Application of improved decentralized fuzzy inference methods for estimating the thermal boundary condition of participating medium

Abstract

The decentralized fuzzy inference method (DFIM) was applied to estimate the time-dependent heat flux of 1D participating medium. The direct problem concerned on coupled radiation and conduction heat transfer in the medium was solved by the finite volume method and discrete ordinate method. The simulated boundary temperature was served as input for the inverse analysis. The inverse problem was formulated as an optimization approach. Three improved decentralized fuzzy inference methods (IDFIMs) were developed to accelerate the convergence rate and enhance the estimation accuracy. Five kinds of time-dependent heat fluxes were considered to test the performance of the present inverse technique. No prior information on the functional forms of the unknown boundary conditions was needed for the inverse analysis. All retrieval results showed that the incident heat flux of participating medium can be accurately estimated by DFIMs. The proposed IDFIMs achieved better performance than the original DFIM in terms of computational accuracy and efficiency. Moreover, a comparison between the IDFIM and other optimization techniques was conducted. The proposed IDFIM was proved to be more efficient and accurate than conjugate gradient method, Levenberg-Marquardt method, stochastic particle swarm optimization algorithm and genetic algorithm.