NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	OLD DOMINION UNIVERSITY RESEARCH FOUNDATION
Initial Amendment Date:	September 20, 2001
Latest Amendment Date:	September 20, 2001
Award Number:	0121207
Award Instrument:	Standard Grant
Program Manager:	Tracy Kimbrel CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2001
End Date:	May 31, 2004 (Estimated)
Total Intended Award Amount:	$805,000.00
Total Awarded Amount to Date:	$805,000.00
Funds Obligated to Date:	FY 2001 = $100,276.00
History of Investigator:	David Keyes (Principal Investigator) david.keyes@columbia.edu
Recipient Sponsored Research Office:	Old Dominion University Research Foundation 4111 MONARCH WAY STE 204 NORFOLK VA  US  23508-2561 (757)683-4293
Sponsor Congressional District:	03
Primary Place of Performance:	Old Dominion University HAMPTON BLVD NORFOLK VA  US  23529-0001
Primary Place of Performance Congressional District:	03
Unique Entity Identifier (UEI):	DSLXBD7UWRV6
Parent UEI:	DSLXBD7UWRV6
NSF Program(s):	ITR MEDIUM (GROUP) GRANTS
Primary Program Source:	01000102DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	1652, 9216, HPCC
Program Element Code(s):	168700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

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.

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