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.

雷诺平均纳维-斯托克斯 (RANS) 模拟和风洞测试已成为低压涡轮 (LPT) 叶片工业设计的首选工具。然而，人们对使用尺度解析模拟也越来越感兴趣，包括直接数值模拟 (DNS)。这些可以产生洞察力和数据，以支持用于 LPT 设计的改进 RANS 模型的开发。此外，它们可以支持虚拟 LPT 风洞能力，这比实验更便宜、更快、数据更丰富。当前的研究将 PyFR（一种基于 Python 的计算流体动力学 (CFD) 求解器）应用于三维 MTU-T161 LPT 叶片上的可压缩流的五阶精确千万亿次 DNS，该叶片在非结构化的雷诺数为 200,000 时具有发散端壁每个方程具有超过 110 亿个自由度的网格。将各种流量指标（包括中跨处的等熵马赫数分布、表面剪切和尾流压力损失）与可用的实验数据进行比较，发现它们是一致的。随后，对各种流动特征进行了更详细的分析。这些包括叶片吸力侧和压力侧的分离/过渡过程、端壁涡流和不同翼展位置的尾流演变。结果构成了有史以来规模最大、保真度最高的 CFD 模拟之一，证明了高阶精确 GPU 加速 CFD 作为提供 LPT 叶片工业 DNS 的工具的潜力。和尾流压力损失与可用的实验数据进行比较，发现是一致的。随后，对各种流动特征进行了更详细的分析。这些包括叶片吸力侧和压力侧的分离/过渡过程、端壁涡流和不同翼展位置的尾流演变。结果构成了有史以来规模最大、保真度最高的 CFD 模拟之一，证明了高阶精确 GPU 加速 CFD 作为提供 LPT 叶片工业 DNS 的工具的潜力。将尾流压力损失和尾流压力与可用的实验数据进行比较，发现结果一致。随后，对各种流动特征进行了更详细的分析。这些包括叶片的吸力侧和压力侧的分离/过渡过程，端壁涡旋以及在不同翼展方向位置的尾流演变。结果构成了有史以来规模最大、保真度最高的 CFD 模拟之一，证明了高阶精确 GPU 加速 CFD 作为提供 LPT 叶片工业 DNS 的工具的潜力。