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Optimal Regularization Methods for Inverse Heat Transfer Problems

[+] Author Affiliations
Kei Okamoto, Ben Q. Li

Washington State University, Pullman, WA

Paper No. HT-FED2004-56395, pp. 825-834; 10 pages
doi:10.1115/HT-FED2004-56395
From:
  • ASME 2004 Heat Transfer/Fluids Engineering Summer Conference
  • Volume 4
  • Charlotte, North Carolina, USA, July 11–15, 2004
  • Conference Sponsors: Heat Transfer Division and Fluids Engineering Division
  • ISBN: 0-7918-4693-8 | eISBN: 0-7918-3740-8
  • Copyright © 2004 by ASME

abstract

The Tikhonov regularization method has been used to find the unknown heat flux distribution along the boundary when the temperature measurements are known in the interior of a sample. Mathematically, the inverse problem is ill-posed, though physically correct, and prone to instability. This paper discusses the fundamental issues concerning the selection of optimal regularization parameters for inverse heat transfer calculations. Towards this end, a finite-element-based inverse algorithm is developed. Five different methods, that is, the maximum likelihood (ML), the ordinary cross-validation (OCV), the generalized cross-validation (GCV), the L-curve method, and the discrepancy principle, are evaluated for the purpose of determining optimal regularization parameters. An assessment of these methods is made using 1-D and 2-D inverse steady heat conduction problems where analytical solutions are available. The optimal regularization method is also compared with the Levenberg-Marquardt method for inverse heat transfer calculations. Results show that in general the Tikhonov regularization method is superior over the Levenberg-Marquardt method when the input data errors are noisy. With the appropriately determined regularization parameter, the inverse algorithm is applied to estimate the heat flux of spray cooling of a 3-D microelectronic component with an embedded heating source.

Copyright © 2004 by ASME
Topics: Heat transfer

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