An inversion approach for the inverse heat conduction problems

Abstract The inverse heat conduction problems (IHCP) analysis method provides an efficient approach for estimating the thermophysical properties of materials, the boundary conditions, or the initial conditions. Successful applications of the IHCP method depend mainly on the efficiency of the inversion algorithms. In this paper, a generalized objective functional, which has been developed using a generalized stabilizing functional and a combinational estimation that integrates the advantages of the least trimmed squares (LTS) estimation and the M-estimation, is proposed. The objective functional unifies the regularized M-estimation, the regularized least squares (LS) estimation, the regularized LTS estimation, the regularized combinational estimation of the LTS estimation and the M-estimation, and the regularized combinational estimation of the LS estimation and the M-estimation into a concise formula. The filled function method, which is coupled with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm, is developed for searching a possible global optimal solution. Numerical simulations are implemented to evaluate the feasibility and effectiveness of the proposed algorithm. Favorable numerical performances and satisfactory results are observed, which indicates that the proposed algorithm is successful in solving the IHCP.

[1]  Yi Zhang,et al.  A two-dimensional inverse heat conduction problem in estimating the fluid temperature in a pipeline , 2010 .

[2]  Chein-Shan Liu A self-adaptive LGSM to recover initial condition or heat source of one-dimensional heat conduction equation by using only minimal boundary thermal data , 2011 .

[3]  B. Blackwell,et al.  Inverse Heat Conduction: Ill-Posed Problems , 1985 .

[4]  Jongwoo Kim,et al.  Application of the least trimmed squares technique to prototype-based clustering , 1996, Pattern Recognit. Lett..

[5]  Cheng-Hung Huang,et al.  A three-dimensional inverse heat conduction problem in estimating surface heat flux by conjugate gradient method , 1999 .

[6]  Y. Hwang,et al.  Applying neural networks to the solution of forward and inverse heat conduction problems , 2006 .

[7]  S. Baek,et al.  Inverse radiation–conduction design problem in a participating concentric cylindrical medium , 2007 .

[8]  Woo Il Lee,et al.  A Maximum Entropy Solution for a Two-Dimensional Inverse Heat Conduction Problem , 2003 .

[9]  Sou-Chen Lee,et al.  An efficient on-line thermal input estimation method using kalman filter and recursive least square algorithm , 1997 .

[10]  C. Yoo,et al.  Underdetermined Blind Source Separation Based on Generalized Gaussian Distribution , 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

[11]  Juan Andrés Martín García,et al.  A sequential algorithm of inverse heat conduction problems using singular value decomposition , 2005 .

[12]  C. Vogel,et al.  Analysis of bounded variation penalty methods for ill-posed problems , 1994 .

[13]  Nicholas Zabaras,et al.  A Bayesian inference approach to the inverse heat conduction problem , 2004 .

[14]  Jun Sun,et al.  Estimation of unknown heat source function in inverse heat conduction problems using quantum-behaved particle swarm optimization , 2011 .

[15]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[16]  Guangjun Wang,et al.  A decentralized fuzzy inference method for solving the two-dimensional steady inverse heat conduction problem of estimating boundary condition , 2011 .

[17]  Pan-Chio Tuan,et al.  A recursive least-squares algorithm for on-line 1-D inverse heat conduction estimation , 1997 .

[18]  Renpu Ge,et al.  A Filled Function Method for Finding a Global Minimizer of a Function of Several Variables , 1990, Math. Program..

[19]  O. Alifanov Inverse heat transfer problems , 1994 .

[20]  Lonnie Hamm,et al.  GLOBAL OPTIMIZATION METHODS , 2002 .

[21]  Anthony T. Patera,et al.  Inverse identification of thermal parameters using reduced-basis method , 2005 .

[22]  Naouel Daouas,et al.  Version étendue du filtre de Kalman discret appliquée à un problème inverse de conduction de chaleur non linéaire , 2000 .