presentation summary: design and optimization group nsf/doe/apc workshop: the future of modeling in...
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Presentation Summary:Design and Optimization Group
NSF/DOE/APC Workshop:
The Future of Modeling in Composites Molding Processes
June 9-10, 2004
VisionThe development and implementation of a comprehensive composites design environmentthat generates the geometric configuration, component materials, and processing schedule for industrial products. Designtool to be based on validated simulations, and address uncertainty in the product’s use, its processing, and models used to assess each, and provide desirable performance over its entire life cycle.
Composite Design AttributesComposite Design AttributesUsability
ExtendibilityDurability
Dimensional stabilityReliability
ManufacturabilityServiceabilityRecycle abilityDisposability
etc…
cradleto
gravelengthscale
Product Design
Process Design
Material Design
Product/Process Design ExampleIntegrated product and process design for short fiber reinforced Integrated product and process design for short fiber reinforced
polymer compositespolymer composites
• Stiffness and strength defined by fiber direction during manufacturingStiffness and strength defined by fiber direction during manufacturing
• IPPD enabling technologiesIPPD enabling technologies
mold fillingfiber orientation
material propertiesproduct performance
polymermelt flowanalysis
staticstress
analysis
modalanalysis
thermalstress
analysis
moldfilling
simulation
fiberorientationprediction
materialproperty
calculation
mold coolinganalysis
warpagesimulation
numericaloptimization
designsensitivityanalysis
multidisciplinarydesign
methodologies
structuraloptimization
State of the Art• Numerous software / algorithms available for numerical optimization
– VDoc/DOT, ISight, Hyperopt, LMS Optimus, Dakota, IMSL, Excel, Matlab, IMSL, Minpack, etc….
• Structural optimization well established– Sizing, Shape, and Topology
• Metamodeling techniques reduce cost of simulation-based design• Enterprise-Driven Multidisciplinary Design Optimization (MDO) developed
for niche applications, e.g., aeroelasticity, automotive body structure, etc…
• Non-deterministic approaches address uncertainty in design– Reliability Analysis Methods, Robust Design,
Reliability-Based Design, etc…• Optimization and design sensitivity analysis
methods developed for numerous manufacturing applications
MPP
f(u1, u2)
u1
u2
g=0
0
0.5
1
0
0.5
10
200
400
Perceived Gaps• Common language needed across materials scientists, product
designers, manufacturing process engineers, etc.• Validated models needed for all aspects of composites processing
– E.g., strength and stiffness prediction from flow simulation• Design sensitivities not developed to level of analyses
– Fiber orientation– Mechanical properties from process models– Non-isothermal flow, reactive flow
• Integrated design methodologies not available to end user• Optimal design applications are task or discipline focused
– I.e., Multidisciplinary design methods rarely not applied to composite molding problems
• Nondeterministic approaches not applied to composite molding problems
Future Research• Further develop/validate composite molding process/product models and
validate optimization results
• Development of language/representations for seamless communication
• Efficient optimization methods that incorporate multidisciplinary variable-fidelity simulation models
• Development of a user-oriented composites molding design environment– Incorporate design knowledge and experience– Further develop DSA methods for composites molding– Incorporate multidisciplinary design methodologies– Incorporate design under uncertainty tools– Include process control in optimal process design
• Application / Validation on industrial scale problems under distributed and collaborative design environment
1.1. Address clearly the heterogeneous nature Address clearly the heterogeneous nature of composite materialsof composite materials
2. Identify “defects” or “features” of interest for modeling and design
- porosity- texture
- interface imperfections - fiber clustering
- fiber misalignment
3. Develop “metamodels” expressing the effect of microstructure on “performance” or “properties”
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.4
0.5
0.6
0.7
0.8
0.9
1.0
n = 0.70
n = 0.12
Kef
f/Khe
x
(dm-D)/
hex
Multi-scale modeling and topology optimization
Experimental/modeling approach
• Evolve from experimentally-based empirical models physics-based models
• Reduce number of experiments required to validate models• Length scales for homogenization
Some Specific Topics• Micromechanics
– Process micromechanics: Effects of fiber content, length on the rheology and fiber orientation
– Micromechanics of materials: Homogenization accounts for interaction between constituents and defects
• Continuum mechanics: Need of constitutive models for– Fatigue– Time dependent behaviors (creep, relaxation,..)– Impact– Moisture– Crashworthiness
• Nonlinear behaviors– Minimization of damage– Improvement of durability (fatigue, creep)
Operation count Memory storage Traditional BEM assembly
O(N2)
O(N)
solution O(N3) (direct solver) O(N) (iterative solver)
O(N2)
= 2~3
Fast Multipole-Accelerated BEM assembly
NA
NA
Solution O(NlogN) (far field shift) O(N) (far + near shift)
O(N) *
The limiting factor for large scale BEM simulations is the memory requirement of O(N2), which easily becomes excessive as the problem size increases. Fast Multipole-Accelerated BEM can reduce both operation count and memory to O(N) level.
Specific numerical issues in BEM
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