• Proceedings of the KSME Conference
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• 2000.04b
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• pp.144-147
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• 2000

A numerical method for the solution of one-dimensional inverse heat conduction problem is established and its performance is demonstrated with computational results. The present work introduces the maximum entropy method in order to build a robust formulation of the inverse problem. The maximum entropy method finds the solution that maximizes the entropy functional under given temperature measurement. The philosophy of the method is to seek the most likely inverse solution. The maximum entropy method converts the inverse problem to a non-linear constrained optimization problem of which constraint is the statistical consistency between the measured temperature and the estimated temperature. The successive quadratic programming facilitates the maximum entropy estimation. The gradient required fur the optimization procedure is provided by solving the adjoint problem. The characteristic feature of the maximum entropy method is discussed with the illustrated results. The presented results show considerable resolution enhancement and bias reduction in comparison with the conventional methods.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.59-65
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• 2007

This paper suggests a method for developing computer code that can solve inverse heat conduction problem, The concept of the object-oriented development is employed to implement the computer code in an efficient and flexible fashion. The software design is conducted based on the unified modeling language. Furthermore, this paper also explains how to implement the deliverable computer code using the existing software development tools.

• Journal of Advanced Marine Engineering and Technology
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• v.21 no.5
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• pp.549-556
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• 1997

The inverse heat conduction problems is the calculation of surface heat transfer coefficients by utilizing measured temperature. The numerical technique of finite element analysis and optimizition is introduced to calculate temperatures and heat transfer coefficients. The calculated heat transfer coefficients and temperature distribution are good agreement with the results of direct analysis. The inverse method has been applied to the control valve of nuclear power plant.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.820-827
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• 2006

The convection heat transfer coefficients on the top surface of a large liquid petroleum liquid injection(LPLi) engine piston are analyzed by solving an inverse thermal conduction problem. The heat transfer coefficients are numerically found so that the difference between analyzed temperatures from the finite element method and measured temperatures is minimized. Using the resulting heat transfer coefficients as the boundary condition, temperature of a large LPLi engine piston is analyzed.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.146-159
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• 2002

An efficient method to predict the convection heat transfer coefficients on the top surface of the engine piston is proposed. The method is based on the inverse method of the thermal conduction problem and uses a numerical optimization technique. In the method, the heat transfer coefficients are numerically obtained so that the difference between analyzed temperatures from the finite element method and measured temperatures is minimized. The method can be effectively used to analyze the temperature distribution of engine pistons in case when application of prescribed-temperature boundary condition is not reasonable because of insufficient number of measured temperatures. A hollow sphere problem with an analytic solution is taken as a simple example and accuracy and efficiency is demonstrated. The method is applied to a practical large liquid petroleum gas(LPG) engine piston and the heat transfer coefficients on the top surface of the piston is successfully calculated. Resulting analyzed temperature favorably coincides with measured temperature.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1183-1188
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• 2004

The inverse heat conduction problem (IHCP) is a problem of estimating boundary condition from temperature measurement at one or more interior points. Neural networks are general information processing systems inspired by the connectionist theory of human brain. By properly training the network by the learning rule, the neural network method can handle many non-linear or other complex problems. In this work, neural network is applied to complicated inverse heat conduction problems. Efficiency of the procedure is enhanced by incorporating the radial basis functions (RBF). The RBF is trained faster than other neural network and can find smooth solution. In order to demonstrate the effectiveness of the current scheme, a typical one-dimensional IHCP is considered. At one surface, the temperature as well as the heat flux is known. The unknown temperature of interest is estimated on the other side of the slab. The results from the proposed method based on RBF neural network are compared with the conventional method.

• Journal of Mechanical Science and Technology
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• v.16 no.10
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• pp.1320-1326
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• 2002

In a one-dimensional heat conduction domain with heated and insulated walls, an integral approach is proposed to estimate time-dependent boundary heat flux without internal measurements. It is assumed that the expression of the heat flux is not known a priori. Hence, the present inverse heat conduction problem is classified as a function estimation problem. The spatial temperature distribution is approximated as a third-order polynomial of position, whose four coefficients are determined from the heat fluxes and the temperatures at both ends at each measurement. After integrating the heat conduction equation over spatial and time domain, respectively, a simple and non-iterative recursive equation to estimate the time-dependent boundary heat flux is derived. Several examples are introduced to show the effectiveness of the present approach.

• Journal of the Korean Society of Safety
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• v.22 no.4
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• pp.13-19
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• 2007

An numerical method to estimate thermal diffusivity has been developed for one-dimensional unsteady heat conduction problem, when the temperatures are know at two positions in a semi-infinite body. Using the closed form solution which has already derived an explicit solution for the inverse problem for one-dimensional transient heat conduction using Laplace transform technique, we first estimate the surface temperature. The thermal diffusivity can be estimated by using the estimated surface temperature and measured temperatures, which include some uncertainties. The estimated surface heat flux and thermal diffusivity are found to be in good agreement with those of the experimented conditions. This method will be extended to the simultaneous measurement of thermal diffusivity and thermal conductivity.

• Journal of applied mathematics & informatics
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• v.35 no.5_6
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• pp.439-447
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• 2017

This paper investigates the inverse problem of determining an unknown heat radiative coefficient, which is only time-dependent. This is an ill-posed problem, that is, small errors in data may cause huge deviations in determining solution. In this paper, the existence and uniqueness of the problem is established by the second Volterra integral equation theory, and the method of trace-type functional formulation combined with finite difference scheme is studied. One typical numerical example using the proposed method is illustrated and discussed.

• Transactions of Materials Processing
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• v.15 no.9 s.90
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• pp.679-685
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• 2006

New method for evaluation of heat transfer coefficient is proposed. In general, many researchers have been studied about inverse problem in order to calculate the heat transfer coefficient on three-dimensional heat conduction problem. But they can get the time-dependent heat transfer coefficient only through inverse problem. In order to acquire temperature-dependent heat transfer coefficient, it requires much time for numerous repetitive calculation and inconvenient manual modification. In order to solve these problems, we are using the SQP(Sequential Quadratic Programming) as an optimization algorithm. When the temperature history is given by experiment, the optimization algorithm can evaluate the temperature-dependent heat transfer coefficient with automatic repetitive calculation until difference between calculated temperature history and experimental ones is minimized. Finally, temperature-dependent heat transfer coefficient evaluated by developed program can used on various heat transfer problem.