Genetic Algorithm as the Solution of Non-Linear Inverse Heat Conduction Problems: a Novel Sequential Approach

Direct measurement of surface heat flux could be extremely challenging, or impossible, in numerous applications. In such cases, the use of temperature measurement data from sub-surface locations can facilitate the determination of surface heat flux and temperature through the solution of the inverse heat conduction problem (IHCP). Different techniques have been developed for solving IHCPs. Inspired by the filter coefficient approach, a novel method is presented in this paper for solving one-dimensional IHCPs in a domain with temperature-dependent material properties. A test case is developed in COMSOL Multiphysics where the front side of a slab is subject to known transient heat flux and the temperature distributions within the domain are calculated. The IHCP solution in the form of filter coefficients is defined and a genetic algorithm is used for the calculation of filter matrix. The number of significant filter coefficients required to evaluate surface heat flux at each time step is determined through trial and error and the optimal number is selected for evaluating the solution. The structure of the filter matrix is assessed and discussed. The resulting filter coefficients are used to evaluate the surface heat flux for several cases and the performance of the proposed approach is assessed in detail. The results showed that the presented approach is robust and can result in finding optimal filter coefficients to accurately estimate various types of surface heat flux profiles using temperature data from a limited number of time steps and in a near real-time fashion.