Detection of contact failures with the Markov chain Monte Carlo method by using integral transformed measurements

Abstract

This work deals with the solution of an inverse heat conduction problem aiming at the detection of contact failures in layered composites through the estimation of the contact conductance between the layers. The spatially varying contact conductance is estimated using a Bayesian formulation of the problem and a Markov chain Monte Carlo method, with infrared camera measurements of the transient temperature field on the surface of the body. The inverse analysis is formulated using a data compression scheme, where the temperature measurements are integral transformed with respect to the spatial variable. The present approach is evaluated using synthetic measurements and experimental data from controlled laboratory experiments. It is shown that only few transformed modes of the data are required for solving the inverse problem, thus providing substantial reduction of the computational time in the Markov chain Monte Carlo method, as well as regularization of the ill-posed problem.