Abstract:
This paper deals with the inverse prediction of
parameters in a trapezoidal fin with temperature-dependent
thermal conductivity and heat transfer coefficient. Three
critical dimensions along with the relevant heat transfer
coefficient at the fin base have been simultaneously predicted for satisfying a given temperature distribution on the
surface of the trapezoidal fin. The inverse problem is
solved by a hybrid differential evolution-nonlinear programming (DE-NLP) optimization method. For a given fin
material which is considered to be stainless steel, it is
found from the present study that many feasible dimensions exist which satisfy a given temperature distribution,
thereby providing flexibility in selecting any dimensions
from the available alternatives by appropriately regulating
the base heat transfer coefficient. A very good estimation
of the unknown parameters has been obtained even for
temperature distribution involving random measurement
errors which is confirmed by the comparisons of the
reconstructed distributions. It is concluded that for a given
fin material, the hybrid DE-NLP algorithm satisfactorily
estimates feasible dimensions of a trapezoidal fin even with
random measurement error of 11 %.
