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Training Based Method for Heat Flux Function Estimation Using Sensed Temperatures

Author(s): Morteza Mohmmadzaheri | Orang Asef-Afshar | Ali Mirsepahi | Mansur Bornak

Journal: Sel├žuk Journal of Applied Mathematics
ISSN 1302-7980

Volume: 7;
Issue: 2;
Start page: 55;
Date: 2006;
Original page

Keywords: Inverse heat conduction | artificial neural networks | inverse modelling.

Heat flux function estimation problems are inverse heat conduction problems which heat flux functions (boundary or initial conditions) are the unknowns and temperature distribution (at present and earlier times) is available. There are several algorithms for solving this type of IHC problems. In all of these methods the computational cost is very heavy and all of common IHCP algorithm requires finding the solution of the direct heat conduction problem numerous times. In this paper the neural networks is utilized to estimate the "filter coefficients" needed to estimate heat flux in a particular system. In developing the training phase of the network inspiration is drawn from the Burgraff's exact solution of the IHCP as well as the filter method. Thus, the estimation phase neither requires any temperature field nor the sensitivity coefficients calculations which are common in classical methods. The neural network used in this work is a 2-layer perceptron. It is shown via classical triangular heat flux test cases that the method can yield very accurate, very efficient as well as stable estimations.
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