Transitional Markov Chain Monte Carlo for estimation of heat transfer coefficients in a microchannel heat sink
DOI:
https://doi.org/10.5902/2179460X84238Keywords:
Conjugate heat transfer, Internal convection, Microchannels, Inverse problems, Bayesian inference, Transitional Monte Carlo Markov Chain MethodAbstract
This work addresses a Bayesian methodology to solve an inverse heat transfer problem in a microchannel heat sink. The direct problem involves calculating the temperature profile on the plate, as well as the temperature of the fluid flowing in the microchannel, through a simplified mathematical model of the physical system in question. The direct problem was numerically solved using the explicit finite difference method. The inverse problem in question consists of estimating the convective heat transfer coefficients of the system within the Bayesian framework. The Transitional Markov Chain Monte Carlo was used to sample the posterior probability density function of the model parameters. The proposed methodology was evaluated from numerical simulations involving temperature data corrupted with additive noise and different models for the estimated heat transfer coefficients. Numerical analyzes are also presented considering observed temperature data generated from a complete model implemented in software COMSOL Multiphysics. The presented results show that the proposed methodology was able to estimate the heat transfer coefficients in the different considered scenarios.
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