Many engineering problems involve heat and mass transfer. A typical solution procedure for such a problem represents the determination of thermal behaviour and thermal response of a system under specific conditions, including thermal conditions (initial and boundary conditions) and material-related parameters (thermo-physical properties). Such a problem is referred to as a direct problem. In certain cases, however, some of these conditions or parameters are not known. Instead, information about the thermal behaviour is available. This is the case in which an inverse heat transfer problem has to be solved. Such a problem is actually a data-fitting and optimization task since conditions and parameters, which minimize the error between actual and prescribed data, are searched indirectly. A number of gradient-based methods to inverse problems have been developed in the past. However, they suffer from some disadvantages, including proneness to get trapped in local optima or a low performance in large-scale problems. This is the reason why so-called soft computing methods have experienced great development in recent years. In this paper, seven meta-heuristic algorithms and one algorithm based on an artificial neural network (ANN), referred to as an LSADE algorithm, were applied to the solution of an inverse heat transfer problem with phase change. The problem involved an inverse identification of parameters of an effective heat capacity function, which is a common technique used in phase change modelling. An air-PCM heat exchanger for latent heat thermal energy storage and solar air heating was used as a study case. Results obtained within the scope of the study indicate that the ANN-based LSADE algorithm significantly outperformed other meta-heuristic algorithms, which makes it a very promising tool for the solution of similar kinds of problems.

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