Bayesian Estimation of Temperature-Dependent Thermophysical Properties and Transient Boundary Heat Flux

In this article, we apply a Bayesian approach for the simultaneous identification of volumetric heat capacity, thermal conductivity, and boundary heat flux, in a one-dimensional nonlinear heat conduction problem. The Markov chain Monte Carlo sampling approach, implemented in the form of the Metropol...

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Bibliographic Details
Published in:Heat transfer engineering 2010-06, Vol.31 (7), p.570-580
Main Authors: Mota, Carlos A. A., Orlande, Helcio R. B., De Carvalho, Mario Olavo M., Kolehmainen, Ville, Kaipio, Jari P.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Boundaries
Computer simulation
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Heat conduction
Heat conductivity
Heat flux
Heat transfer
Markov analysis
Mathematical models
Monte Carlo methods
Monte Carlo simulation
Physical properties
Physics
Temperature
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Summary:In this article, we apply a Bayesian approach for the simultaneous identification of volumetric heat capacity, thermal conductivity, and boundary heat flux, in a one-dimensional nonlinear heat conduction problem. The Markov chain Monte Carlo sampling approach, implemented in the form of the Metropolis-Hastings algorithm, was used for the solution of the inverse problem. Simulated temperature measurements were used in the inverse analysis in order to examine the accuracy and stability of the overall approach. Independent measurement data were used to construct the prior model for the coefficients to be estimated. The approach is also applied to experiments involving the heating of a reference material with an oxyacetylene torch.
ISSN:0145-7632
1521-0537
DOI:10.1080/01457630903425635