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Award Abstract #0121667
ITR/AP COLLABORATIVE RESEARCH: Real Time Optimization for Data Assimilation and Control of Large Scale Dynamic Simulations
| NSF Org: |
CCF
Division of Computer and Communication Foundations
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| Initial Amendment Date: |
September 21, 2001 |
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| Latest Amendment Date: |
September 21, 2001 |
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| Award Number: |
0121667 |
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| Award Instrument: |
Standard Grant |
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| Program Manager: |
Eun K. Park
CCF Division of Computer and Communication Foundations
CSE Directorate for Computer & Information Science & Engineering
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| Start Date: |
October 1, 2001 |
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| Expires: |
September 30, 2007 (Estimated) |
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| Awarded Amount to Date: |
$1145000 |
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| Investigator(s): |
Lorenz Biegler lb01@andrew.cmu.edu (Principal Investigator)
Omar Ghattas (Co-Principal Investigator)
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| Sponsor: |
Carnegie-Mellon University
5000 Forbes Avenue
PITTSBURGH, PA 15213 412/268-8746
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| NSF Program(s): |
ITR MEDIUM (GROUP) GRANTS,
PROCESS & REACTION ENGINEERING
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| Field Application(s): |
0000099 Other Applications NEC
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| Program Reference Code(s): |
HPCC, 9216, 1687, 1652
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| Program Element Code(s): |
1687, 1403
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ABSTRACT
This project will create and apply algorithms and software tools for on-line simulations that continuously (1) assimilate sensor data from dynamic physical processes, and (2) generate optimal strategies for their control. A number of critical industrial, scientific, and societal problems stand to benefit from this research such as aerodynamics, energy, geophysics, infrastructure, manufacturing, medicine, chemical process and environmental applications; two of these will be the focus of the current research. In these and many other cases, the underlying models have become capable of sufficient fidelity to yield meaningful predictions, provided unknown parameters (typically initial/boundary conditions, material coefficients, sources, or geometry) can be estimated appropriately using observational data.
The critical step is the solution of a large-scale nonlinear optimization problem that is constrained by the simulation equations, typically PDEs or their reduced order models. A data assimilation phase will seek to minimize the mismatch between sensor data and model-based predictions by adjusting unknown parameters of the PDE simulation, and the optimal control phase will find an optimal control strategy based on the updated model.
Despite advances in hardware, networks, parallel PDE solvers, large-scale optimization algorithms, and real-time ODE optimization, significant algorithmic and software challenges must be overcome before the ultimate goal of real-time PDE data assimilation and optimal control can be realized. Needed are fundamentally new PDE optimization algorithms that must: (1) run sufficiently quickly to permit decision-making at time scales of interest; (2) scale to the large numbers of variables and constraints that characterize PDE optimization and processors that characterize high-end systems; (3) adjust to different solution accuracy requirements; (4) target time-dependent objectives and constraints; (5) tolerate incomplete, uncertain, or errant data; (6) be capable of bootstrapping current solutions; (7) yield meaningful results when terminated prematurely; and (8) be robust in the face of ill-posedness.
To create, apply, and disseminate the enabling technologies for real-time PDE data assimilation and optimal control, the project will: (1) Develop algorithms and tools for real-time data assimilation and optimal control that meet the above specifications for a class of important applications. (2) Implement and publicly distribute these algorithms within an object-oriented framework that incorporates problem structure, interfaces easily with high performance PDE solver libraries fosters applicability of our tools to a broad range of real-time data assimilation and optimal control problems, and enables extension of the algorithms without interfering with applications. (3) Apply these algorithms and tools to two critical environmental and industrial problems: modeling and control of chemical vapor deposition (CVD) reactors and of wildland firespread. (4) Interact and work with other user communities to ensure that the algorithms and software we produce are useful across a broad range of applications.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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(Showing: 1 - 10 of 28)
(Showing: 1 - 28 of 28)
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Biegler, LT. "A Survey of Simultaneous Dynamic Optimization Strategies," Chemical Engineering and Processing, v.46, 2007, p. 1043.
D. A. Knoll & D. E. Keyes,. ""Jacobian-free Newton-Krylov Methods:
A Survey of Approaches and Applications"," Journal of Computational Physics, v.193, 2004, p. 357.
F. Abraham, M. Behr, and M. Heinkenschloss. ""The Effect of
Stabilization in Finite Element Methods for the Optimal Boundary
Control of the Oseen Equations",," Finite Elements in Analysis and
Design., v.41, 2004, p. 229.
F. Abraham, M. Behr, M. Heinkenschloss. "The effect of stabilization
on the optimal control of the Oseen equations," Lecture Notes in
Computational Science and Engineering, v.40, 2004, p. 589.
G. Biros and O. Ghattas. "Parallel Lagrange-Newton-Krylov-Schur Methods for PDE constrained optimization. Part II: The Lagrange-Newton Solver," SIAM Journal on Scientific Computing, v.27, 2005, p. 687.
G. Biros and O. Ghattas. "Parallel Lagrange-Newton-Krylov-Schur Methods for PDE constrained optimization. Part I: the Krylov-Schur solver," SIAM Journal on Scientific Computing, 2005, p. 7.
Itle, G. C., A. G. Salinger, R. P. Pawlowski, J.N. Shadid and
L. T. Biegler. "A Tailored Optimization Strategy for PDE-based Design: Application to a CVD Reactor," Computers and Chemical Engineering, v.28, 2004, p. 291.
Jockenhoevel, T., L. T. Biegler and A. Waechter. "Dynamic Optimization
of
the Tennessee Eastman Process Using the OptControlCentre," Computers and
Chemical Engineering, v.27, 2003, p. 1513.
Kameswaram, S. and L. T. Biegler. "Simultaneous Dynamic Optimization Strategies: Recent Advances and Challenges," Computers and Chemical Engineering, v.30, 2006, p. 1560.
Kameswaran, S., G. Staus and L. T. Biegler. "Parameter Estimation of Core Flood and Reservoir Models," Computers and Chemical Engineering, 2005, p. 1787.
Laird, C. D., L. T. Biegler, B. van Bloemen Waanders. "A mixed integer approach for obtaining unique solutions in source inversion of drinking water networks," ASCE Journal of Water Resource Management and Planning, v.132, 2006, p. 242.
Laird, C. D., L. T. Biegler, B. van Bloemen Waanders, R. A. Bartlett,. "Time Dependent Contaminant Source Determination for Municipal Water Networks," ASCE Journal of Water Resource Management and Planning, v.1, 2005, p. 125.
Lang, Y-D and L. T. Biegler. "Large-scale nonlinear programming with a CAPE-OPEN compliany interface," Chemical Engineering Research and Design, v.83, 2005, p. 718.
Lang, Y-D and L. T. Biegler. "A Software Environment for Simultaneous Dynamic Optimization," Computers and Chemical Engineering, v.31, 2007, p. 931.
M. Heinkenschloss. "Time-Domain Decomposition Iterative Methods for Solution of Linear Quadratic Optimal Control Problems," Journal of Computational and Applied Mathematics, 2005, p. 16.
M. Heinkenschloss and H. Nguyen. "Neumann-Neumann Domain Decomposition Preconditioners for Linear Quadratic Elliptical Optimal Control," SIAM J. Sci. Comp., 2006, p. 1001.
M. Heinkenschloss and M. Herty. "A Spatial Domain Decomposition Method for Parabolic Optimal Control Problems," Journal of Computational and Applied Mathematics, v.201, 2007, p. 88.
R. Bartlett and M. Heinkenschloss and D. Ridzal and B. van Bloemen. "Domain Decomposition Methods for Advection Dominated Linear Quadratic Elliptic Optimal Control Problems," Comp. Meth. Appl. Mech. Engng., v.19, 2006, p. 6428.
Raghunathan, A. and
L. T. Biegler. "MPEC Formulations and Algorithms
in Process Engineering," Computers and Chemical Engineering, v.27, 2003, p. 1381.
Raghunathan, A., V. Gopal, D. Subramanian, L. T. Biegler and T. Samad,. ""Dynamic Optimization Strategies for 3D Conflict Resolution of
Multiple Aircrafts,"," AIAA J. of Guidance, Control and Dynamics, v.27, 2004, p. 586.
Raghunathan, Arvind, M. Soledad Diaz, Lorenz T. Biegler. ""An MPEC
Formulation for Dynamic Optimization of Distillation Operation,"," Computers and Chemical Engineering, v.28, 2004, p. 2037.
V. Akcelik, G. Biros, and O. Ghattas. "Parallel Multiscale Gauss-Newton-Krylov Methods for Inverse Wave Propagation," Proceedings of Supercomputing 2002, v.1, 2002, p. 143.
V. Akcelik, G. Biros, O. Ghattas, K.R. Long, and
B. van Bloemen Waanders. "A Variational Finite Element Method for
Source Inversion for Convective-Diffusive Transport," Finite
Elements in Analysis and Design, v.2003, 2003, p. 683.
V. Akcelik, O. Ghattas, D. Keyes, K. Ko, L.-Q. Lee, and E. Ng,. "Adjoint methods for electromagnetic shape optimization of the low-loss cavity for the International Linear Collider," Journal of Physics: Conference Series, 2005, p. 4.
Waechter, A. and L. T. Biegler. "Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence," SIAM Journal of Optimization, v.28, 2005, p. 1.
Waechter, A., and L. T. Biegler. "On the Implementation of an Interior Point Filter Line Search Algorithm for Large-Scale Nonlinear Programming," Mathematical Programming,, v.106, 2006, p. 25.
Waechter, Andreas and L. T. Biegler. "Line Search Filter Methods for Nonlinear Programming: Local Convergence," SIAM Journal of, v.16, 2005, p. 32.
X.-C. Cai and D. E. Keyes. "Nonlinearly Preconditioned Inexact Newton Algorithms," SIAM J. Sci. Comp, v.24, 2002, p. 183.
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