Abstract
This article deals with the prediction of parameters in an annular hyperbolic fin with temperature-dependent thermal conductivity. Three parameters such as thermal conductivity, variable conductivity coefficient and the surface heat transfer coefficient have been predicted for satisfying a prescribed temperature distribution on the surface of fin. This is achieved by a hybrid differential evolution-nonlinear programming optimization method. The effect of random measurement errors is also considered. It is observed from the present inverse analysis that many feasible materials exist satisfying the given temperature distribution, thereby providing engineering flexibility in selecting any material from the available choices. For a given material, this is possible by regulating the surface heat transfer coefficient.
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