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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. [ABSTRACT FROM AUTHOR]

An artificial neural network (NN)-based solution of the inverse heat conduction problem of identifying the temperature-dependent volumetric heat capacity function of a solid material is presented in this paper. The inverse problem was defined according to the evaluation of the BICOND thermophysical property measurement method. The volumetric heat capacity versus temperature function is to be determined using the measured transient temperature history of a single sensor. In this study, noiseless and noisy artificial measurements were generated by the numerical solution of the corresponding direct heat conduction problem. The inverse problem was solved by back-propagation and radial basis function type neural networks applying the whole history mapping approach. The numerical tests included the comparison of two different data representations of the network inputs (i.e., temperature vs. time and time vs. temperature) and accuracy analysis of the two network types with noiseless and noisy inputs. Based on the results presented, it can be stated that feed-forward NNs are powerful tools in a non-iterative solution of function estimation inverse heat conduction problems and they are likely to be very effective in evaluation of real measured temperature histories to determine the volumetric heat capacity as an arbitrary function of temperature. [ABSTRACT FROM AUTHOR]

The paper presents a curve-fitting-based calculation of the thermal contact resistance and other parameters (absorbed energy and material properties) from laser flash measurements considering a two-layer specimen (aluminum substrate and stainless-steel film). Sensitivity analysis of different cases was used to examine the sensitivities of the unknown parameters: thermal contact resistance, absorbed energy, specific heat of the film, and thermal conductivity of the film. A nonlinear curve-fitting method was applied to perform the estimation of the unknown parameters using simulated measurements generated by the solution of the direct problem. An extensive analysis was performed to examine which parameters might be estimated simultaneously with the contact resistance for different noise levels of the simulated measurement. It was concluded that in the noiseless case all four unknown parameters can be estimated simultaneously with high accuracy. The noise has a significant effect on the accuracy of the parameter estimation, but even when a reasonable noise level is present, it is still possible to accurately estimate one or two parameters together with the thermal contact resistance. [ABSTRACT FROM AUTHOR]