table of contents - research developmentresearchdevelopment.byu.edu/wp-content/uploads/2013… ·...

Page # Title Name Department

2-9 Biomedical, Biochemical, and Global Engineering Randy Lewis Chemical Engineering

10-27 Tissue Engineering and Regenerative Medicine Alonzo Cook Chemical Engineering

28-37 Ultrasound Activated Drug Delivery Bill Pitt Chemical Engineering

38-44 Multi-Scale Simulation of Turbulent Reacting Flow David Lignell Chemical Engineering

45-54 Dynamic Optimization John Hedengren Chemical Engineering

55-61 Earthquake Resistant Buildings Paul Richards Civil and Environmental Engineering

62-68 Isogeometric Analysis Michael Scott Civil and Environmental Engineering

Table of Contents

Biomedical, Biochemical, and Global Engineering

Randy S. LewisChemical Engineeringrandy.lewis@byu.edu(801) 422‐7863

Areas of Interest: Sustainable Energy; Biofuels Production; Biomaterials; Biological Interactions With Nitric Oxide; Global Engineering (emphasis on developing nations)

Randy S. Lewis Chemical Engineering

Biomedical, Biochemical, and Global Engineering

1. Nitric Oxide (NO)

Nitric Oxide Research

• NO cellular kinetic modeling• Kinetics of NO-releasing compounds• Studies of reaction intermediates

(superoxide, …) on cells• Platelet adhesion inhibition via NO• Novel NO-releasing polymers• In-vitro NO analysis• NO effects on artificial pancreas

2. Biofuels Production

Grinding Gasification

(or syngas)

GrindingLignocellulose

GasificationProducer gas

Acid hydrolysis orChemical/Physical

disruption

FermentationLignocellulose Biofuel

Milling Liquefaction Saccharification

Saccharification

Starch/sugars

sugars

sugars

Acid hydrolysis orChemical/Physical

disruption

Distillation/other separation

processes

Milling Liquefaction Saccharification

Saccharification

Starch/sugars

sugars

MetalCatalysis

Biofuels Research

Focus: Fermentation

• Modeling of enzyme kinetics and inhibition

• Gas impurity studies• Thermodynamic analysis of

pathways• Studies of electron

mediated processes• Reactor design and mass

transfer analysis

3. Global Engineering

Tonga 2007 Ghana 2009 Peru 2008-13

• Emphasis: Developing nations• Course: Jr/Sr project course• Trip: 2-week implementation/ follow-up• Research: Cookstove design with UNP

Projects

Biodiesel Solar cooking Water heating Water purification Pump Washing machine Windmill Biofilter toilet Reed cutter Ovens Cookstoves

Tissue Engineering and Regenerative Medicine

Alonzo D. Cook, PhDChemical Engineeringcook@byu.edu (801) 422‐1611 

Areas of Interest:Biomedical Engineering; Vascular Engineering; Tissue Engineering; Regenerative Medicine

Tissue Engineering and Regenerative Medicine

Alonzo D. Cook, PhDAugust 26, 2013

Spinal Cord

Upper and Lower Jaw

Limb

Retina and LensTailHeart

Adapted from BrockesAdapted from Brockes

The NewtTime lapse of 20 days

Nature’s Paradigm for Tissue Regeneration

Vascular Engineering

Let’s Grow Organs from Cells and Scaffolds

Tissue Regeneration EssentialsBioActivesRecombinant proteinPeptide factorsPlatelet rich plasmaSmall molecules

ScaffoldsSyntheticCollagenCeramicNitinol

Allo/Xenograft

Responding CellsMature cells

Progenitor cellsStem cells

Industry Examples of Regenerative Medicine

• Skin Living Tissue– Advanced Biohealing– Organogenesis

• Bone Growth Factor– Medtronic

• Cartilage Autologous Cells– Genzyme Tissue Repair

Living Skin

• Organogenesis• Advanced Biohealing

Spine Fusion

Autologous Chondrocyte Implantation

Decellularized Matrices: Heart

Cook Lab:  Decellularized Rat Heart

Cook Lab:  Decelluarized Bovine Heart

3D Printing

Building Blood Vessels

• Heart• Kidney• Eye• Nerve• Blood Vessel

Cook Lab Projects

Ultrasound Activated Drug Delivery

Bill Pitt, PhD

Chemical Engineering

pitt@byu.edu

(801) 422-2589

Areas of Interest: Triggered and targeted drug delivery in cancer treatment via phase changing emulsions that change from liquid to gas upon application ultrasound or near infrared light. Also, transport of small molecules in contact lenses to release drugs to the eye.

Bill Pitt

Chemical Engineering

Ultrasound Activated Drug Delivery

• Ultrasound o Focused pressure

waves cause bubbles to expand and contract, creating high shear stresses and shock waves

CAVITATION:It come in

two varieties

High FrequencyHigh Voltage

Power Fluxor

Power Densityor

Intensity

W/cm2

Ferrara et al., IEEE, 2005

Ultrasound can cause phase change

eLiposomes• Phase changing nanoemulsion inside a liposome

Fig1.eLiposomebycryoTEM.

James Lattin, Marjan Javadi, Dr. David Belnap

In vitro drug release with eLiposomes100 mspulse of 20 kHz ultrasound

1 W/cm2

of 20 kHzultrasound

James Lattin, submittedUltrasound Med. Biol.

Large emulsion inside

Small emulsion inside

Large emulsion outside

Small emulsion outside

No emulsion anywhere

Sham control

Release to the Cytosol Only • We put an active targeting ligand on the eLiposome that

induces endocytosis.

• Rupture of the eLiposome also ruptures the endosome.

HeLa cells with eLiposomes

No emulsions in the liposomes Emulsions in the liposomes

Delivery of the fluorescent molecule calcein using folatedeLiposomes and 20 kHz ultrasound at 1 W/cm2 for 2 seconds.

Javadi et al., J. Controlled Release 2013

Folate, US, and emulsions are required for internal delivery.

Example of Folated Active Delivery

Javadi et al., Langmuir 2013, calcein-containing liposomes, HeLa cells

Liposomes without attached folate Liposomes with attached folate

The calcein is released inside the cell, not outside the cell.

We can delivery plasmids

Confocal image of HeLa cells exposed for 2 hours to ultra eLiposomes containing plasmid, followed by application of 20-kHz ultrasound at 1W/cm2 for 2 seconds. (A) eLiposomes were not folated. (B) eLiposomes contained folate in their phospholipid membrane. (C) Folate receptors were already blocked with extra folate before adding the eLiposomes. Pictures were taken 48 hr after applying the ultrasound.

A. Non-folated. B. Folated eLiposomes C. Competitive binding by folate

Multi‐Scale Simulation of Turbulent Reacting Flow

David LignellChemical Engineeringdavidlignell@byu.edu(801) 422‐1772

Areas of Interest:Modeling Turbulent Nonpremixed Combustion; Soot formation and transport; Flame Extinction and Reignition Processes; Multi‐Phase Flows 

Multi‐Scale Simulation of Turbulent Reacting Flow

David LignellChemical EngineeringAugust 26, 2013

Motivation

• 83% of our energy is from combustion of fossil fuels.

• Goals– Design more efficient processes– Understand key physical phenomena– Limit and control pollutants– Analyse and predict hazards.

Costs and Challenges

• Combustion is turbulent Multi‐scale• 3D simulation cost scales as > Re3

• Example: DNS ethylene jet flame– Sugar cube size domain: O(cm)– Run for 1 ms.– Solve Navier‐Stokes + 19 species chemistry– Cost = 2 MM cpu‐hrs, 14000 procs, 341 MM cells– 2 x domain size  x 10 cost!– Use for research, model development

• Practical flows cannot be resolved Models: RANS, LES

Research

• Multi‐scale approaches for turbulent flows: ODT, LES, ODT• ODT approach: Resolve All Scales in 1‐D

– Solve 1D reaction/diffusion equations– Turbulence modeled by stochastic mapping process– Reach new parameter spaces– Cost effective!  O(100 cpu‐hrs)– No fine‐scale modeling! Line of 

Sight

Time

0 0.05 0.1 0.15

500

1000

1500

2000

Tem

pera

ture

(K)

Distance from wall (m)

232339394747

0.35 m

Applications

0

2

4

6

8

2

4

6

Flame Extinction Soot Formation Wall Fire Channel FlowParticle Deposition

3‐D Extensions

• Grids/Lattices of ODT lines• Autonomous Microstructure Evolution• Capture large‐scale 3D effects, with full 1‐D microscale resolution

Dynamic OptimizationJohn HedengrenChemical Engineeringjohn_hedengren@byu.edu(801) 422‐2590

Areas of Interest: PRISM Group; UAV’s; Systems Biology; Smart Grid Optimization; Arctic Research; C‐UAS I/UCRC

John HedengrenAssistant Professor

Department of Chemical EngineeringIra A. Fulton College of Engineering and Technology

Brigham Young University

26 August 2013

PRISM Group PRISM Group

Methods Mixed Integer Nonlinear Programming (MINLP) Dynamic Planning and Optimization Uncertain, Forecasted, Complex Systems

Research Applications Unmanned Aerial Vehicle (UAV) control Systems biology and pharmacokinetics Oil and gas exploration and production Hybrid and sustainable energy systems

Chemical Engineering

24 July 2013

Same Method, Many ApplicationsSame Method, Many Applications

Standard Problem Formulation

Objective Function (f(x))

Dynamic model equations that relate trajectory constraints, sensor dynamics, and discrete decisions

Uncertain model inputs as unmodeled or stochastic elements

Solve large-scale MINLP problems (100,000+ variables)

4

max

subjectto , , , 0

h , , 0

24 July 2013

Dynamic Optimization with UAVsDynamic Optimization with UAVs

1

2

3

5

Courtesy Sentix Corp

Systems BiologySystems Biology

Objective: Improve extraction of information from clinical trial data

Dynamic data reconciliation Dynamic pharmacokinetic models (large-scale) Data sets over many patients (distributed) Uncertain parameters (stochastic)

05

1015

1.9

1.95

21

2

3

4

5

6

7

Log(

Viru

s)

HIV Virus Simulation

time (years)Log(kr1)

1.828

2.364

2.899

3.435

3.970

4.506

5.041

5.576

6.112

6.647

0 5 10 151

2

3

4

5

6

7

8

Time

log1

0 vi

rus

Smart Grid Optimization

Smart grid integration with solar, wind, coal, biomass, natural gas, and energy storage

Nuclear integration withpetrochemicalproduction, processing, and distribution

Dynamic Energy System Tools

Solid Oxide Fuel Cell (SOFC)

Toolbox for Object Oriented Modeling in MATLAB, Simulink, and Python

Advanced tools are required for collaborative modeling and high performance computing

Optimization BenchmarkOptimization Benchmark

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 510

20

30

40

50

60

70

80

90

100

Not worse than 2 times slower than the best solver ()

Per

cent

age

(%)

APOPT+BPOPTAPOPT1.0BPOPT1.0IPOPT3.10IPOPT2.3SNOPT6.1MINOS5.5

Summary of 494 Benchmark Problems

Speed

Success

Speed and Successwith combined approach

Survey of DAE SolversSurvey of DAE Solvers

Software Package Max DAE Index

Form Adaptive Time Step

Sparse Partial‐DAEs

Simultaneous Estimation / Optimization

APMonitor 3+ Open No Yes Yes Yes

DASPK  / CVODE / Jacobian

2 Open Yes No No No

gProms 1 (3+ with transforms)

Open Yes Yes Yes No

MATLAB 1 Semi‐explicit

Yes No No No

Modelica 1 Open Yes Yes No No

DAE = Differential and Algebraic Equation

Earthquake Resistant Buildings

Paul RichardsCivil and Environmental Engineeringprichards@et.byu.edu(801) 422‐6333

Areas of Interest: Structural Engineering; Materials Engineering; Dissipation of Energy; Dynamic Analysis; Blended Learning in Higher Education (Effective on‐line course materials (videos),Meaningful automated feedback, Implementable solutions); Trying to get Research Initiation Grant Engineering Education (RIGEE)

Paul RichardsEarthquake Resistant Buildings

Civil and Environmental Engineering

Dissipate Energy Through Damage

Structural “Fuse”

Hard to replace a building Easy to replace a car

High Performance Systems Too Expensive

Discovering Economical Alternatives

• Steel frames that can undergo large deformations without yielding

• Steel frames with increased “self‐centering” capabilities

• Use of new materials to dissipate energy without structural damage

• Increasing inherent damping

Tools

• Dynamic analysis – Open‐source program (OpenSees)

– Tool Command Language (tcl)

– Genetic Optimization

• Component Level Experimental Testing

Other Research Interests

• Blended Learning in Higher Education– Effective on‐line course materials (videos)– Meaningful automated feedback– Implementable solutions

• Trying to get Research Initiation Grant Engineering Education (RIGEE)

Isogeometric Analysis

Michael A. ScottCivil and Environmental Engineeringmichael.scott@byu.edu(801) 422‐6324

Areas of Interest: Isogeometric Analysis

Isogeometric Analysis

Michael A. ScottCivil and Environmental Engineering

Brigham Young University

Collaborators: Derek Thomas (BYU), Emily Evans (BYU), Kevin Tew (BYU), Thomas W. Sederberg (BYU), Xin Li (USTC), Laura de Lorenzis (Salento), Robert Simpson (Cardiff), Matthias Taus (UT Austin), Tom Hughes (UT Austin), Jessica Zhang (CMU), Lei Liu (CMU), John Evans (UC Boulder), 

Dominik Schillinger (UMN), Yuri Bazilevs (UCSD)

Courtesy of General Dynamics / Electric Boat Corporation 

The Big Picture

Based on technologies (e.g., NURBS, T‐splines, etc.) from computational geometry used in: Design  Animation Graphic art Visualization

Includes standard FEA as a special case, but offers other possibilities: Precise and efficient geometric modeling Simplified mesh refinement Smooth basis functions with compact support Superior approximation properties Integration of design and analysis

Isogeometric Analysis

Lots of Cool Applications…

Lots of Cool Applications…

Lots of Cool Applications…