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Space-variable thermophysical properties identification in nanocomposites via integral transforms, Bayesian inference and infrared thermography

Simultaneous estimation of space-variable thermal conductivity and heat capacity in heterogeneous samples of nanocomposites is dealt with by employing a combination of the generalized integral transform technique (GITT), for the direct problem solution, Bayesian inference as implemented with the Markov chain Monte Carlo (MCMC) method, for the inverse analysis and infrared thermography, for the temperature measurements. Another aspect of the proposed approach is the integral transformation of the thermographic experimental data along the space variable, which allows for a significant data compression since the inverse analysis is undertaken within the transformed field. Results are presented for the covalidation of the experiment with a homogeneous polyester plate, as well as for a plate made of polyester–alumina nanoparticles composite with abrupt variation of the filler concentration.