The Steady Inverse Heat Conduction Problem: A Comparison of Methods With Parameter Selection

2001 ◽  
Vol 123 (4) ◽  
pp. 633-644 ◽  
Author(s):  
Robert Throne ◽  
Lorraine Olson

In the past we have developed the Generalized Eigensystem GESL techniques for solving inverse boundary value problems in steady heat conduction, and found that these vector expansion methods often give superior results to those obtained with standard Tikhonov regularization methods. However, these earlier comparisons were based on the optimal results for each method, which required that we know the true solution to set the value of the regularization parameter (t) for Tikhonov regularization and the number of mode clusters Nclusters for GESL. In this paper we introduce a sensor sensitivity method for estimating appropriate values of Nclusters for GESL. We compare those results with Tikhonov regularization using the Combined Residual and Smoothing Operator (CRESO) to estimate the appropriate values of t. We find that both methods are quite effective at estimating the appropriate parameters, and that GESL often gives superior results to Tikhonov regularization even when Nclusters is estimated from measured data.

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Arghand ◽  
Majid Amirfakhrian

We propose a new meshless method to solve a backward inverse heat conduction problem. The numerical scheme, based on the fundamental solution of the heat equation and radial basis functions (RBFs), is used to obtain a numerical solution. Since the coefficients matrix is ill-conditioned, the Tikhonov regularization (TR) method is employed to solve the resulted system of linear equations. Also, the generalized cross-validation (GCV) criterion is applied to choose a regularization parameter. A test problem demonstrates the stability, accuracy, and efficiency of the proposed method.


1989 ◽  
Vol 111 (2) ◽  
pp. 218-224 ◽  
Author(s):  
E. P. Scott ◽  
J. V. Beck

Various methods have been proposed to solve the inverse heat conduction problem of determining a boundary condition at the surface of a body from discrete internal temperature measurements. These include function specification and regularization methods. This paper investigates the various components of the regularization method using the sequential regularization method proposed by Beck and Murio (1986). Specifically, the effects of the regularization order and the influence of the regularization parameter are analyzed. It is shown that as the order of regularization increases, the bias errors decrease and the variance increases. Comparatively, the zeroth regularization has higher bias errors and the second-order regularization is more sensitive to random errors. As the regularization parameter decreases, the sensitivity of the estimator to random errors is shown to increase; on the other hand, the bias errors are shown to decrease.


2005 ◽  
Vol 2005 (8) ◽  
pp. 1221-1237 ◽  
Author(s):  
Chu-Li Fu ◽  
Hong-Fang Li ◽  
Xiang-Tuan Xiong ◽  
Peng Fu

We consider an inverse heat conduction problem with convection term which appears in some applied subjects. This problem is ill posed in the sense that the solution (if it exists) does not depend continuously on the data. A generalized Tikhonov regularization method for this problem is given, which realizes the best possible accuracy.


1997 ◽  
Vol 119 (1) ◽  
pp. 38-45 ◽  
Author(s):  
A. M. Osman ◽  
K. J. Dowding ◽  
J. V. Beck

This paper presents a method for calculating the heat flux at the surface of a body from experimentally measured transient temperature data, which has been called the inverse heat conduction problem (IHCP). The analysis allows for two-dimensional heat flow in an arbitrarily shaped body and orthotropic temperature dependent thermal properties. A combined function specification and regularization method is used to solve the IHCP with a sequential-in-time concept used to improve the computational efficiency. To enhance the accuracy, the future information used in the sequential-in-time method and the regularization parameter are variable during the analysis. An example using numerically simulated data is presented to demonstrate the application of the method. Finally, a case using actual experimental data is presented. For this case, the boundary condition was experimentally measured and hence, it was known. A good comparison is demonstrated between the known and estimated boundary conditions for the analysis of the numerical, as well as the experimental data.


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