High-Order Accurate Direct Numerical Simulation of Flow over a MTU-T161 Low Pressure Turbine Blade

The archive comprises snapshot, point-probe, and time-average data produced via a high-fidelity computational simulation of turbulent air flow over a low pressure turbine blade, which is an important component in a jet engine. The simulation was undertaken using the open source PyFR flow solver on over 5000 Nvidia K20X GPUs of the Titan supercomputer at Oak Ridge National Laboratory under an INCITE award from the US DOE. The data can be used to develop an enhanced understanding of the complex three-dimensional unsteady air flow patterns over turbine blades in jet engines. This could in turn lead to design of greener more fuel efficient aircraft. It could also be used to train a next-generation of Reynolds Averaged Navier-Stokes turbulence models via a machine learning approach, which would have broad applicability to a wide range of science and engineering problems.

本档案包含了对低气压涡轮叶片上湍流空气流动进行高保真计算模拟所生成的快照、点探测和时间平均值数据。该模拟采用开源的 PyFR 流体求解器在橡树岭国家实验室的泰坦超级计算机的5000多块 Nvidia K20X GPU 上进行，此项目获得美国能源部 INCITE 奖励。这些数据可用于深化对喷气发动机涡轮叶片上复杂三维非定常空气流动模式的理解。这进而可能导致设计出更加节能的绿色飞机。此外，该数据还可用于通过机器学习方法训练下一代雷诺平均纳维-斯托克斯湍流模型（Reynolds Averaged Navier-Stokes turbulence models），这将广泛适用于众多科学和工程问题。