Simulation and Experimental Study of Inverse Heat Conduction Problem

2011 ◽  
Vol 233-235 ◽  
pp. 2820-2823 ◽  
Author(s):  
Ley Chen ◽  
S Askarian ◽  
M Mohammadzaheri ◽  
F Samadi
Keyword(s):  
Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Experimental Tests ◽  
Inverse Heat Conduction Problem ◽  
Inverse Heat Conduction ◽  
Input Output ◽  
Output Data ◽  
Neural Network Method ◽  
Network Method

In this paper, a neural network method is proposed to solve a one dimensional inverse heat conduction problem (IHCP). The method relies on input/output data of an unknown system to create an intelligent neural network model. Multi layer perceptrons with recurrent properties are utilised in the model. Prepared input/output data are used to train the neural network. Reliable checking processes are also offered to justify the robustness of the method. A numerical sequential function specification (SFS) method is used as another technique to solve the IHCP. The numerical result is compared with that of the proposed method and good agreement is shown between the two methods. However, the numerical method can be only used to solve the IHCP off-line due to the high computation requirement. The proposed neural network method can be used in real-time situations as shown in the experimental tests.

Inverse Estimation of Surface Temperature in Nanoscale Using the Artificial Neural Network

2005 ◽  
Author(s):  
Bup Sung Jung ◽  
Sun K. Kim ◽  
Woo Il Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Inverse Heat Conduction Problem ◽  
Inverse Heat Conduction ◽  
Nanoscale Structures ◽  
Temperature Observation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An inverse heat conduction problem (IHCP) for nanoscale structures was studied. The conduction phenomenon is modeled using the Boltzmann transfer equation. Phonon-mediated heat conduction in one dimension is considered. One boundary, where temperature observation takes place, is subjected to a known boundary condition and the other boundary is exposed to an unknown temperature. The artificial neural network (ANN) is employed to solve the described inverse problem. Sample results are presented and discussed.

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