An inverse heat conduction analysis is presented to simultaneously estimate the temperaturedependent thermal conductivity and heat capacity based on a modified elitist genetic algorithm (MEGA). In this study, MEGA is used to minimize a least squares objective function containing estimated and simulated (filtered) temperatures. The estimated temperatures are obtained from the direct numerical solution (finite differences method, or FDM) of the finite one-dimensional conductive model by using an estimate for the unknown temperaturedependent thermophysical properties (TDTPs). The accuracy of the MEGA is assessed by comparing the estimated and the preselected TDTPs. The results of the MEGA are used as the starting point for a locally convergent optimization algorithm, i.e., the Levenberg-Marquardt (L-M) method. It is shown in this work that hybridization of the MEGA with the L-M method can lead to accurate estimates. From the results, it is found that the RMS error between estimated and simulated temperatures is very small irrespective of whether measurement errors are included or excluded. In addition to estimation of the TDTPs, sensitivity analysis is performed to investigate the effects of heating duration. Also, it is found that the results of the MEGA are highly satisfactory with only single-sensor measurements on the heated surface.

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PurposeThe main aim of this paper is to utilize the different forms of functions for the numerical solution of the two‐dimensional (2‐D) inverse heat conduction problem with temperature‐dependent thermo‐physical properties (TDTPs).Design/methodology/approachThe proposed numerical technique is based on the modified elitist genetic algorithm (MEGA) combined with finite different method (FDM) to simultaneously estimate temperature‐dependent thermal conductivity and heat capacity. In this paper, simulated (noisy and filtered) temperatures are used instead of experimental data. The estimated temperatures are obtained from the direct numerical solution (FDM) of the 2‐D conductive model by using an estimate for the unknown TDTPs and MEGA is used to minimize a least squares objective function containing estimated and simulated (noisy and filtered) temperatures.FindingsThe accuracy of the MEGA is assessed by comparing the estimated and the pre‐selected TDTPs. The results show that the measurement errors do not considerably affect the accuracy of the estimates. In other words, the proposed method provides a practical and confident prediction in simultaneously estimating the temperature‐dependent heat capacity and thermal conductivity. From the results, it is found that the RMS error between estimated and simulated temperatures is smaller for linear simulation and also we found this form convenient for parameters estimations.Research limitations/implicationsFuture approaches should find the optimal design of case study and then apply the proposed method to achieve the best results.Originality/valueApplications of the results presented in this paper can be of value in practical applications in parameter estimation even with one sensor temperature history.