Abstract
Based on the finite difference method and the artificial bee colony algorithm, the thermal conductivity in the two-dimensional unsteady-state heat transfer system is deduced. An improved artificial bee colony algorithm (IABCA), that artificial bee colony algorithm (ABCA) coupled with calculated deviation feedback, is proposed to overcome the shortcomings of insufficient local exploitation capacity and slow convergence rate in the late stage of the artificial bee colony algorithm (ABCA). For the forward problems, the finite difference method (FDM) is used to calculate the required temperature value of a discrete point; for the inverse problems, the IABCA is applied to minimize the objective function. In the inversion problem, the effects of colony size, number of measuring points, and the existence of measurement errors on the results are studied, and the inversion convergence rate of IABCA and ABCA is compared. The results demonstrate that the methods adopted in this paper had good effectiveness and accuracy even if colony sizes differ and measurement errors exist; and that IABCA has a more efficient convergence rate than ABCA.
Highlights
The inverse heat conduction problem is a typical inversion problem
Inverse heat conduction problems have been widely applied with great success in materials science, geology and environmental science, biological engineering, mechanical engineering, power engineering, construction engineering, aerospace engineering, metallurgical engineering, and other fields [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]
The inversion of thermal conductivity as one branch of inverse heat conduction problems has been studied by a significant number of scholars with different optimized algorithms
Summary
The inverse heat conduction problem is a typical inversion problem. It is based on the local temperature and other known parameters of a heat transfer system surface or interior that are used to inversely solve some unknown characteristic parameters of the system; examples are geometric boundary, thermal conductivity of materials, thermal boundary conditions (heat flux, surface heat transfer coefficient, temperature distribution), and historical temperature. A non-gradient optimization algorithm is usually a global search algorithm, which has good adaptability and overcomes the difficulty of the gradient optimization algorithm settling on a local minimum It does not involve the calculation of a Jacobian partial derivative matrix in the inversion process. The inverse thermal conductivity of materials was solved by Tang et al and Zhao et al with a genetic algorithm [29,30] This kind of method often has a high computation cost in the search process and has a slow convergence rate in the latter stage, which limits its application in the inverse heat conduction problem. Measurement error on the inversion result is studied and the true thermal conductivity is obtained
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