The precise assessment of thermophysical properties of anisotropic materials is crucial in many industrial applications, including thermal insulation design, automobile part design and analysis, aerospace industries, and so on. However, due to the anisotropic nature of the material, measuring these properties is difficult. In this case, inverse heat transfer methods based on the recorded temperature field can be used to estimate these parameters, and heuristic optimization approaches can be employed to improve the accuracy of the estimation process. The process of discovering unknown boundary conditions, material qualities, or heat source based on temperature readings is known as the inverse heat conduction problems.

The current study describes a method for estimating thermophysical parameters of anisotropic honeycomb structures, such as thermal conductivities in three directions, heat capacity, and contact conductance, using an inverse heat transfer method optimized using heuristic algorithms. The inverse heat transfer problem is defined as an optimization problem, with the objective function being the difference between the measured and anticipated temperature fields. To solve the problem, the numerical approach is implemented using MATLAB and ANSYS software. The optimization heuristic algorithms GA, DE, and ACO were initially validated with standard functions, and Differential Evolution was then applied for the current problem.

Overall, the suggested method uses inverse heat transfer and heuristic optimization approaches to give a reliable and accurate strategy for estimating the properties of anisotropic materials. The method is applicable to a wide range of industrial applications, including thermal management of electronic devices and a variety of aerospace applications where accurate prediction of anisotropic properties is critical for efficient design and operation. Further research can be done to investigate the applicability of the proposed method to other anisotropic materials and to enhance the computational efficiency of the optimization process.