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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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References

1. Meeks, E., Kee, R.J., Dandy, D.S. & Coltrin, M.E., Combust. and Flame 92, 144–160 (1993).10.1016/0010-2180(93)90204-GGoogle Scholar
2. Spence, P.A., Winters, W.S., Kee, R.J. & Kermani, A., The Application of Computational Simulation to Design Optimization of an Axisymmetric Rapid Thermal Processing System, (2nd International Rapid Thermal Processing Conference, RTP’94, Round Rock, TX, 1994) pp. 139–145.Google Scholar
3. Mason, W.E., TACO3D – A Three-Dimensional Finite-Element Heat-Transfer Code, (Sandia National Laboratories, SAND83–8212, 1983).Google Scholar
4. Ting, A., The Influence of Band-Dependent Radiation in Simulation of Lamp-Heated Rapid Thermal Processing Systems, (2nd International Rapid Thermal Processing Conference, RTP’94, Round Rock, TX, 1994) pp. 102–109.Google Scholar
5. Evans, G.H. & Greif, R., Int. J. Heat Mass Transfer 37 (10), 1535–1543 (1994).Google Scholar
6. Kee, R.J., Miller, J.A., Evans, G.H. & Dixon-Lewis, G., A Computational Model of the Structure and Extinction of Strained, Opposed Flow, Premixed Methane-Air Flames, (Twenty-second Symposium (International) on Combustion, The Combustion Institute, 1988) pp. 1479–1494.Google Scholar
7. Norman, S., Ph.D. thesis, Stanford University, 1992.Google Scholar
8. Cho, Y.M. & Kailath, T., IEEE Trans. on Semiconductor Man. 6 (3), (1993).Google Scholar
9. Petzold, L.R., A Description of DASSL: A Differential/Algebraic Solver, (Sandia National Laboratories, SAND82–8637, 1982).Google Scholar
10. Sato, T., Jap. J. Appl. Phys. 6, 339 (1967).Google Scholar
11. Coltrin, M.E., et aL, SPIN (Version 3.83): A Fortran Program for Modeling One-Dimensional Rotating-Disk/Stagnation-Flow Chemical Vapor Deposition Reactors, (Sandia National Laboratories, SAND91–8003, 1991).Google Scholar
12. Grcar, J.F., Kee, R.J., Smooke, M.D. & Miller, J.A., A Hybrid Newton/Time-Integration Procedure for the Solution of Steady, Laminar, One-Dimensional, Premixed Flames, (Twenty-first Symposium (International) on Combustion, The Combustion Institute, 1986) pp. 1773–1782.Google Scholar
13. Grcar, J.F., The Twopnt Program for Boundary Value Problems, (Sandia National Laboratories, SAND91–8230, 1991).Google Scholar