Table of Contents - Research Developmentresearchdevelopment.byu.edu/wp-content/uploads/2013… ·...
Transcript of 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 [email protected](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 [email protected] (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
(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 [email protected](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 [email protected](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 [email protected](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 [email protected](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…