Abstract:
This paper investigates an inverse conductiveconvective problem working on a hybrid differential evolutionnonlinear programming (DE-NLP) algorithm. Thermophysical parameters such as the thermal conductivity and the
heat transfer coefficient have been estimated for satisfying a
given temperature distribution. The objective function to be
minimized is represented by the least squares of error between
the randomly-guessed and the exact temperature distributions.
The estimations have been found to be in good agreement.
Results show that DE-NLP algorithm successfully estimates
various possible combinations of thermal conductivity and heat
transfer coefficient which satisfy the given temperature
distribution.
