Inverse Identification of Temperature-Dependent Thermal Properties Using Improved Krill Herd Algorithm

Abstract

A novel intelligent algorithm, krill herd (KH), is firstly introduced to solve the inverse identification of temperature-dependent thermal properties of materials. To promote the searching ability and accelerate the convergence velocity, three improved KH (IKH) algorithms are proposed and developed for solving the optimization tasks. The temperature-dependent thermal conductivity and specific heat of a building material are estimated by using the KH algorithms, and the IKHs achieve better performance than the original KHs. Moreover, the functional forms of thermal conductivity of insulating and refractory materials are also reconstructed. The IKH algorithm is proved to be more accurate than other algorithms. Finally, a two-dimensional nonhomogeneous heat conduction model is investigated and the thermal conductivities of materials at specified temperatures are reconstructed, in which no prior information is needed for the expressions of the thermal conductivity to be identified. All the retrieval results show that IKH algorithm is robust and effective for solving the inverse heat conduction problems.