strategic multi scale october 2010
TRANSCRIPT
VisionVisionVisionVision PaperPaperPaperPaper
“Strategic Multiscale:
A New Frontier for
R&D and Engineering”
Alessandro Formica
October 2010
All rights reserved
Alessandro Formica – October 2010 All rights reserved
2
TABLE OF CONTENTS
1. Introduction pag. 3
2. Strategic Multiscale Framework pag. 4
2.1 R&D and Engineering Scenario: From Computational To Strategic Multiscale pag. 4
2.2 Strategic Multiscale Framework Architecture pag. 8
2.3 Strategic Multiscale Framework Goals pag. 9
3. Integrated Multiscale Science - Engineering Framework pag. 11
3.1 Architecture pag. 11
3.2 Multiscale Data, Information and Knowledge Analysis and Management System pag. 13
3.3 Multiscale Science – Engineering Information Space pag. 19
3.4 Modeling & Simulation as Knowledge Integrators and Multipliers pag. 23 3.4.1 The New Computational Modeling Vision pag. 23
3.4.2 Extension of the Multiscale Approach to the Experimental, Testing and Sensing Worlds pag. 27
3.4.3 Methodologically Integrated Multiscale Science – Engineering Strategies pag. 30
3.4.4 Multiscale Knowledge – Based Virtual Prototyping and Testing pag. 32
3.5 Designing the R&D and Engineering Process pag. 33 3.5.1 The Information - Driven Concept pag. 33
3.5.2 The R&D and Engineering Process Design Management System pag. 35
3.5.3 Multiscale R&D and Engineering Information - Driven Strategies pag. 38
3.6 Integrated Multiscale Science – Engineering Analysis Strategies pag. 39
4. Integrated Multiscale Science – Engineering Technology, Product and Process pag. 43 Development (IMSE-TPPD)Framework 4.1 Overview and Architecture pag. 43
4.2 Multiscale Systems Engineering pag. 47
4.3 Multiscale Process Engineering pag. 49
4.4 Multiscale Environmental, Safety and Extreme Engineering pag. 51
4.5 Innovative Technology and Systems Development Planning pag. 54
5. Integrated Multiscale R&D and Engineering Infrastructural Framework pag. 57
Biography pag. 60
Contacts pag. 61
Alessandro Formica – October 2010 All rights reserved
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1. Introduction
This document intends to be a first attempt to analyze the “structural” impact of the “Strategic Multiscale”
view on the R&D and engineering organization and the way complex innovative technology
developments and engineering solutions are planned, managed and implemented.
Relationships between science and engineering, basic and applied research, Innovation and industry are
deeply changing, accordingly, a new language and theoretical framework to understand and manage this
evolving scenario and drive technology innovation well into the 21st century, is a reasonable step forward.
Dramatic advances in Computing Information and Communication (CIC) technologies are reshaping
research, Innovation and industry. However, significant methodological advances are needed to take full
advantage of this technological “Revolution” and effectively cope with educational, industrial, economic,
environmental and societal challenges.
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 Science and Engineering and Science-
Engineering Integration.
The term “Strategic” means that multiscale will be the catalyst to deeply change R&D and Engineering and
Education organization, structure and strategies.
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 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 and 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:
� The “Integrated Multiscale Science – Engineering Framework” which represents the theoretical,
conceptual and methodological basis
� The “Integrated Multiscale Science – Engineering Technology, Product and Process Development
(IMSE-TPPD) Framework” which is constituted by a set of Software Environments that implement
theories, methods and concepts described in the previously quoted Framework
� The “Integrated Multiscale R&D and Engineering Infrastructural Frameworks”
Application of “Strategic Multiscale” to the Education, Information and Communication areas is described
by a Vision Paper enclosed to this Document: Multiscale Science – Based Language”.
Alessandro Formica – October 2010 All rights reserved
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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
integration of science and engineering. This vision was highlighted, at the 5th 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 introduced the concept of “Strategic
Multiscale” and he described the related Integrated Framework.
Strategic Multiscale is the theoretical and methodological basis to reshape R&D and Engineering
organization, structure and strategies integrating 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 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 – October 2010 All rights reserved
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The drivers for a new vision of Multi scale come from some specific features which characterize modern
R&D and Engineering 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
c) Growing complexity of technological and 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 Scientific knowledge is increasingly at the root of new technology developments in key fields : materials,
materials processing, electronics, optics, combustion,…..Innovative technologies are directly based on
scientific principles and phenomena but, unified scientific and engineering environments are in the early
development phase. 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 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 to understand, predict and control 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).
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 (or Architectural) 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 – October 2010 All rights reserved
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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 to reliably predict systems behavior, select alternative technological
and engineering solutions, validate computational and experimental, testing and sensing methods/techniques,
define 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 you are not able 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 hypotheses computational models are formulated upon as well as
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.
The Figure is drawn 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 – October 2010 All rights reserved
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It is possible to identify three fundamental stages in the development of Multiscale and Science –
Engineering Integration :
a) Multiscale Computational tools and methods to address specific R&D and Engineering issues. It is the
basic development stage. There is an increasing activity to improving existing multiscale methods and
develop new schemes and strategies (adaptive, concurrent, hierarchical,….)
b) Integrated Computational Multiscale Environments 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) “ Strategic Multiscale Science – Engineering Frameworks”, 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 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 Modeling & Simulation as “Knowledge Integrators and Multipliers” and “Unifying
Paradigm” for Scientific and Engineering Methodologies. In this perspective “Modeling & Simulation”
integrate the full spectrum of science and engineering methodological approaches and knowledge
environments.
− The “Science-Engineering Information Space” concept to integrate 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 (Designing the R&D and Engineering Design
Process)
− New Multiscale Science – Engineering Data, Information and Knowledge Management Systems based
upon the 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. Not at all. The author of this White Book is convinced that, for the progress of
Science and Engineering, is, and, it will be, of fundamental importance that a significant part of basic and
applied research activities are to be carried out for the “sake of knowledge” outside of rigid guiding schemes.
The fusion between a “science-driven engineering” and an “engineering-driven science” approach means a
reasonable balance between “directed” and “freely motivated” research activities
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 – October 2010 All rights reserved
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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 new R&D and Engineering Strategies, SW Frameworks and related R&D and
Engineering SW and HW Infrastructures (B and C items)
B. “Integrated Multiscale Science – Engineering Technology, Product and Process Framework” which represents the Integrated Product and Process Development (IPPD) Frameworks next Generation
C. “Integrated Multiscale R&D and Engineering Infrastructural Framework
The impact of the “Strategic Multiscale Vision” on the Education, Information and Communication World is
analyzed in the enclosed “Multiscale Science – Based Language and Framework” Vision Paper.
Alessandro Formica – October 2010 All rights reserved
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2.3 Strategic Multiscale Framework Goals
As quoted in the Introduction, the fundamental goal of the Strategic Multiscale is to change in a qualitative,
not, quantitative way, organization, structure and strategies of the R&D and Engineering landscape and
catalyze and foster a spectrum of innovation trends:
Innovation for the For the Computing World
Fostering the design, development and application of a new generation of HW Systems b ased upon
Multiscale Quantum 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 established]
− a comprehensive Methodological Integration (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 Technology and Systems
Development Planning Processes
Innovation For Technology and Engineering
Promoting and Easing the development a New Field for R&D and Engineering: Multiscale
Experimentation, Testing and Sensing (Paragraph 3.4.2) 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 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 and Simulation and
Experimentation, Testing and Sensing, to define a real new way to do Research, Technology Innovation and
Engineering.
New Technological and Engineering Solutions (Multiscale Technology and Engineering: From
Multiscale Analysis to Multiscale Design) (Paragraphs 4.2, 4.3, 4.4). 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 Systems (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
Alessandro Formica – October 2010 All rights reserved
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Innovation For Research, Technology Development and Engineering Process Organization,
Strructure 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 on
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.
Definition of Unified Principles and Schemes to Analyze and Organize R&D and Engineering Data,
Information and Knowledge
New schemes described in the document allow to organize data, information and knowledge from a full
spectrum of “Information Sources” (computational models, experimentation, testing and sensing) in such a
way that different research and engineering disciplinary and application fields can take advantage from
information and knowledge from other disciplines and application sectors to develop “Integrated
Multidisciplinary Science - Engineering Knowledge Domains” are described in the Paragraph 3.4.3 and
Paragraph 3.6.
Development of Methodologically Integrated Multiscale R&D and Engineering Strategies 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” (Paragraph 3.4.3) 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
A New Generation of Computational Centers (Chapter 5)
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.
Building a new Generation of Cyberinfrastructures (Chapter 5) The new generation of Cyberinfrastructures which can be referred to as “Integrated Multiscale
Multidisciplinary Knowledge Integrator and Multiplier Cyberinfrastructural Environments” foresee a
comprehensive on-line integration of the full spectrum of Scientific and Engineering Methodologies and
related Teams. The Unifying Conceptual Context if offered by the new Modeling and Simulation Vision
(Modeling and Simulation as Knowledge Integrators and Multipliers) and the related Knowledge
Management schemes.
Alessandro Formica – October 2010 All rights reserved
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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
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 – October 2010 All rights reserved
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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
this kind of approach. 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 only 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 fields. The growing complexity of Materials, Devices,
Components, Systems and Systems of Systems which embody mutually interacting hierarchies of
technological, natural and human elements, suggest some specific extensions to classical CAD/CAE and
related Data, Information and Knowledge Management Systems.
The new Data, Information and Knowledge Management System proposed in this Vision Paper hinges on
the concept of “Map”. Map is a “Information and Knowledge Structure” which allows to integrate, link
and analyze Data from the full spectrum of scales (from atomistic to macro), the full spectrum of disciplines
and from a wide range of “Data Sources” (analytical and computational models, data bases, experimentation,
testing and sensing).
The Data, Information and Knowledge Management System correlates and fuses inside a coherent and
comprehensive framework data and information coming from different scientific and engineering teams,
from different methodologies, from the different tasks in the different stages of the whole Technology
Development and Engineering process. “Maps” allow for an effective insertion and management of the
more fundamental knowledge (basic and applied research) inside Technology Development and Engineering
phases. The concept of “Multiscale Map”, in the context of a Multiscale Vision and Framework, was
described by Alessandro Formica in the “Multiscale Science – Engineering Integration A new Frontier for
Aeronautics, Space and Defense” White Book, published by Italian Association of Aeronautics and
Astronautics (AIDAA) on March 2003.
Multiscale Maps (we would like to state again that Multiscale is a general term that includes as particular
case the single scale case)
Multiscale Maps describe (2D, 3D, or 4D representations) relationships between:
− Data (Multiscale Data Maps). Multiscale Data Maps are built applying statistical analysis schemes
(multivariate, PCA) or other techniques like neural networks. Multiscale Data Maps can be referred to as
Analysis and Design Variable Maps and they describe relationships between variables and parameters
used to characterize “Systems Behaviour”
− Physics Multiscale Physics Maps describing relationships between Physical (Chemical and
Biochemical) Phenomena
− System Architectural/Structural Elements (Multiscale Science – Engineering System Maps) describing
relationships between the hierarchy of Sub-Systems, Components, Devices, Elementary Structures
constituting a System
− Functions (Multiscale Science – Engineering Functional Maps) describing relationships between
System Architectural/Structural Element sand Functions performed
− Requirements - Performance – Properties – Architectural/Structural Elements (Requirement -
Performance – Property – Structure Maps) describing relationships between Requirements,
Performance (measured or calculated), Architectural/Structural Element and related Properties over the
whole scales and resolution levels
− Processing/Manufacturing Techniques – System Structures (Processing/Manufacturing – Structural
Maps) describing relationships between Processing/Manufacturing techniques and structures properties,
composition and characteristics (measured or calculated)
Alessandro Formica – October 2010 All rights reserved
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Main objectives:
� Developing new schemes allowing for a more in-depth analysis of data, information and knowledge
and related correlations and interdependencies
� Integrating the full spectrum of “Data Sources” (Data Bases, Analytical Theories, Computational
Models, Experimentation and Testing). The “Information Space” and the “Modeling and Simulation
as Knowledge Integrators and Multipliers” ease this kind of Integration
� Developing new CAD/CAE Environments specifically conceived to Design new Hierarchical
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.
Maps are indexed and related to specific R&D and Engineering Tasks and Phases and Design Hypotheses
and Decisions
A Multiscale Science – Engineering Data, Information and Knowledge Management System records,
organizes and manages: Information about all the previously defined Maps
This figure, drawn 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”), depicts a “Structure” like the proposed Multiscale Maps. In this case the Multiscale Map
describes relationships between physical phenomena and chemical/physical structural transformations linked
to Radiation Damage Process for Metals
Several Multiscale Maps can define what can be called a “Knowledge Domain”. “Knowledge Domains”
can be organized in a “Hierarchical Way”. For instance, A Physical “Knowledge Domain” linked to a
specific Process (Hypervelocity Impact or Explosion) can be constructed by assembling a range of
Multiscale Physical Maps describing more elementary physical (chemical and biochemical) phenomena
(fracture, fragmentation, phase change,..) for a material or component of a specific System.
Knowledge Domains are managed by a Multiscale Science – Engineering Knowledge Management
System.
Alessandro Formica – October 2010 All rights reserved
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Hierarchical Multiscale 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”. The following figure (from EADS) illustrates a two dimensional multilevel multiscale
view of an aircraft.
Two new features distinguish this kind of Maps and related Multiscale Multilevel Science – Engineering
CAD Systems:
− They should describe Architectural and Structural Elements of a System (or System of Systems) and
interconnections among all its constituents including the “Operational Environment” which is considered
as a “Architectural Element”. 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, resolution and scales
in an interactive way.
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)
– 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.
Alessandro Formica – October 2010 All rights reserved
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Maps represent the Hierarchical Multiscale Multilevel Science - Engineering CAD/CAE Systems Next
Generation or to better say Hierarchical Multiscale Multilevel Computer Aided Research, Development
and Engineering (CARDE) Systems.
Architectural and Structural Maps evolve along the Technology Development and Engineering Analysis and
Design Process thanks to Analysis and Design Modules (described in the paragraph 3.5.2), globally referred
to as “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
− Physics and Process Maps
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 can be 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 Processes, Phases and Tasks.
Physics and Process Maps
We use the term “Process” to indicate a cluster of elementary physical and biochemical 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. Processes
can concern more Architectural/Structural Elements. Physics and Process Maps are linked to:
− Architectural/ Structural and Functional Maps.
− Requirement - Performance – Property –Architecture/ Structure Maps and Processing – Structure
Maps
“Physics and Process 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 related to a specific R&D and
Engineering Task .
� Relationships between the full hierarchy of processes, phenomena and Architectural/Structural
transformations for a specific Task
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Maps are indexed in such a way as to relate them to specific R&D and Engineering Processes, Phases and
Tasks.
Physics Maps are linked to Integration Strategy Maps described in the Paragraph 3.6.4. Integration Strategy
Maps describes what Computational Models, Experimentation, Testing and Sensing Techniques/Procedures
are applied to analyze specific physics 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 Map Data Bases”.
Integration of the previously defined Maps allow to correlate:
− architectural and structural elements to functions performed and functions to architectural and structural
elements (linking Architectural and Functional Maps)
− functions to physical phenomena and processes (linking Functional Maps with Phenomena and Processes
Maps
− functions to design hypotheses (linking Functional Maps with Architectural/Structural Maps)
− properties to phenomena and processes
“ Performance – Properties – Architecture/Structure – Processing” Maps
The definition of the Performance – Properties – Architecture/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 article: Vol. 277 (29 August 1997) pp. 1237-1242. The following
figure illustrates the application (by Prof. Olson – Northwestern University) of the Performance – Properties
- Structure – Processing Integration Strategy to the design of new alloys.
Performance – Properties – Architecture/Structure - Processing Maps are indexed in such a way as to relate
them to specific R&D and Engineering Processes, 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”
Alessandro Formica – October 2010 All rights reserved
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Processing – Architecture/Structure Maps This kind of software environments contribute to characterize and manage relationships between processing
and manufacturing activities and the resulting architecture/sstructures
. 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
Maps are indexed in such a way as to relate them to specific R&D and Engineering Processes, Phases and
Tasks. Processing – Structure Maps defined during the R&D and Engineering Process for different purposes
and tasks are organized and recorded in the “Processing - Structure Map Data Bases”
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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 “Science-Engineering Information Space” is associated to any
analytical, computational model/method, and experimental, testing and sensing procedure and technique.
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 (uncertainty level definition),
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” characterize experimental, testing and sensing techniques and
procedures.
The ”Science – Engineering Information Space” applies also 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 3.4.2
− 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 are related to the specific task. 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 fidelity. Validation procedures can also be
applied to experimentation, testing and sensing. In this case a “Cross Validation” strategy is applied which
foresee the comparison of different experimentation and testing techniques.
Alessandro Formica – October 2010 All rights reserved
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The “Information Space”, should also include Multiscale Data and Physics Maps worked out during the
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 in a systematic way external conditions and/or
system variables (typology and architecture of a material or device)
− fixing external conditions and system variables and varying in a systematic way model and/or
methodology variables (for a molecular dynamics model: simulation time, force fields typology, number
of particles,…).
− any other possible combinations
Information Space Relevance
Three considerations underlie the definition of the “Multiscale Science – Engineering Information Space”
concept and method:
� rationally correlating advances for models and possible 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 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 and related coupling schemes to get some
Information at a specific level of accuracy and uncertainty.
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 and experimental, testing and sensing techniques and procedures.
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“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 and experimental and testing procedures/techniques and
possible coupling schemes at a certain level of accuracy and reliability.
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 are and
� 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..
The importance to define in a formal way the “Range of Validity (or Applicability Domain)” of a model is
highlighted in the following figure (Center for Computational Materials Design – NSF)
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 “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 techniques. The need to define 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”. The following figure is drawn from this document:
Alessandro Formica – October 2010 All rights reserved
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Thanks to the “Multiscale Science – Engineering Information Space” concept and method, it is possible to
define “Costs/Benefits Function” for models 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” 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. According to the previous analysis, the “Multiscale Science-
Engineering Information Space” concept and method is instrumental to identify:
� shortcomings and limitations of computational models 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
� 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 techniques to deal with specific R&D and Engineering Tasks
� integrated strategies for jointly applying multiphysics multiscale analytical, computational and
(multiscale) experimental & testing techniques to deal with specific R&D and Engineering Tasks
Alessandro Formica – October 2010 All rights reserved
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3.4 Modeling and Simulation as Knowledge Integrators and Multipliers and
Unifying Paradigm for Scientific and Engineering Methodologies
3.4.1 The New Computational Modeling Vision The “Vision” of “Modeling & Simulation” as “Knowledge Integrators and Multipliers” (KIM) and
“Unifying Paradigm” for Scientific and Engineering (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 Methodologies because they
are able to integrate and synthesize, in a coherent framework, Data, information, and Knowledge from:
���� a number of different 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.
���� Multiscale Science – Engineering Information Spaces
���� Maps generated by a wide range of methodologies (analytical theories, experimentation, testing and
sensing)
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. Computational Model Information
Spaces and Maps embody and organize Information and Knowledge get by the full spectrum of analytical
theories, computational models of different accuracy and resolution levels, experiments, tests and sensing
measures used to develop and validate Models. 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. A
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, 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.
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 next page:
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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 and Testing World, for “Model”, as referred to a specific Experiment or Test carried out
with a specific experiment working in a specific operational mode and probing a specific system for a
specific tasks, 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 Data and 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 and modes among the different equipments
− Integration schemes
− Data and Information Flow among the different equipments
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3.4.2 Computation and Experimentation/Testing/Sensing Integration Even if attention to integration is positively increasing, particularly for models development 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 and
validation phases. Advances in modeling and simulation are intimately linked to progress in experimental
methods and techniques and vice versa. A direct correlation and strong mutual dependencies, both in the
model development and validation 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. That is very seldom carried out. 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 phase, as it occurs today, but, also, in the application phase.
An effective R&D and Engineering Strategy should find the way to synergistically take advantage of
advances in both the fields.
In many cases, today, advanced HPC/Modeling/Simulation and experimental/testing/sensing programs are
conceived and managed, if not as antithetic entities, surely, 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 full
spectrum (tsunami) 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 and Multiscale Maps are key elements to realize this integration.
Experimental, Testing and also Sensing Data Networks have to be analyzed, interpreted and correlated
according to a unified vision which can be offered by new Modeling Strategies and Frameworks.
The KIM concept is a fundamental theoretical and methodological basis Methodologically Integrated
Multiscale Science - Engineering Strategies are built upon. Three key objectives characterize the KIM
concept:
���� putting on different bases relationships between Modeling and Simulation, from one side, and
Experimentation, Testing and Sensing, from the other side.
� easing the development of multiscale multidisciplinary experimental, testing and sensing technologies
and strategies which fully integrate a spectrum of experimental, testing and sensing techniques and
related application procedures
� shaping a full integration of multiscale modeling and simulation with multiscale experimentation, testing
and sensing to define “Methodologically Integrated Multiscale Science - Engineering” Strategies.
Alessandro Formica – October 2010 All rights reserved
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Modeling & Simulation Application strategies in the innovative technology development field are
significantly hampered and limited the following fundamental contradiction:
“when we develop innovative technologies, we often (practically always) 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 science and engineering strategies are not up to the challenge. Key elements of
the new Vision of Modeling and Simulation are:
� 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 3.4.3]
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3.4.3 Extension of the Multiscale Approach to Experimental and Testing World
The following issues are motivating the birth and they are driving the development of the Experimental
Multiscale and Testing 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 and testing
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 and testing 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
and Testing Multiscale World exists. Advances in Multiscale Computational Models and Methods is
directly linked to advances in experimental and testing multiscale.
� Hierarchical Materials and Devices and Complex Systems made up of a Wide spectrum of sub-systems,
components, devices call for Multiscale Integrated Experimental and Testing Equipments to get an in-
depth and Comprehensive understanding that, in several cases cannot be given only by computational
models
� 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. In this context, 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, X Ray Synchrotron
are two 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.
For “Experimental/Testing/Sensing Multiscale” we mean:
���� Single Experimental/Testing/Sensing Equipments able to probe “Systems” (also using different
operational modes) over a range of space and time scales.
���� Integration of multiple experimental/testing/sensing equipments. Integration can occur off-line. 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 also be implemented over a Cyberinfrastructure. In this case the set of
experimental/testing/sensing equipments is connected through a network. A key problem is
synchronization.
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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…..”
Examples of Multiscale Experimental and Testing Techniques and Systems
� Synchrotron Radiation from European Synchrotron Radiation Facility 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.
� ESRF was also involved, jointly with the Laboratoire de Physique et Mécanique des Matériaux - FRE
CNRS 3236, 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
� MUSTER (Multi-scale Testing and Evaluation Research) Facility Institute of Advanced Energy,
Kyoto University The facility covers from atomic to real application scale on material performances
and structural / chemical / physical features and it is dedicated to R&D of innovative and advanced
energy materials. Multi-scale testing and evaluation is considered as critical for R&D of advanced
materials. Structural, chemical, physical and mechanical features of the advanced materials are
studied atomic through real application scale.
� W. M. Keck Laboratory for Combinatorial Nanosynthesis and Multiscale Characterization at
University of Maryland has built an experimental system which consists of a scanning electron
microscope, atomic force microscope, and scanning tunnelling microscope combined in one state-of-
the-art instrument (JEOL JSPM-4500A). This instrument allows for multi-scale microscopy at
variable temperatures and proximal probe measurements of devices, growth structures and attendant
fields
Alessandro Formica – October 2010 All rights reserved
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Strategic Research and Development Agenda for Multiscale Experimentation and Testing:
� 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 analyze Data from a spectrum of Single and Multi Scale Experimental and
Testing Systems
� The development of new Strategies and related Frameworks to “rationally” integrate a wide range
of Single and Multi Scale Experimental and Testing Fields for specific R&D and Engineering Tasks
� 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 and Testing
Methodologies, Strategies and Environments to define really “Integrated Multiscale R&D and
Engineering” Strategies and Frameworks.
Alessandro Formica – October 2010 All rights reserved
29
3.4.4 Methodologically Integrated Multiscale Science – Engineering Strategies
The “Multiscale Science – Engineering Information Space” and the “Information – Driven” concept
(described in the paragraph 3.5.1) allow us to define new “Applicability Conditions” and “Predictability
Criteria” for Computational Models which 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 with
experimentation, testing and sensing for specific tasks.
Applicability Conditions. Two basic conditions which rule the development and the implementation of
highly 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 are important for specific
R&D and Engineering tasks and purposes.
� at what level of accuracy 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 and reliability of models [Multiscale Science – Engineering Information Space].
Applicability Conditions can be applied to the Experimental, Testing and Sensing Fields. A derailed
comparison of the “Information” which can be get bythe 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 and uncertainties for specific tasks? Errors and
uncertainties can be fairly 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 uncertainty and evaluate the level of confidence in models?
[Multiscale Science – Engineering Information Space and 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 single and multi scale computational models and what single and multi scale experiments, tests and
sensing measures have been selected to deal with a specific task
� What is the order of execution and the overall Integration Scheme as shaped by the “Applicability
Conditions” (Multilevel Network of Computational, Experimental, Testing and Sensing Models and
Techniques)
� What is the flow of input and output data and information among the full spectrum of models and
experiments/tests/sensing techniques.
Alessandro Formica – October 2010 All rights reserved
30
Several hypotheses can be taken into account and interactively changed
For each specific task, “Integration Strategy Maps” describe:
� The full set of Computational, Experimental, Testing and Sensing Models/Techniques applied to deal
with specific task
� The order of execution and Integration Scheme: Multilevel Network of Multiscale Computational,
Experimental, Testing and Sensing Models and Techniques. For each Model
� Multiscale Science – Engineering Information Spaces
� Input and Output Data and Information Flow
� Multiscale Maps
“Integration Strategy Maps” defined during the R&D and Engineering Process are recorded, organized and
managed by a specific Integration Strategy Maps Data Base
The following figure (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, is a representation of a possible (simplified version) combination
of the proposed Multiscale Map of Physics and Multiscale “Models Integration Strategy Map” of
Computational Models and Experiments & Tests
Alessandro Formica – October 2010 All rights reserved
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3.4.5 Multiscale Knowledge – Based Virtual Prototyping and Testing 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 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 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) to testing
for 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
The following figure, drawn 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 context of the Workshop “Mathematical Methods for V&V SANDIA , Albuquerque, August 14-16,
2007, shows an “Integrated Multiscale Multilevel Testing Strategy for a Complex System”: from coupons to
the full System.
Alessandro Formica – October 2010 All rights reserved
32
3.5 Designing the R&D and Engineering Process
3.5.1 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 introduced 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 describe 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 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 models/techniques)
are needed and how they can be combined to get the previously identified information
Alessandro Formica – October 2010 All rights reserved
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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 flow 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 uncertainty 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 and testing procedures/techniques should be developed and integrated to deal
with a specific analysis task. It is absolutely fundamental 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 is the
“lack of Knowledge” 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 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 and
testing procedure/technique and related coupling scheme. Application Strategies defined in the Paragraph
3.4.3, 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 “Science-Engineering Information Space” and the
“Multiscale Scientific and Engineering Information Analysis” concepts and methods 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 – October 2010 All rights reserved
34
3.5.2 The R&D and Engineering Process Analysis and Design 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 Analysis and Design Basic Constituent Elements Multilevel Networks of Phases (Temporal Period inside which specific activities are accomplished)
and Analysis and Design Modules and Tasks. Any R&D and Engineering Process can be
subdivided in a Multilevel Network of Phases, In turn any Phase subdivided into a Multilevel
Network of Analysis and Design Modules and Tasks . At the highest level a Phase can corresponds
to a specific R&D and Engineering Project.
.
� R&D and Engineering Process Analysis and Design Basic Information Structures and Sources
− Library of Computational, Analytical Codes, Experimental and Testing Equipments
− Data Bases
− Computational, Analytical, Experimental and Testing Models
− Analysis and Design variables (projected over the full set of levels and scales)
− Multilevel Multiscale Relationships between Requirements/Performance –
Architecture/Structures Properties – Physics
− Multilevel Network of Relationships between Analysis and Design Variables
− Multilevel (Multilevel) Multiscale Network of Physical Phenomena and Processes
���� R&D and Engineering Process 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,
Cyberinfrastructural Frameworks
The Multilevel Network of Entities defines what can be called a “Collaboratory Framework”
���� Architectural/Structural System Architecture (detailed over the full set of levels/scales)
− Multilevel Multiscale Network of Architectural/Structural Elements: Systems (in case of System of
Systems) – Sub – Systems – Components – Devices – Structures (Materials, Fluids, Plasmas)
���� R&D and Engineering Objectives (projected over the full set of System Architectural/Structural
Elements at all the levels and scales)
− Requirement
− Performance
− Properties
− Functions
− Requirement/Performance – Structure – Property Relationships
− Structure – Processing Relationships
− Architectural/Structural Element – Function Relationships
Alessandro Formica – October 2010 All rights reserved
35
R&D and Engineering Process Analysis and Design Process Strategy
The Strategy to carry out a generic “R&D and Engineering Project” is described by a multilevel network of
R&D and Engineering Strategy Modules and Hypothesis Modules. Data and Information collected by the
full spectrum of Maps and Modules are managed by a specific Multiscale Science – Engineering Maps and
Modules Management System.
Hypothesis and Decision Modules
The “Hypotheses Module” tracks, for a specific R&D and Engineering Project, three Hypothesis/Decision
classes and related relationships and interdependencies:
���� Hypotheses/Decisions related to possible set of System Requirements/Performance and Functions
���� Hypotheses/Decisions related to a possible set of System Architectural/ Structural tentative solutions
���� Hypotheses/Decisions related to a set of possible Analysis and Design Strategies
The full set of Hypotheses/Decisions linked to specific R&D and Engineering Project and related
relationships is described by a Multilevel Network of Hypothesis/decisions Modules and Maps
Each Hypothesis linked to a specific set of System Requirements/Performance and Functions can be related
to a set of System Architectural/Structural tentative solutions, in turn, each Hypothesis related to a specific
Architectural/Structural solution can be related to a set of possible Analysis and Design Strategies which are
described by “R&D and Engineering Strategy Modules.
The Grid of Relationships is described by a specific “Hypothesis/Decision Maps”
All the Information related to the full set of Hypotheses taken into account inside a specific R&D and
Engineering Project (and related relationships) is managed by a “Hypotheses Data Base Management
System”
We introduce the concept of “R&D and Engineering Strategy Modules” which shape the “Architecture” of
a R&D and Engineering Process. Two classes of “R&D and Engineering Modules” are defined:
− R&D and Engineering Design Modules
− R&D and Engineering Analysis Modules
R&D and Engineering Design Modules
Research, Development and Engineering Design Modules represent the overall architecture of any Research,
Technology Development and Engineering Process. Any R&D and Engineering Process can be describe by
a Multilevel Network of R&D and Engineering Design Modules. Any Module can be broken down in a
multilevel network of R&D and Engineering Tasks
R&D and Engineering Design Modules describe:
���� The full set and hierarchy of R&D and Engineering Design Modules and Tasks linked to them
���� The full set of Architectural/Structural and Functional Maps linked to them
���� R&D and Engineering Design Variables, Objectives and Analysis – Design Variable relationships
���� The full set of Analysis Modules linked to them
R&D and Engineering Design Modules are recorded and managed in a specific “R&D and Engineering
Design Modules Data Base”
Alessandro Formica – October 2010 All rights reserved
36
R&D and Engineering Analysis Modules
Analysis Modules are organized in a hierarchical and recursive way (Multilevel Network). An Analysis
Modules of high level can embody “Analysis Modules” of lower level. in a recursive way “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 Models/Techniques) are applied to achieve
Analysis Objectives. “Integration Strategy Maps”, described in the Paragraph 3.6, are defined to 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. 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 Libraries
Libraries are software environments which allow to catalogue and manage a whole set of :
���� Computational codes which implement different methods [Molecular Dynamics, Coarse Grained MD,
Monte Carlo, Density Functional Theory, Phase Fields, Dislocation Dynamics, Continuum Finite
Elements,….]
For each Computational Code, Libraries describe:
���� Characteristics, applicability conditions and what kind of information can be get from them for specific
application domains and conditions.
���� Single and Multi-scale (space and time) Multiphysics coupling methods and schemes.
Experimental, Testing and Sensing Technique/Equipment 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 Multi-scale (space and time) coupling methods and schemes.
Alessandro Formica – October 2010 All rights reserved
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3.5. 3 Multiscale R&D and Engineering Information – Driven Strategies
Two interrelated Phases characterize the “Designing of the R&D and Engineering Process” Strategy
Multiscale Information Analysis (linking information with R&D and engineering tasks)
���� identify the critical (scientific and engineering) information for the full spectrum tasks of the R&D
and Engineering analysis process [Multiscale Science-Engineering Information-driven Strategies]
���� define the related levels of resolution, accuracy, and reliability (uncertainties management) for the
previously identified information [Multiscale Science-Engineering Information-driven Strategies]
� correlate phenomena occurring at different time and space scales (from continuum down to atomic
scales, as needed) and elucidate the related relationships. This step enables researchers and engineers to
correlate science and engineering issues inside a really unified vision [Multiscale Maps]
� Analyze Information and turning raw data and Information into Knowledge [Multiscale Maps]
These steps are fundamental to achieve a comprehensive overall picture of the information which
characterize and condition technology development and advanced engineering and assess when, where, to
what extent, and if multiscale is really needed.
Multiscale Information Flow Analysis (Linking information with information sources and define the
overall Integration Strategy)
� correlate information and information sources (experiments, tests, sensing measures, computations,
analytical formulations) identify the most critical methodological (analytical theories, modeling &
simulation, experimental & testing methods and techniques) shortcomings and related development
paths [Multiscale Science – Engineering Information Space]
� identify the relationships and the interdependencies among the several information sources at different
scales (experiments, tests, sensor measures, computations, analytical formulations) to define R&D and
engineering strategies [Multiscale Information Driven Strategy, Multiscale Science – Engineering
Information Space, Integration Strategy Maps]
� analyze the overall information flow pattern inside the spectrum R&D and Engineering phases and for
the full spectrum of multilevel network of tasks [ “Multiscale Design and Analysis Modules]
���� Define Integrated R&D and Engineering Strategies [what is the more effective mix of Information
Sources (Multiscale Science-Engineering analytical, computational and experimental, testing, sensing
models/methods/techniques)] [Integration Strategy Maps and Methodologically Integrated
Multiscale Science-Engineering Strategies]
Alessandro Formica – October 2010 All rights reserved
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3.6 “Integrated Multiscale Science – Engineering Analysis Strategies”
“Integrated Multiscale Science – Engineering Analysis Strategies”: are the last component of the “Integrated
Multiscale Science – Engineering Framework” and they synthesize and take advantage of all the previously
described concepts and methods.
“Integrated Multiscale Science – Engineering Analysis Strategies” are implemented inside the “Analysis
Modules” described in the Paragraph 3.5.2 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.
A key goal of Multiscale Information - Driven Strategies is to develop a hierarchy of Multiscale Multilevel
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” concept and 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.
Alessandro Formica – October 2010 All rights reserved
39
Three application lines for Multiscale Analysis Strategies can be devised:
� Multiscale Scientific Analyses finalized to “Understand” Physical and Chemical Phenomena and
Processes and their Relationships a spectrum of multi scale 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 of varying complexity 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
� 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 Multiscale Scientific Analyses described in the previous item. Constitutive Equations and
Sub – Grid Models are inserted inside classical Engineering codes. 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
Both the approaches can be integrated in an interactive way inside an overall strategy. Some tasks can be
executed with the Multiscale Scientific Analysis approach. Some other tasks can be carried out by applying
Reduced – Order Modeling and/or the Hierarchical approach.
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 recommendation for all the strategies is to adopt an “Adaptive and Multi Step Selection of
Details and Resolution”
Alessandro Formica – October 2010 All rights reserved
40
Integrated Multiscale R&D and Engineering Analysis Strategies develop over the following phases and
steps:
1) Definition of 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 (Analysis Modules): Multilevel
Network of R&D and Engineering Analysis Modules and Tasks and related relationships and
interdependencies. More hypotheses can be worked out. Hypotheses are tuned and/or modified
following Analysis results.
This Phase 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]
– 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]
– Identification 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]
– 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]
– 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 fidelity 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)
Alessandro Formica – October 2010 All rights reserved
41
– 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 allow 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
− 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 and Testing 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.
4) 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 each Analysis Module (Architecture of the Multilevel Network of Tasks can be changed
following results from Tasks Analysis execution)
���� 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 – October 2010 All rights reserved
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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 step towards 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 to develop 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. It is to be highlighted that the IMPULSE experience can be relevant to many
EUMAT and NMP themes and areas and also outside them: Energy and Aeronautics and Space for instance.
Concepts like “Integrated Product and Process Development” (IPPD) and “Product Life – Cycle
Management” (PLM) have reshaped the way Industry dealt with the development of high-tech products and
related manufacturing processes. The ”Integrated Multiscale Science-Engineering Framework” can be a
suitable basis to develop upon a new generation of Multiscale Science-based CAD, CAM, CAE and PLM
and IPPD Software Environments which can start a new phase for Research and Innovation.
IMSE-TPPD Software Environments are constituted by:
� Multiscale Science – Engineering Data, Information and Knowledge Management Systems
� Multiscale Multiphysics Computer Aided Design (CAD) System (based upon Architectural and
Functional Maps)
Alessandro Formica – October 2010 All rights reserved
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� Multiscale Multiphysics Computer Aided R&D and Engineering (CARDE) Systems
− Multiscale Maps
− R&D and Engineering Strategy Modules
− Integration Strategy Maps
− Experimental, Testing and Sensing Modules
− Computational Codes Library
− Experimental and Testing Techniques/Equipments Library
� Application Modules and Frameworks (Life – Cycle, Safety & Security, Environmental Impact,…)
� Multiscale Visualization Modules
Software Environments run over “Integrated Multiscale Science – Engineering Cyberinfrastructural
Environments”
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 and context which can be called “ Integrated Multiscale
Multidisciplinary Science – Engineering (or Science – Based) Cyber Extended Enterprise”.
The IMSE-TPPD Framework fundamental goal is to significantly improve identification, generation,
analysis, fusion, integration and interpretation of Data, Information and Knowledge associated to the
different tasks in the different phases of a general R&D and Engineering Process. and for the whole Product
Life – Cycle.
Innovative Technology Products and Processes call for the development and the integration of a so large set
of basic technologies, devices and components and so long development and maturation times that a new
kind of “two way strategic collaborations” among universities, research centers, and high-tech companies
is needed. A “two way strategic collaboration” should
� Enable 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 abd engineering process taking full and
systematic advantage of science knowledge and progress
Advances in Computing, Information and Communication (CIC) technologies shape the “structural and
technological layer” for new cooperative landscapes. At the same time, the new IMSE-TPPD Framework
can represent the “methodological and conceptual layer” which allow to take full advantage of technology
advances (Computer, Information and Communication (CIC) and Experimentation, Testing and Sensing
Technologies).
People often talk, today, about “two-way” relationships between the industrial world, from one side, and the
academic and research world, from the other side. However, we think that is very difficult to set up a real
comprehensive and highly effective “two way” partnership between the two worlds without being able to
Organize and Integrate “Knowledge” get in the different Technology Readiness Level phases: Basic and
Applied Research, Technology Development, Product Design, Product Manufacturing, Product Testing
(Development and Operational Testing).
Alessandro Formica – October 2010 All rights reserved
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Some elements to improve Knowledge Integration and Organization have been described in the previous
chapters:
− Multiscale Maps
− Integration Strategy Maps
− R&D and Engineering Strategy Modules which define, at several levels of detail, the Architecture of the
R&D and Engineering Process
“Multiscale Science –Engineering Data, Information and Knowledge Management Systems“ are powerful
“Integration Schemes” because they correlate and fuse inside a coherent and comprehensive framework
data, information and knowledge coming from different scientific and engineering teams, from different
methodologies, from the different tasks in the different stages of the whole Technology Development and
Engineering Process.
As “Cyberinfrastructures” become bigger and bigger by connecting an ever increasing number of resources,
facilities and teams, as the “Data Challenge” becomes a critical conditioning factor. In the Virtual
Distributed Environments or Cyberinfrastructures, researchers and designers can access a really huge amount
(tsunami) of data. How can we turn this ocean of data in useful knowledge, taking into account that the
needs and the points of view of the different groups are absolutely not homogeneous? Advanced graphic and
virtual reality technologies are very important assets, but not the ultimate solution to the problem. The
amount of data related to a complex R&D and engineering process and analysis of complex systems is
continuously increasing. It is worthwhile highlighting that, if it is true that the lack of data and information
represents a negative status, it is also true that the a flood of data and information can represent an even more
dangerous situation. Data and information growth reflects, of course, increasingly systems complexity and
science-engineering integration.
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 Engineering World”.
The IMSE-TPPD Framework deals with the following areas:
���� Research and Technology Development
���� Engineering Analysis and Design
– Mission and Scenario Analysis
– 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
���� System Engineering
���� Life – Cycle Engineering
���� Safety Engineering
Alessandro Formica – October 2010 All rights reserved
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���� Manufacturing and Processing R&D and Engineering
���� 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)
���� Innovative Technology and Systems Development Planning
Note: Multiscale Maps and Multiscale Knowledge Domains can not only improve the transfer of
Knowledge between Scientific and Engineering Areas (Knowledge Vertical Integration), but, also, improve
the transfer of Knowledge among the spectrum of the previously defined Areas (Knowledge Horizontal
Integration).
Alessandro Formica – October 2010 All rights reserved
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4.2 Multiscale Systems Engineering
Integration among different technologies and different sub-systems, components and devices is, today, a
fundamental challenge in the 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 will be a catalyst 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.
In the “System Engineering” field, Analyses Challenges are linked to the following issues:
���� Analysis of the Multiscale Interactions between the “System” and the Operational Environment for the
whole “Operational Envelope” and for extreme and accident conditions in order to define “Systems
Requirements”
���� Analysis of Requirements over the full spectrum of scales (Multiscale Requirements Traceability)
���� Analysis of the “Requirements – Performance – Architecture/Structure” 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
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 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 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.
Alessandro Formica – October 2010 All rights reserved
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���� 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. No unified
multiscale and Multilevel variable fidelity hierarchies of models exist that describe behavior across this
huge scale range. Instead, we have separate and non-communicating models at the different scales and
fidelity levels.
The development of “Integrated l Hierarchies of Multiscale Multilevel and reduced – order
computational models and experimental, testing and sensing models/techniques” can be considered as a
key target.
A multiscale system design approach opens the way to new strategies for complex systems control. A
combination of new multiscale sensors, meso, micro and nano systems, and distributed computing systems,
can lead to innovative 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.
Hierarchical System Integration and Nanotechnology
Hierarchical System Integration is a fundamental goal for Nanotechnology in order to fully exploit
Nanotechnology potentialities for Engineering and Manufacturing.
The Following text drawn from the “Active Nanostructures and Nanosystems (ANN) Program Solicitation
NSF 05-610” clearly describes this goal
� Nanoscale Devices and System Architecture.
….New concepts, tools and design methodologies are needed to create new nanoscale devices, synthesize
nanosystems and integrate them into architectures for various operational environments….. In order to
systemize the design of complex nanosystems, multiple layers of abstractions and various mathematical
models to represent component behavior in different layers are also required….. A special focus is on new
architectures and improved functionality of large and complex nanosystems, and their integration with
larger scale systems….
� Hierarchical Nanomanufacturing.
….Research in this area will focus on creating nanostructures and assembling them into nanosystems and
then into larger complex structures of at least two length scales where the principles of manufacturing or
operation are different……
Alessandro Formica – October 2010 All rights reserved
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4.3 Multiscale Process Engineering
“Environmental and Climatological Issues” emerged as one of the critical challenges facing the the whole
Manufacturing and Bio-Chemical Processing 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, strategies focus
essentially on pollution cleanup and waste management technologies. A more radical and innovative
approach, which can be defined "clean by design", “green engineering” or “process intensification” , entails
the re-design of manufacturing and process system to eliminate at the root the formation of pollutants and
toxic by-products. This new approach entails a tight multiscale multidisciplinary science – engineering
integration and new Integrated Frameworks like the one here described.
The limits of the single scale approach to Chemical Engineering Design were well described 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". This scheme describes a multiple
space and time scenario.
It is important to highlight that the integrated multiscale methodology allows to go beyond the classical
reductionist approach which tries to represent, for instance, a whole plant by decomposition into smaller
units down to catalyst particles, droplets, bubbles, and finally to molecular processes, going into more and
more detail. Multiscale enables the development of systemic models that considers the global behavior of
complex systems as a whole integrating representations and information across multiple scales : _ nanoscale:
molecules and clusters
− microscale: drops, bubbles, and particles
− mesoscale: unit operations such as reactors and heat exchangers
− macroscale: production units and chemical plants
− mega scale: local and global environmental impact studies
Multiscale can also lead to an integrated “Vision” of the classical four areas which characterizes
chemical engineering :
− Process Modeling
− Ecosphere Modeling
− Properties Modeling
− Product Modeling
Two examples of fields which can be affected by Multiscale and even more by “Integrated Multiscale
Science – Engineering Frameworks” are highlighted in the following:
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 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 molecular levels by
manipulating supramolecular building blocks. New 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 open the way to a "smart chemical
engineering" otherwise referred to as “Process Intensification” to meet, at the same time, tight economic and
environmental requirements. At this level, new functions such as self-organization, regulation, replication,
Alessandro Formica – October 2010 All rights reserved
49
and communication can be created by manipulating supramolecular building blocks. At a higher microscale
level, detailed local temperature and composition control through staged feed and heat supply or removal
would result in higher selectivity and productivity than does the conventional approach that imposes
boundary conditions and lets a system operate under spontaneous reaction and transfer processes. Energy in
specific forms such as microwave or ultrasound also may be supplied locally. The theory of wavelet
decomposition can represent a basis for the multi-resolution description of operating variables and modeling
relations.
.
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 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 formulations. The combined use of new sensors, Micro
Electro Mechanical systems (MEMS) technology and a scientific understanding of physical and chemical
phenomena will lead to microreactors, micro separators, and micro analyzers smaller than a fingernail,
making possible accurate control of reaction conditions with respect to mixing, quenching, and temperature
profile.
Alessandro Formica – October 2010 All rights reserved
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4.4 Multiscale Environmental, Safety and Extreme Engineering
Computational Multiscale is widely used in the Environmental and in the Safety (Nuclear Energy, in
particular) fields. Strategic Multiscale can play a pivotal role in promoting significant innovation and real
breakthroughs not simple improvements in the following areas:
� Analysis of the Dynamics of Ecosystems and two – way relationships between Urban and
Industrial Systems – Ecological Environments.
� Developing unified analysis of the hierarchy (multilevel Network) of interlinked multiscale
multiresolution multiphysics (physics, chemistry and biochemistry) phenomena and processes which
characterize the dynamics of the Ecosystem over a full range of time scales (short, medium long term)
also taking into account the uncertainty issue. This kind of analyses will benefit of advances in new
multiscale multiresolution monitoring Systems and Cyberinfrastructures [Methodologically Integrated
Multiscale Science – Engineering Strategies, Integration Strategy Maps, Information – Driven
Strategies]
� Integrating Multiscale Multiresolution Multiphysics Data from a full spectrum of Space, Aerial, Sea
(and Under Sea), Surface and Sub Surface sensors [Multiscale Maps]
� Integrating (Multiscale) Field Sensor Networks with Laboratory Experimental Facilities [Multiscale
Experimentation, Testing and Sensing, Integration Strategy Maps, Information Space Concept,
Methodologically Integrated Multiscale Science – Engineering Strategies]
The need of integrating science and engineering is even more pressing if we consider a new “Integration
Frontier”: Infrastructures - Environment – Climatology – Health”.
���� Integrated Multiscale Technology and Engineering Systems Design
� The need to meet with and extended range of ever more demanding requirements (safety, resilience,
operation in extreme environments, environmental compliance) put an increasingly pressure on
traditional technology development and engineering solutions. A specific objective is of particular
relevance is the Design of a new generation of “Inherently” Multiscale Infrastructural Technologies
and Engineering Systems. The full spectrum of concepts, methods, environments and strategies
described in the “Integrated Multiscale Science – Engineering Framework” Chapter can be directly
applied in this Area..
���� Extreme Engineering for Extreme Operational Environments (Safety and Security) � Approximations and simplified experimental analyses which can be up to the challenge to characterize
and design materials and systems for normal operational conditions are not well suited when materials
and systems must operate in extreme conditions, accidents included. In these cases, a multiscale
multiresolution science – engineering approach should be applied.
Lessons to be Learnt in the Safety Engineering Field :
− If you do not understand Physics you cannot understand, manage and reduce uncertainties and risks
in a comprehensive and “acceptable” way. The warning pointed out by Nobel prizewinner Richard
Feynman in the appendix F of the Challenger explosion report (Roger’s Commission) about the risk
to use only empirical and semi-empirical models has yet to be fully perceived and evaluated. This
warning has a general value for all the engineering (not only Safety) and far beyond the aerospace
world.
Alessandro Formica – October 2010 All rights reserved
51
���� Multiscale Systems Testing for an Extended Operational Envelope
� We implement the concept of “Multiscale Multiresolution Knowledge – Based Development and
Operational Testing” and the related application schemes described in the paragraph 3.4.4
���� Design and Management of Multiscale Environmental and Climatological Monitoring Systems
� A critical element to understand, predict and control “Complex Systems”, with particular reference to
Environmental, Civil, Infrastructural, Aerospace and Defense Systems, is the design of “Multiscale
System Monitoring Networks” . Significant advances in Sensors, Computing, Information and
Communication technologies enable the creation of complex networks of different types of monitoring
sensors/systems operating over a full spectrum of space and time scales and a wide range of physical,
chemical and biological domains. These networks are connected ( also in real time) at a powerful set of
distributed data repositories and computing facilities. . Key issues:
−−−− identify key variables to be monitored over the spectrum of scales and resolution levels accounted
for, and at what level of accuracy and reliability
−−−− identify relationships and interdependencies between physical and bio-chemical phenomena and
processes at the different space and time scales and resolution levels
Integration of available analytical theories, computational models, and data from sensing and
laboratory experimental facilities applying Multiscale Maps and the Information Space method and
concept can be useful to build first hypotheses about this issue
−−−− devise a strategy to select the right type of sensors [Multiscale Science – Engineering Information
Space and Information – Driven strategies] to monitor the previously identified key variables over
the right range of space and time scales at a well defined degree of accuracy and reliability.
−−−− define the field monitoring systems architecture at all the scales and for all the media [Multiscale
Science – Engineering Information Space, Information – Driven strategies and Integration Strategy
Maps].
− define a strategy to integrate fields data and information with laboratory experimental systems,
theory and computational models [Multiscale Maps and Multiscale Science – Engineering
Information Space and Information – Driven strategies]
− define a suitable mix of field sensors and experimental techniques and methods at all the scales and
integrate them in order to improve the knowledge about the dynamics of the (natural, technological,
natural-technological) system under observation and analysis. Two –way relationships between
field monitoring systems and experimental facilities are functional to develop innovative sensing
strategies. [Multiscale Maps, Multiscale Science – Engineering Information Space and Information
– Driven strategies, Integration Strategy Maps]
− devise a general “integration strategy” which allows to link together all the previously quoted items
inside a coherent and comprehensive context Multiscale Maps, Multiscale Science – Engineering
Information Space and Information – Driven strategies, Integration Strategy Maps, R&D and
Engineering Project Strategy Modules]
Alessandro Formica – October 2010 All rights reserved
52
���� Integrated Space – Time Environmental/Pollution Analysis
� The Integrated Multiscale Science – Engineering Framework” “ enables a new “Integrated Space-Time”
approach to environmental and pollution issues. “Integrated Space-Time” approach means that in this
new methodological and conceptual context, we can link together inside a unified context data,
information, knowledge and models which characterize the three fundamental phases which characterize
the pollution process :
− Generation Phase (generation of pollutants inside a technological system)
− Transportation/Diffusion Phase through different media (air, water, land)
− Interaction or Biomedical Phase (interaction with natural, urban & industrial and biological systems
(humans included)
Alessandro Formica – October 2010 All rights reserved
53
4.5 Innovative Technology and Systems Development Planning
The following figure illustrates the NASA’s Technology Readiness Level (TRL) Scale. This scale describes
the several phases of an “Innovative Technology and Systems Development Planning Process” linked to a
specific R&D and Engineering Project.
This representation has a general value. It can be applied to any technological and engineering sector
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 among the
different subsystems, components and devices which constitute the overall “System Architecture”.
Interactions are, in many cases, intrinsic multiscale problems
� improve evaluation of the impact of advances in fundamental scientific knowledge over the development
of innovative technology solutions and systems architectures
� 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.
� improve assessment of the Science-Engineering Information 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
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� 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 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 Innovative Technology and Systems Development Planning Framework”. The
term “Virtual” means that:
In both the cases:
� A model of the planned system (and of the hierarchy of sub-systems, components and devices) is
developed using available information that is being organized using the “Multiscale Science –
Engineering Data, Information and Knowledge Management System”. Information are being
progressively increased as we transition from one phase to another one in an incremental way. As data
and information, along the TRL chain, become available from 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 in.
The Model is progressively updated
“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) 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.
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 combined. Several different scenarios can be taken into account and evaluated (What
if Strategy)
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. 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 multiscale 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.
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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.
Furthermore, 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.
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5. Integrated Multiscale R&D and Engineering Infrastructural
Framework
The Integrated Framework described in the previous Chapters is the conceptual and methodological basis to
design a “New Integrated Multiscale R&D and Engineering Infrastructural Framework” which I
constituted by:
� Management Organizations which coordinate the following “Performing and Structural Entities”
− Industrial and Service Companies
− Universities
− Research Centers and Labs
− Computing and Information Centers
− Experimental and Testing Facilities
− Field Monitoring Systems
− Research, Technology Development, Engineering and Management Teams
− Cyberinfrastructures which link together Management Organizations and Performing Entities
Management Organizations and Performing Entities can also be organized in a hierarchical way
The Integrated Multiscale R&D and Engineering Infrastructural Framework foresees:
� New roles and functionalities for some “Performing Entities”
� New Methodological and SW Frameworks which improve the definition and the implementation of
coordinated strategies and activities
Key elements are:
� A New Generation of Cooperative University - Research – Industry Innovation Clusters and
Environments: “Integrated Multiscale Science – Engineering Cyber Extended Enterprise
Frameworks” These are Cooperative Environments where the new IMSE-TPPD Framework and the
related Integrated Multiscale Science – Engineering Framework, proposed in this document, can be
implemented.
− Science –Engineering (or Science – Based) means that the “Integrated Multiscale Science –
Engineering Framework” shape Research, Technology Innovation and Engineering Strategies and
operational activities
− Extended Enterprise means that the IMSE-TPPD Framework shape a new “University – Research
– Industry – Society Cooperative Environments”. 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
− Cyber means that the “Multiscale Science-Based Enterprise” concept is implemented over
“Integrated Multiscale Science – Engineering Knowledge Integrators and Multipliers
Cyberinfrastructural Environments”
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The “Multiscale Science - Based Cyber 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.
� A New Generation of Computational Centres referred to as “ Integrated Computational Multiscale
Multidisciplinary 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 (experimental, testing, sensing) Methodologies. A “two – way”
partnership among the new envisaged Computational Centers and Experimental, Testing and Sensing
Centers 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 Theoretical, Experimental, Testing and Sensing Worlds
New previously illustrated concepts, methods and frameworks lead to a new set of Functionalities for the
Centres:
a) Integrated Environment for jointly (cooperating with Experimental, Testing and Sensing, Teams)
“Designing” Integrated Computational and Experimental, Testing and Sensing Roadmaps and
Strategies
b) Integrated Environment 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 and Knowledge
c) Integrated Environment for the “Design” of new Multiscale Methodologically Integrated Application
Strategies and related Frameworks
� A New Generation of Cyberinfrastructures referred to as “Integrated Multiscale Multidisciplinary
Science – Engineering Knowledge Integrator and Multiplier (KIM) Cyberinfrastructures”
The term “Knowledge Integrators and Multipliers” spurs from the previously quoted New Vision of
Modeling and Simulation. New Cyberinfrastructures represent the “Infrastructural and Technological
Layer” for the Integrated Multiscale Science – Engineering Technology, Product and Process
Development (IMSE-TPPD) 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. This kind of integration is already underway in the computational world,
it is at an early stage in the Experimental, Testing and Sensing Areas. 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 Data, Information and Knowledge Data Bases
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� Teams 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.
Integrated Multidisciplinary Multiscale Science – Engineering Teams (University, Research,
Engineering) working in the full range of methodologies (theory, computation, experimentation,
testing and sensing) and disciplines are made possible, from a technological point of view, by the
new generation of Cyberinfrastructures and by a theoretical and methodological point of view, by
the Integrated Multiscale Science – Engineering and the Integrated Multiscale Science – Engineering
Technology, Product and Process development (IMSE-TPPD) Frameworks.
All of that explains and justifies the term “Knowledge Integrators and Multipliers”.
Cyberinfrastructural Technology should be paralleled by and complemented by the development of new
“Methodological Frameworks” to take full advantage of technological potentialities. We think that
“ Integrated Multiscale Science – Engineering Frameworks” will be instrumental to:
– Design an “Adaptive Architecture” for the New Cyberinfrastructures: what computational,
experimental, testing and sensing facilities with what functionalities should be connected for specific
tasks and purposes and Define related Cyberinfrastructures – Based Methodologically Integrated
Strategies“ to coordinate the use of a full spectrum of resources and methodologies.
What has been described in the above can give a “New Meaning”, from a Theoretical and Application
point of view, to the “Knowledge and Innovation Communities” (KIC) term already used by the European
Institute of Innovation and Technology (EIT)
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Biography
Alessandro Formica. Formica has thirty years of experience in Computer-Aided Engineering, High
Performance Computing, Modeling and Simulation and R&D and Engineering Project Analysis and Design.
During his career his worked for ARS S.p.A. (ENI Group R&D and Engineering Company) as 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), RCI Consulting
Company (Scientific Director); Executive Office of US President (Consultant); European Space Agency
(Consultant); Alenia Space (Consultant); CSCS (Consultant); Daimler Group (Consultant); Alenia
Aeronautica (Consultant); Polytechnic of Milan (Consultant); Polytechnic of Turin (Consultant),. Presently
he is EUMAT Platform Working Group 2 Modeling and Simulation active member. Polytechnic of Turin
School of Doctorate Lecturer for Multiscale Science – Engineering Integration.
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 – October 2010 All rights reserved
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Contacts
Alessandro Formica
Via Piazzi, 41
10129 Torino
Italy
Phone. +39 338 71 52 564
E-mail : [email protected] and [email protected]