Shape Optimization of Pin-Fins in Cooling Channel

This work presents a numerical procedure for multiple objectives to optimize the staggered elliptic-shaped short pin-fin arrays by selecting an appropriate compromise between turbulent heat transfer and friction-loss. The response surface approximation (RSA) is used to reduce the computational burden and to approximate the Pareto optimal front (POF) with Reynolds-averaged Navier-Stokes (RANS) analysis of flow and heat transfer. The Pareto optimal solutions are obtained using a combination of an evolutionary algorithm (elitist non-dominated sorting genetic algorithm NSGA-Ⅱ) and a local search (e-constraint strategy). Shear stress transport (SST) turbulence model is used as a turbulence closure. Computational results for heat transfer rate show a reasonable agreement with the experimental data. Four nondimensionless design variables are chosen. D-optimal method is used to determine the training points as a mean of design of experiment. The Pareto optimal front for the pin-fin arrays has been obtained.