessandro Formica – March 2012 All rights reserved
73
4.4 Multiscale Science - Engineering Knowledge Integrator and Multiplier Computing, Information and Communication (CIC) Infrastructural Framework
New Cyberinfrastructures (GRIDS) (or Computing, Information and Communication Infrastructures)
represent the “Infrastructural and Technological Layer” for the Integrated Multiscale Science – Engineering
Technology, Product and Process Development (IMSE-TPPD) Frameworks.
� Multiscale Science – Engineering means that:
� the “Integrated Multiscale Science – Engineering Framework” can be used to design the Architecture of
Cyberinfrastructures: what kind of resources are interconnected with specific functionalities and
performance) conceived for specific Research, Environmental, Engineering, Manufacturing, Monitoring
and Control purposes.
� Methodologically Integrated Multiscale Science – Engineering Strategies (Paragraph 3.6) shape
Cyberinfrastructure Operational Modes. Specific Resources and Services can be activated, tailored and
integrated in an adaptive way for specific Tasks. This new kind of Cyber Infrastructure links together
the full spectrum of Computational, Experimental, Theoretical, Testing Centers and Networks of Earth
and Space based sensor systems according to Unified Multiscale Science - Engineering Strategies.
Fig. 19 (from US Department of Energy) A possible representation of Multiscale Multidisciplinary
Science – Engineering Cyberinfrastructure.
Alessandro Formica – March 2012 All rights reserved
74
� Knowledge Integrator and Multiplier means that
� the “Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers and Unifying
Paradigm for Scientific and Engineering Methodologies (analytical, experimental, testing and sensing)
and related Knowledge Domains” can give the birth to a New Generation of Computational Centers with
extended functionalities and capabilities referred to as “Multiscale Computational Science - Engineering Knowledge Integrator and Multiplier Centres”. These Centres would be based upon
the new central concept of “Multiscale Multidisciplinary Modeling and Simulation as Knowledge
Integrators and Multipliers” and “Unifying Paradigm” for the full spectrum of Scientific and Engineering
Knowledge Domains and (analytical, experimental, testing, sensing) Methodologies. MethodologicallyA
“two – way” partnership among the new envisaged Computational Centers and Experimental, Testing
and Sensing Centers, Systems and Facilities is a distinguishing feature of this new vision. Furthermore,
Computational Centers, following the “Knowledge Integrators and Multipliers” view will become a key
node and catalyst of multiple interaction patterns between the Experimental, Testing and Sensing
Worlds. Significant technological advances allow to design and implement remote control techniques for
experimental, testing and sensing systems. Accordingly, new Computational Centers can easily interact
with extended Virtual Distributed Environments which integrate a wide spectrum of equipments and
facilities allowing a network of multiple cooperations. New previously illustrated concepts, methods and
frameworks lead to a new set of Functionalities for the Centres:
a) Integrated Environments for jointly (cooperating with Analytical, Experimental, Testing and
Sensing, Teams) “Designing” Integrated Computational and Experimental, Testing and Sensing
Frameworks and Strategies
b) Integrated Environments for the construction of Multiscale Multidisciplinary Science – Engineering
“Knowledge Domains” which turn Data coming from a full spectrum of scientific and engineering
sources (data bases, computations, analytical formulations, experimentation, testing and sensing)
into Multiscale Multidisciplinary Knowledge Domains
c) Integrated Environments for the Development and Validation of advanced Multiscale Computational
Models and Frameworks
A new level of Integration is made possible by the KIM Vision Integration develops along the following
lines:
� Scale integration (Multiscale Science and Engineering Integration) involving teams inside University,
Research Centres, Industry, dealing with research and engineering issues at different scales and
resolution levels. The design and implementation of Multiscale Science – Engineering
Cyberinfrastructures or GRIDs can give a real boost to the development of Multiscale Multiresolution
Experimental, Testing and Sensing technologies, procedures and strategies.
� Data, Information and Knowledge Integration: integration of data, information and knowledge from a
full spectrum of sources: theory, experimentation, testing and sensing) to build Multiscale Multiphysics
Science – Engineering Maps
� Teams and Methodologies lntegration: teams employing the full spectrum of methodologies (theory,
computation, experimentation/testing/sensing) for a wide range of disciplines (mathematics, physics,
chemistry, biology, electronics,..) work together to apply unified strategies for specific Tasks.
New Computational Centers become “Portals” to a Composite Multidisciplinary Multiscale Science –
Engineering World
Alessandro Formica – March 2012 All rights reserved
75
Integration of Supercomputing and Experimental Resources
Blue Collar Computing™ - Polymer Portal Collaboration Program:
Blue Collar Computing™ (BCC) is a collaborative program sponsored by the Ohio Supercomputer
Center (OSC) to help industry gain easy and affordable access to advanced computing technologies.
With support from the Ohio Board of Regents, OSC launched Blue Collar Computing™ in 2004. The
Ohio Supercomputer Center built an Integrated Virtual Environment called “Polymer Portal
Collaboration” which enables researchers and designers to effectively use and integrate a wide
spectrum of computational, experimental/testing and data repository resources. It could be a valuable
example of a New Generation of Supercomputing Centers. The following figures illustrate Polymer
Portal functionalities:
Alessandro Formica – March 2012 All rights reserved
76
About the Author
- Alessandro Formica, born in Milano (Italy) 3/20/51
- University Education : Nuclear Engineering at Polytechnic of Milan.
- Office : Via Piazzi, 41 – 10129 Turin, Italy
- Home : Via Sismondi, 4 – 20133 Milan, Italy
� Professional Skills - Alessandro Formica has more than thirty years of experience in the following
fields:
− Analysis, Design and Management of Computer-Aided Engineering, Computing, Information and
Communication, Modeling and Simulation, R&D and Engineering Projects and Initiatives (Aerospace
and Defense, Chemical and Environmental Engineering, Materials, High Performance Computing,….)
in the European and International scenario.
− Design and Management of European and International Cooperations
− Design and Management of European and International Events (Conferences and Workshops)
− Scenario, Marketing and Development Trend Analyses
− Design of Large Scale Projects and related Innovative Visions
� Professional Experiences:
− ARS S.p.A. (ENI Group R&D and Engineering Company), Director of Advanced Projects;
− Engineering Systems International, Head of Italian Branch;
− Singapore Government Industrial Group, Consultant;
− RCI Ltd. [US based International Consortium, operating in the Modeling & Simulation and High
Performance Computing areas], European Scientific Director, Director of Business Development and
Strategic Initiatives;
− RCI Consulting Company, European Scientific Director
− Executive Office of US President, Consultant;
− HPC companies (Cray, Convex, Gould), Consultant;
− European Space Agency, Consultant;
− Alenia Space, Consultant;
− Swiss Center for Scientific Computing (CSCS), Consultant;
− Computer Sciences Corporation, Consultant
− Alenia Aeronautica, Consultant;
− Torino Wireless, Large Projects Direction Consultant
− Polytechnic of Milano, Consultant
− Polytechnic of Turin, Consultant,
− Polytechnic of Turin School of Doctorate Lecturer for Multiscale Science – Engineering Integration;
− EUMAT (European Union Materials Technology) Platform Working Group 2 Modeling and Simulation
member;
− Polimeri Europa (ENI Group Chemical Company), Consultant;
− Nanoshare consultant (Nanoshare is a new company promoted by University and Research Italian
Ministry and involving Rome University “Tor Vergata” and people from Rome University “La
Sapienza”).
The “Strategic Multiscale” White Book synthesizes several years of studies and consulting activities by the
author in the field of Multiscale Science – Engineering integration and its application to Research,
Technology Development and Engineering. Studies on Multiscale started at the beginning of the nineties
when Alessandro Formica held the position of RCI Ltd (US based HPC International Consortium) European
Scientific Director. In the Report “Fundamental R&D Trends in Academia and Research Centres and Their
Integration into Industrial Engineering” (September 2000), drafted for European Space Agency (ESA), a first
version of an “Integrated Multiscale Science - Engineering Framework” was outlined and its impact on R&D
and Engineering analyzed.
Alessandro Formica – March 2012 All rights reserved
77
The White Book “Multiscale Science – Engineering Integration – A New Frontier for Aeronautics, Space
and Defense (May 2003) sponsored by Italian Association of Aeronautics and Astronautics (AIDAA)
introduced the concept of “Strategic Multiscale” and a more refined version of the related Integrated
Framework.. A Framework version specifically conceived for Industrial Applications: “Integrated
Multiscale Science – Based Technology, Product and Process Development” was drafted in the context of
the consulting cooperation with Alenia Aeronautica and Finmeccanica Group (November 2006). Multiscale
Analyses and Studies were also carried out on behalf of Polytechnic of Milan and Turin and in cooperation
with University of Rome “La Sapienza” and University of Rome “Tor Vergata”.
This White Book synthesizes several years of studies and consulting activities by the author in the field of
Multiscale Science – Engineering integration and its application to Research, Technology Development and
Engineering. Studies on Multiscale started at the beginning of the nineties when Alessandro Formica held the
position of RCI Ltd (US based HPC International Consortium) European Scientific Director. In the Report
“Fundamental R&D Trends in Academia and Research Centres and Their Integration into Industrial
Engineering” (September 2000), drafted for European Space Agency (ESA), a first version of an “Integrated
Multiscale Science - Engineering Framework” was outlined and its impact on R&D and Engineering
analyzed. The White Book “Multiscale Science – Engineering Integration – A New Frontier for Aeronautics,
Space and Defense (May 2003) sponsored by Italian Association of Aeronautics and Astronautics (AIDAA)
introduced the concept of “Strategic Multiscale” and a more refined version of the related Integrated
Framework.. A Framework version specifically conceived for Industrial Applications: “Integrated
Multiscale Science –Based Technology, Product and Process Development” was drafted in the context of the
consulting cooperation with Alenia Aeronautica and Finmeccanica Group (November 2006). Multiscale
Analyses and Studies were also carried out on behalf of Polytechnic of Milan and Turin and in cooperation
with University of Rome “La Sapienza” and University of Rome “Tor Vergata”.
Alessandro Formica – March 2012 All rights reserved
78
Contacts
Alessandro Formica
Via Piazzi, 41
10129 Torino
Italy
Phone. +39 338 71 52 564 +39 342 1350 390
E-mail: [email protected] and [email protected]