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.
Highlights
Commercial air transportation moved over 4.3 billion passengers in 2018 and produced upwards of 800 million tonnes of CO2 and other Greenhouse Gases (GHG) [1,2]
In order to reduce weight and CO2 emissions, modern turbines are designed to use as few a blades as possible. This results in individual blades being subjected to higher-loading, which can lead to fully-separated flow over the aft-portion of each blade, and the introduction of complex, unsteady, three-dimensional flow phenomena. These can have a detrimental effect on overall aerodynamic efficiency [5], which translates almost directly to an increase in specific fuel consumption [6], and GHG emissions
Note that point Pi/S j is the z-wise projection of point Pi onto slice S j
Summary
Commercial air transportation moved over 4.3 billion passengers in 2018 and produced upwards of 800 million tonnes of CO2 and other Greenhouse Gases (GHG) [1,2]. In order to reduce weight and CO2 emissions, modern turbines are designed to use as few a blades as possible. This results in individual blades being subjected to higher-loading, which can lead to fully-separated flow over the aft-portion of each blade, and the introduction of complex, unsteady, three-dimensional flow phenomena. Taken together, these can have a detrimental effect on overall aerodynamic efficiency [5], which translates almost directly to an increase in specific fuel consumption [6], and GHG emissions. There exists a trade-off between reducing the number of blades — and total turbine weight — while maintaining an appropriate level of aerodynamic performance
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