Chaos-regularization hybrid algorithm for nonlinear two-dimensional inverse heat conduction problem

2002 ◽  
Vol 23 (8) ◽  
pp. 973-980 ◽  
Author(s):  
Wang Deng-gang ◽  
Liu Ying-xi ◽  
Li Shou-ju
Keyword(s):  
Heat Conduction ◽  
Hybrid Algorithm ◽  
Heat Conduction Problem ◽  
Inverse Heat Conduction Problem ◽  
Two Dimensional ◽  
Inverse Heat Conduction

Numerical solution of two-dimensional radially symmetric inverse heat conduction problem

2015 ◽  
Vol 23 (2) ◽  
Author(s):  
Zhi Qian ◽  
Benny Y. C. Hon ◽  
Xiang Tuan Xiong
Keyword(s):  
Heat Conduction ◽  
Numerical Solution ◽  
Heat Conduction Problem ◽  
Inverse Heat Conduction Problem ◽  
Stability And Convergence ◽  
Two Dimensional ◽  
Inverse Heat Conduction ◽  
Convergence Error ◽  
Regularization Techniques ◽  
Ill Posed

AbstractWe investigate a two-dimensional radially symmetric inverse heat conduction problem, which is ill-posed in the sense that the solution does not depend continuously on input data. By generalizing the idea of kernel approximation, we devise a modified kernel in the frequency domain to reconstruct a numerical solution for the inverse heat conduction problem from the given noisy data. For the stability of the numerical approximation, we develop seven regularization techniques with some stability and convergence error estimates to reconstruct the unknown solution. Numerical experiments illustrate that the proposed numerical algorithm with regularization techniques provides a feasible and effective approximation to the solution of the inverse and ill-posed problem.

A Maximum Entropy Solution for a Two-Dimensional Inverse Heat Conduction Problem

2003 ◽  
Vol 125 (6) ◽  
pp. 1197-1205 ◽  
Author(s):  
Sun Kyoung Kim ◽  
Woo Il Lee
Keyword(s):  
Heat Conduction ◽  
Maximum Entropy ◽  
Optimization Problem ◽  
Entropy Solution ◽  
Heat Conduction Problem ◽  
Inverse Heat Conduction Problem ◽  
Measured Temperature ◽  
Two Dimensional ◽  
Inverse Heat Conduction ◽  
Inverse Heat Conduction Problems

A solution scheme based on the maximum entropy method (MEM) for the solution of two-dimensional inverse heat conduction problems is established. MEM finds the solution which maximizes the entropy functional under the given temperature measurements. The proposed method converts the inverse problem to a nonlinear constrained optimization problem. The constraint of the optimization problem is the statistical consistency between the measured temperature and the estimated temperature. Successive quadratic programming (SQP) facilitates the numerical estimation of the maximum entropy solution. The characteristic feature of the proposed method is investigated with the sample numerical results. The presented results show considerable enhancement in resolution for stringent cases in comparison with a conventional method.

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