Benchmarking Reynolds Averaged Navier–Stokes Turbulence Models in Internal Pin Fin Channels

Abstract

The performance of gas turbine engines increases with higher turbine inlet temperatures. To enable operation at temperatures beyond material limitations, turbine blades are often cooled using internal pin fin channels, which are used at the trailing-edge region. This study validates the accuracy of various Reynolds-averaged Navier–Stokes turbulence models in predicting the heat transfer rates and pressure distributions on the surface of the pins by comparing computational fluid dynamics simulation data against a valid set of experimental data obtained from literature. Considering the problem from an industrial design perspective, several commercially available, computationally efficient Reynolds-averaged Navier–Stokes turbulence models are used to predict the cooling performance of the pin fin channel. Turbulence models investigated are Menter’s shear stress transport, realizable , and a quadratic formulation of the realizable model. The geometry investigated is a staggered eight-row pin fin channel. Results show that the quadratic realizable and shear stress transport models predict pin surface heat transfer rates with the highest accuracy, with a dependency on Reynolds number and location within the channel. Comparisons have also shown that depending on Reynolds number, the realizable and quadratic realizable models match the pressure coefficient on the surface of the pin to within 8%.