GRADS 2006 Petr Krysl
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Transcript of GRADS 2006 Petr Krysl
8/8/2019 GRADS 2006 Petr Krysl
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P. Krysl 04/08/06
Simulation:
To infinity and beyond.
Petr Krysl
http://Hogwarts.ucsd.edu/~pkrysl
8/8/2019 GRADS 2006 Petr Krysl
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P. Krysl 04/08/06
Simulation-based engng. science
Payoff: • Medicine
• Predictive Homeland Security • Energy and the Environment Materials • Industrial and Defense Applications Issues: • The Tyranny of Scales: The Challenge of Multiscale Modeling and Simulation• Verification, Validation, and Uncertainty Quantification• Dynamic Simulation Systems, Sensors, Measurements, and Heterogeneous Simulations • Big Data in Simulation and the Role of Visualization in SBES • Next-Generation Algorithms and Computational Performance
Payoff: • Medicine
• Predictive Homeland Security • Energy and the Environment Materials • Industrial and Defense Applications
Revolutionizing Engineering Science through SimulationFebruary 2006 Report of the National Science FoundationBlue Ribbon Panel on Simulation-Based Engineering Science
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P. Krysl 04/08/06
Engineering Design
DesignDescription
PrototypeHardware
Function/ Behavior
Customer Preferences Technical Specifications
Design Synthesis
Modeling and Simulation
Fabrication Operation
Product
Manufacturing
Refinement
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P. Krysl 04/08/06
Predictive?
Is this simulation capability predictive?
From: O’Brien et al. 1999
Experiment
Simulation
Not in this case: non-physical material properties had been used to make it
“look” right (computer graphics ;).
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P. Krysl 04/08/06
Modeling
Physical problem
Mathematical idealization
Numerical approximation
Accurate solutionof mathematical model?
Agreement w/ physics of problem?
Equations,assumptions
Discretization(finite elements),algorithms (solvers)
Discretizationerrors
Conceptual errors
Transformationerrors
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P. Krysl 04/08/06
Simulation Errors
Errors:Modeling Conceptual (omitted physics, range, …)
ApproximationDiscretization (space, time); and Transformation (finite-precisionarithmetics!)
Error control:Quantify (error estimation), and Minimize (adaptivity).
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P. Krysl 04/08/06
Cost of Simulations
Asymptotic behaviour Example: linear statics Assembly of K: O(N),solution of Kx=f: O(N 2 ).
Total cost = aN 2 + bN + c Fixed budget (given grid)
Select modes as linear
combinations of hat functions
log(N) l o g
( E r r o r
) pCN e
−
≈
log(#modes) l o g
( E r r o r )
optimal
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P. Krysl 04/08/06
RC Bridge under seismic loads
El Centro accelerogramIsotropic damage model
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P. Krysl 04/08/06
RC Bridge: continued
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P. Krysl 04/08/06
Monte Carlo Simulation
Statistics of theresponse due to
uncertainty?
Notched plate with uncertain material properties under low-cycle fatigue load
Displacement of theloaded face
Contour of plastic deformation at the end of the load cycling
Copper plate 24x20x2 mm.Finite element model of 1/8 of the plate: 415 hexahedra. J-2 elastoplastic material.Elastic and hardening modulus, yield stress areuncertain with standard deviation 0.025.
Evolution of theequivalent plastic strain at the notch
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P. Krysl 04/08/06
Monte Carlo Simulation: cont
Plastic strain at the notch:mean value
Plastic strain at the notch:
standard deviation
Reduced dynamic model :3 Ritz modes yield accuracy of plastic strain better than 2%.
Full FE model: 3,250 CPU sec Reduced FE model: 170 CPU sec
Monte Carlo: Vary thematerial properties for eachrepeated run. Problem: may have to run thousands of simulations. Hence: use an
optimal (reduced) model .
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P. Krysl 04/08/06
Bioacoustics
Forced vibration of the anatomical structures (neonate Z. Cavirostris).Planar acoustic waves in seawater of 3500 Hz and 180 dB re: 1µPa received level. Finite element model with 2 ½ million elements (resolution of 3.61 mm). Small displ., small strain kinematics. Almost incompressible elasticity, coupled with Newtonian viscosity (muscle,acoustic fats, connective tissues, and bone). Material properties heterogeneous -- mapped from Hounsfield units.
Horizontal slice.Pressuredistributionsnapshot.
Green:pterygoidsinuses; red:outline of thebones; purple:ear bones.
Vertical slices.Dissipated energy (heat).
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