This paper deals with the solution of an inverse heat conduction problem, aiming at the identification of the interface thermal contact conductance, which can be directly associated to the quality of the adhesion between layers of multilayered composite materials. The inverse problem is solved within the Bayesian framework, with a Markov chain Monte Carlo method. A total variation prior is used for the spatially distributed contact conductance. The feasibility of the approach is evaluated with simulated temperature measurements for cases with contact failures of different sizes.

This paper deals with the solution of an inverse heat conduction problem, aiming at the identification of contact failures in composites formed by layers of different materials. The inverse problem is solved within the Bayesian framework, with a Markov chain Monte Carlo method. The effects of the prior models, for the unknown contact conductance at the interface of a plate with two layers, are examined. The solution of the inverse problem is performed with simulated temperature measurements.

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Deep Brain Stimulation (DBS) is a well-established neurosurgery that alleviates the symptoms of several movement disorders and other brain related conditions. It works by placing a small lead containing electrodes inside the patient's brain and using them to electrically stimulate that area. Although this procedure is very common, little is known about its physiological effects on the brain and, on top of that, injuries caused from burning have been reported. The present work intends to formulate and solve a bioheat transfer model of the brain containing a DBS lead and, furthermore, to use this solution for solving an inverse problem of determining the thermal and electrical conductivities of the brain tissue using measurements supposedly obtained with a sensor inside the DBS lead.The results revealed that it is possible to estimate the both parameters especially when the measurements uncertainties are relatively small.

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