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Towards CI-enabled, high-fidelity, simulation-based engineering sciences and decision making

Towards CI-enabled, high-fidelity, simulation-based engineering sciences and decision making. Omar Ghattas Ultrascale Computing Lab Biomedical Engineering Civil & Environmental Engineering Carnegie Mellon University.

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Towards CI-enabled, high-fidelity, simulation-based engineering sciences and decision making

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  1. Towards CI-enabled, high-fidelity, simulation-based engineering sciences and decision making Omar Ghattas Ultrascale Computing Lab Biomedical Engineering Civil & Environmental Engineering Carnegie Mellon University

  2. From physical models to simulation-based engineering sciences and decision making physical model of natural or engineered system visualization data mining/science multiscale models validation optimization stochastic models uncertainty quantification mathematical model parameter inversion data assimilation model/data error control Simulation-based decision making e.g. design, control, operations, disaster response, manufacturing, hazard assessment, planning, treatment advanced geometry & discretization schemes data/observations numerical model computer simulation scalable algorithms & solvers approximation error control verification

  3. CyberEngineering CyberInfrastructure From physical models to simulation-based engineering sciences and decision making physical model of natural or engineered system visualization data mining/science multiscale models validation optimization stochastic models uncertainty quantification mathematical model parameter inversion data assimilation model/data error control Simulation-based decision making e.g. design, control, operations, disaster response, manufacturing, hazard assessment, planning, treatment advanced geometry & discretization schemes data/observations numerical model computer simulation scalable algorithms & solvers approximation error control verification

  4. Earthquake Modeling for Seismic Hazard Assessment Volkan Akcelik , Jacobo Bielak, George Biros (UPenn), Steven Day (SDSU), Omar Ghattas, Loukas Kallivokas (Texas), Harold Magistrale (SDSU), David O’Hallaron Region of interest for 1994 Northridge earthquake simulation Adaptive grid resolves up to 1Hz freq. w/100 million grid pts; uniform grid would require 2000x more points SCEC geological model provides 3D soil properties in Greater LA Basin Inversion of surface observations for 17 million elastic parameters (right: target; left: inversion result) Comparison of observation with simulation (improved prediction requires petaflops capability) Snapshot of simulated ground motion (simulation requires 3hr on 6Tflops PSC machine, running at >80% parallel eff)

  5. Multiscale Blood Flow Modeling for Artificial Heart Device Design James Antaki, Guy Blelloch, Omar Ghattas, Marina Kameneva (Pitt), Robert Kormos (Pitt), Gary Miller, K. Rajagopal (Texas A&M), George Turkiyyah (Washington), Noel Walkington At macroscopic (device) scales: • Development of artificial heart assist device at Univ Pitt Med Center (Antaki) • Numerous advantages (size, power, reliability, non-invasiveness) • Design challenge: overcome tendency to damage red blood cells • Need macroscopic blood flow theory that accounts for blood (cell) microstructure At microscopic (cell) scales: • Macroscopic model fails in small-length-scale regions (blade tip, rotor bearing) • Need modeling at cell scales to account for blood damage • Our mesoscopic simulations resolve interaction of RBCs elastic membrane with plasma fluid dynamics • Prospects for 3D simulation of blade-tip region: 1 week at sustained 1 petaflops/s

  6. Real time optimization for dynamic inversion & control Volkan Akcelik, Roscoe Bartlett (Sandia), Lorenz Biegler, George Biros (UPenn), Frank Fendell (TRW), Omar Ghattas, Matthias Heinkenshloss (Rice), David Keyes (Columbia), John Shadid (Sandia), Bart van Bloemen Waanders (Sandia), Andreas Wachter (IBM), David Young (Boeing) Inversion and control for airborne contaminant transport • sensor data provides concentrations of hazardous agents • inverse problem solved to reconstruct initial conditions • control problem solved to find optimal remediation strategy Water network contaminant inversion • Nonlinear optimization problem with >300K variables and >100k controls • Solution time < 2 CPU minutes  real time source detection • Algorithm successful on thousands of numerical tests on several municipal water networks • Formulation tool links to existing modeling software (EPANET) and powerful NLP solver (IPOPT)

  7. Workshops on key drivers/technologies for CI • Simulation-based engineering sciences (SBES) • NSF-sponsored workshop held April 2004 • Report identifies key enabling technologies and challenges (multiscale modeling, V&V, large-scale simulation) • Identifies strategic applications poised to benefit from SBES/CI (medicine, energy & environment, national security, manufacturing, materials) • Dynamic data-driven application systems (DDDAS) • NSF-sponsored workshop in 2000 • Focuses on hardware/software/algorithms/models for integration of dynamic data with online simulations • ~20 ITR medium projects address aspects of DDDAS • Science Case for Large Scale Simulation (SCaLeS) • DOE-sponsored workshop June 2003 • Identifies needs and opportunities for large-scale simulation across DOE Science areas • Vol 1 report available online; Vol 2 to be published by SIAM

  8. SBES Report: Example applications Simulation-based planning for vascular bypass surgery. From left: MRI image data, preoperative geometric solid model, operative plan, computed blood flow velocity in aorta and proximal end of bypass, and postoperative image data used to validate predictions. Simulation-based medicine (C. Taylor, Stanford) Biomimetic Devices: functionalized carbon nanotube to mimic biological ion channels (N. Aluru, UIUC) Multi-scale design of a composite component of an aircraft (SCOREC, RPI)

  9. Summary • CI will help catalyze a transformation to high-fidelity simulation-based engineering science and decision-making • But hardware/middleware infrastructure alone is insufficient to achieve this goal • Simultaneous advances on the models, methods, and algorithms that underpin the components – and on their systematic integration to target strategic applications – are crucial for realizing the potential of CI

  10. Final editorial comment • Advances in models and algorithms have often to led to greater improvements in simulation capability than improvements in hardware Example from magnetohydrodynamics: 2.5 orders of magnitude from hardware improvements; 3.5 orders of magnitude from modeling and algorithmic advances (From SCaLeS report, Vol 2 & SBES rpt)

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