Abstract
Using Artificial Neural Networks in Solving Heat Conduction Problems
Highlights
In forward heat conduction problems the heating characteristics, the boundary condition and the initial conditions of a body are known and are used to establish the internal temperature field
In inverse heat conduction problems (IHCPs), experimental temperature measurement are taken at various points in the interior of a body and are used to estimate the unknown boundary conditions existing at the external surface
IHCPs are mathematically ill-posed in the sense that the existence, uniqueness and stability of their solutions cannot be assured (Beak, 1985)
Summary
In forward heat conduction problems the heating characteristics, the boundary condition and the initial conditions of a body are known and are used to establish the internal temperature field. In inverse heat conduction problems (IHCPs), experimental temperature measurement are taken at various points in the interior of a body and are used to estimate the unknown boundary conditions existing at the external surface. IHCPs are mathematically ill-posed in the sense that the existence, uniqueness and stability of their solutions cannot be assured (Beak, 1985). IHCPs are generally solved using some form of numerical technique. Since the 1970s, computer science and technology have advanced rapidly and contemporary researchers generally solve IHCPs using numerical methods such as the finite element method, the finite different method (FDM) and Genetic algorithm (Raudensk, 1995). The rapid development of artificial neural network technology in recent years has led to an entirely new approach for solution of IHCPs (Raudensk, 1995). Neural networks are artificial intelligence systems which mimic the biological processes of a human brain by using non-linear processing units to simulate the functions of biological neurons
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