Nonlinear, Rescaling-Based Inverse Heat Conduction Calibration Method and Optimal Regularization Parameter Strategy

2016 ◽  
Vol 30 (1) ◽  
pp. 67-88 ◽  
Author(s):  
Y. Y. Chen ◽  
J. I. Frankel ◽  
M. Keyhani
Keyword(s):  
Heat Conduction ◽  
Regularization Parameter ◽  
Calibration Method ◽  
Inverse Heat Conduction ◽  
Nonlinear Rescaling

scholarly journals A Meshless Method Based on the Fundamental Solution and Radial Basis Function for Solving an Inverse Heat Conduction Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Arghand ◽  
Majid Amirfakhrian
Keyword(s):  
Heat Conduction ◽  
Fundamental Solution ◽  
Meshless Method ◽  
Heat Conduction Problem ◽  
Regularization Parameter ◽  
Linear Equations ◽  
Inverse Heat Conduction Problem ◽  
Inverse Heat Conduction ◽  
Radial Basis ◽  
The Stability

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.

scholarly journals To the Solution of Geometric Inverse Heat Conduction Problems

2021 ◽  
Vol 24 (1) ◽  
pp. 6-12
Author(s):  
Yurii M. Matsevytyi ◽  
◽  
Valerii V. Hanchyn ◽  
Keyword(s):  
Heat Conduction ◽  
Iterative Process ◽  
Regularization Parameter ◽  
Outer Boundary ◽  
Linear Equations ◽  
The Body ◽  
Two Dimensional ◽  
Inverse Heat Conduction ◽  
System Of Linear Equations ◽  
Inverse Heat Conduction Problems

On the basis of A. N. Tikhonov’s regularization theory, a method is developed for solving inverse heat conduction problems of identifying a smooth outer boundary of a two-dimensional region with a known boundary condition. For this, the smooth boundary to be identified is approximated by Schoenberg’s cubic splines, as a result of which its identification is reduced to determining the unknown approximation coefficients. With known boundary and initial conditions, the body temperature will depend only on these coefficients. With the temperature expressed using the Taylor formula for two series terms and substituted into the Tikhonov functional, the problem of determining the increments of the coefficients can be reduced to solving a system of linear equations with respect to these increments. Having chosen a certain regularization parameter and a certain function describing the shape of the outer boundary as an initial approximation, one can implement an iterative process. In this process, the vector of unknown coefficients for the current iteration will be equal to the sum of the vector of coefficients in the previous iteration and the vector of the increments of these coefficients, obtained as a result of solving a system of linear equations. Having obtained a vector of coefficients as a result of a converging iterative process, it is possible to determine the root-mean-square discrepancy between the temperature obtained and the temperature measured as a result of the experiment. It remains to select the regularization parameter in such a way that this discrepancy is within the measurement error. The method itself and the ways of its implementation are the novelty of the material presented in this paper in comparison with other authors’ approaches to the solution of geometric inverse heat conduction problems. When checking the effectiveness of using the method proposed, a number of two-dimensional test problems for bodies with a known location of the outer boundary were solved. An analysis of the influence of random measurement errors on the error in identifying the outer boundary shape is carried out.

Analysis of Order of the Sequential Regularization Solutions of Inverse Heat Conduction Problems

1989 ◽  
Vol 111 (2) ◽  
pp. 218-224 ◽  
Author(s):  
E. P. Scott ◽  
J. V. Beck
Keyword(s):  
Heat Conduction ◽  
Regularization Method ◽  
Heat Conduction Problem ◽  
Regularization Parameter ◽  
Inverse Heat Conduction Problem ◽  
Regularization Methods ◽  
Inverse Heat Conduction ◽  
Random Errors ◽  
Internal Temperature ◽  
Inverse Heat Conduction Problems

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.

scholarly journals A New Hybrid Method for Solving Inverse Heat Conduction Problems

2021 ◽  
Vol 15 ◽  
pp. 151-158
Author(s):  
M. R. Shahnazari ◽  
F. Roohi Shali ◽  
A. Saberi ◽  
M. H. Moosavi
Keyword(s):  
Heat Conduction ◽  
Inverse Problems ◽  
Regularization Parameter ◽  
Accurate Method ◽  
Fundamental Solutions ◽  
Industrial Applications ◽  
Inverse Heat Conduction ◽  
Constant Coefficients ◽  
Ill Posed ◽  
Inverse Heat Conduction Problems

Solving the inverse problems, especially in the field of heat transfer, is one of the challenges of engineering due to its importance in industrial applications. It is well-known that inverse heat conduction problems (IHCPs) are severely ill-posed, which means that small disturbances in the input may cause extremely large errors in the solution. This paper introduces an accurate method for solving inverse problems by combining Tikhonov's regularization and the genetic algorithm. Finding the regularization parameter as the decisive parameter is modelled by this method, a few sample problems were solved to investigate the efficiency and accuracy of the proposed method. A linear sum of fundamental solutions with unknown constant coefficients assumed as an approximated solution to the sample IHCP problem and collocation method is used to minimize residues in the collocation points. In this contribution, we use Morozov's discrepancy principle and Quasi-Optimality criterion for defining the objective function, which must be minimized to yield the value of the optimum regularization parameter.

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
Keyword(s):  
Heat Conduction ◽  
Tikhonov Regularization ◽  
Heat Conduction Problem ◽  
Regularization Parameter ◽  
Inverse Heat Conduction Problem ◽  
Inverse Heat Conduction ◽  
True Solution ◽  
Sensitivity Method ◽  
Inverse Boundary Value Problems ◽  
Steady Heat Conduction

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.

Numerical Solution of the General Two-Dimensional Inverse Heat Conduction Problem (IHCP)

1997 ◽  
Vol 119 (1) ◽  
pp. 38-45 ◽  
Author(s):  
A. M. Osman ◽  
K. J. Dowding ◽  
J. V. Beck
Keyword(s):  
Experimental Data ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Regularization Parameter ◽  
Simulated Data ◽  
Inverse Heat Conduction Problem ◽  
Transient Temperature ◽  
Two Dimensional ◽  
Inverse Heat Conduction ◽  
Temperature Dependent

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.

Sign in / Sign up

Export Citation Format

Share Document