Simultaneous Identification of Temperature-Dependent Thermal Properties via Enhanced Genetic Algorithm

Simultaneous inverse identification of the temperature-dependent volumetric heat capacity and thermal conductivity of a solid material based on transient temperature histories was studied. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The material property functions were defined by several data points with linear interpolation between them. In this way, no previous information is needed about the expected temperature dependency of the material property functions. The inverse problem was solved by a real-valued genetic algorithm using simulated measurement results. New genetic operators (smooth initialization and smooth mutation) were developed and applied. The accuracy of the inverse solution was studied in two test cases including linear, square, and sinusoidal functions. The effects of regularization and random noise in the temperature histories were also analyzed. Based on the results, the proposed method is likely to be effective in evaluation of real measured temperature histories to simultaneously determine temperature-dependent thermophysical properties.

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