High-order accurate direct numerical simulation of flow over a MTU-T161 low pressure turbine blade. (15th August 2021)

• Record Type: Journal Article
• Authors: Iyer, A.S.; Abe, Y.; Vermeire, B.C.; Bechlars, P.; Baier, R.D.; Jameson, A.; Witherden, F.D.; Vincent, P.E.
• Is Part Of: Computers & fluids. Volume 226(2021)
• Publication Date: 2021-08-15
• Subjects: Computational fluid dynamics -- High-order methods -- Direct numerical simulations -- Low-Pressure turbines; Fluid dynamics -- Data processing -- Periodicals; 532.050285
• DOI: 10.1016/j.compfluid.2021.104989
• Languages: English
• ISSNs: 0045-7930

Abstract

Highlights: PyFR applied to petascale Direct Numerical Simulation of flow over a MTU-T161 low-pressure turbine blade at a Reynolds number of 200, 000 Various flow metrics compared with available experimental data and found to be in agreement Further detailed analyses of separation/transition processes and end-wall vortices are presented Shows potential of high-order GPU-accelerated Direct Numerical Simulations to underpin a virtual low-pressure turbine wind tunnel capability Abstract: Reynolds Averaged Navier-Stokes (RANS) simulations and wind tunnel testing have become the go-to tools for industrial design of Low-Pressure Turbine (LPT) blades. However, there is also an emerging interest in use of scale-resolving simulations, including Direct Numerical Simulations (DNS). These could generate insight and data to underpin development of improved RANS models for LPT design. Additionally, they could underpin a virtual LPT wind tunnel capability, that is cheaper, quicker, and more data-rich than experiments. The current study applies PyFR, a Python based Computational Fluid Dynamics (CFD) solver, to fifth-order accurate petascale DNS of compressible flow over a three-dimensional MTU-T161 LPT blade with diverging end walls at a Reynolds number of 200, 000 on an unstructured mesh with over 11 billion degrees-of-freedom per equation. Various flow metrics, including isentropic Mach number distribution at mid-span, surface shear, and wake pressure losses are compared with available experimental data and found to be in agreement. Subsequently, a more detailed analysis of various flow features is presented. These include the separation/transition processes on both the suction and pressure sides of the blade, end-wall vortices, and wake evolution at various span-wise locations. The results, which constitute one of the largest and highest-fidelity CFD simulations ever conducted, demonstrate the potential of high-order accurate GPU-accelerated CFD as a tool for delivering industrial DNS of LPT blades.