Inverse estimation of thermal properties using Bayesian inference and three different sampling techniques

TLDR: In this paper, three different thermal properties such as thermal conductivity, heat transfer coefficient and emissivity are retrieved simultaneously using Bayesian inverse framework and two population-based sampling techniques such as Parallel Tempering (PT) and Evolutionary Monte-Carlo (EMC) are used along with MH-MCMC to sample through correlated PPDF to retrieve the above three thermal properties.

Abstract: In this article, three different thermal properties such as thermal conductivity, heat transfer coefficient and emissivity are retrieved simultaneously using Bayesian inverse framework. Metropolis–Hasting Markov Chain Monte-Carlo (MH-MCMC) sampling is more commonly used in the literature to sample through posterior probability distribution function (PPDF) to find the expectations such as mean, standard deviation. However, when the posterior is multi-model/correlated, sometimes MH-MCMC struck with one mode and fails to sample through other modes which have significant probability. Nevertheless, efficient sampling techniques are being developed during the last decade to overcome this problem. Therefore, in the present work two population-based sampling techniques such as Parallel Tempering (PT) and Evolutionary Monte-Carlo (EMC) are used along with MH-MCMC to sample through correlated PPDF to retrieve the above three thermal properties. The estimation is carried out at three levels of measurement errors. Th...