Neuro-genetic optimization of laminar slot jets impinging on a moving surface

In this work, heat transfer from a moving surface due to series of impinging slot jets under laminar conditions has been optimized. For this study numerical investigations were carried out initially using Ansys Fluent 14 and these results were used to train an artificial neural network (ANN). This trained network was integrated into Micro-Genetic Algorithm to get the optimum parameters for better heat transfer from the surface, an optimization procedure proposed by Madadi and Balaji. Pitch of the jets (P), height of the jets (H) and the non-dimensional surface velocity (Vs) were chosen as dependent variables for optimum heat transfer. 99 simulations were performed by changing above parameters for each Reynolds number, Re of 100 and 200 were used for case study. Imposition of surface velocity strongly affects the heat transfer magnitude and distribution following a change in flow structure. The performance of Micro-Genetic Algorithm (μGA) was also compared with standard Genetic Algorithm (GA); it shows that μGA reaches optimum in less than half the time of standard GA. The optimum results show that the pitch of the jets, height of the jets and surface velocity should be as low as possible.