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Understanding and Improving Materials Processing Through Interpreting and Manipulating Predictive Models

Published online by Cambridge University Press:  15 February 2011

Robert J. Kee ,
Aili Ting  and
Paul A. Spence
Robert J. Kee
Affiliation:
Sandia National Laboratories, Computational Mechanics Dept., Livermore, CA 94551
Aili Ting
Affiliation:
Sandia National Laboratories, Computational Mechanics Dept., Livermore, CA 94551
Paul A. Spence
Affiliation:
Sandia National Laboratories, Computational Mechanics Dept., Livermore, CA 94551
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Abstract

Most physically based modeling software accepts input in the form of geometry definition, physical parameters, initial conditions, and boundary conditions; and then, on the basis of solving physical conservation equations, predicts the steady-state or transient behavior of a system or process. There is a growing need to create software tools that can themselves control or manipulate the physically based models in certain ways to enhance the usability of models for equipment design and process optimization. These required tools can be described broadly in the following categories: sensitivity analysis, parameter estimation, inverse problems, dynamic optimization, and real-time control. This paper discusses generally the development and application of such modeling tools, drawing examples from a specific RTP reactor design. These techniques accelerate significantly the optimal design of processes and the concurrent engineering of real-time process-control algorithms.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 363 , 1994 , 3
Copyright
Copyright © Materials Research Society 1995

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