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*STTR Project: Development of PUNDA (Parametric Universal Nonlinear Dynamics Approximator) Models for Self-Validating Knowledge-Guided Modeling of Nonlinear Processes in Particle Accelerators and Industry—Pavilion Technologies, Inc., 10415 Morado Circle, Building #3, Suite 100, Austin, TX 78759; 512-438-1560, www.pavtech.com
Dr. Bijan Sayyar-Rodsari, Principal Investigator, bijan@pavtech.com
Mr. Ralph T. Carter, Business Official, rcarter@pavtech.com
DOE Grant No. DE-FG02-04ER86225
Amount: $650,000
Research Institution
SLAC,
The difficult problems being tackled in the accelerator community
are those that are nonlinear, substantially unmodeled, and vary over time. These problems would be ideal candidates for
model-based optimization and control if representative models could be
developed. Such models must be
inexpensive to deploy and maintain, and must remain valid throughout the
operating region of the system and through variations in system dynamics. This project will develop methodology and
algorithms for building high-fidelity mathematical representations of complex
nonlinear systems via a combination of first-principles and neural network
models. In
Phase I, empirical data and
first-principles information were used to train a combined a neural network
model and nonlinear parametric model
through constrained nonlinear optimization. The combined model was applied to three
challenging problems (local orbit correction in electron storage rings and
colliders, beamline model verification in accelerators, and gas composition
model in gas-phase polymerization reactors), demonstrating both accuracy and
computational efficiency. In
Commercial Applications and Other Benefits as described by the awardee: The new software product should allow the modeler to easily use first-principles information, process data, and operator know-how to build high-fidelity models to address current and future needs in process industries and high-energy physics.