This article reports the optimization of film cooling on a leading edge of a gas turbine blade model, with showerhead configuration, it is based on five input parameters, which are hole diameter, hole pitch, column holes pitch, injection angle, and velocity at plenum inlet. This optimization increased the Area-Averaged Film Cooling Effectiveness
and reduced the consumption of coolant flow. Differential Evolution assisted by artificial neural networks was used as optimization algorithm. Reynolds Averaged Navier–Stokes computations were carried out to getting the net database and to evaluate the optimized models predicted by artificial neural network. The results show an effective increment of
by 36% and a mass flow reduction by 66%. These results were reached by means of a better distribution of cooling flow at blade surface as function of the input parameters. To assure the reliability of the numerical model, particle image velocimetry technique was used for its validation.
