Exploiting non-equilibrium turbulence in design using DNS and Machine Learning

目前大型民用涡扇风机设计的趋势是降低转速，增大风机直径。这降低了风扇压力比，增加了旁路比，提高了发动机的推进效率，但这些因素对风扇设计者构成了挑战，新的设计空间正在探索中，远离了当前的大多数设计和经验。这使得准确的低阶(雷诺平均的N-S-S-RANS)模型变得至关重要，然而，以前的工作已经发现了在亚音速压气机情况下用工业标准湍流模型预测湍流边界层非平衡区(湍流产生超过耗散)的缺点。本项目旨在了解跨音速湍流边界层非平衡区的损失机制，并通过回答以下研究问题来研究如何改进低阶模型以更好地捕捉这些现象：1.非平衡湍流对于跨音速边界层的重要性是什么？2.当前行业标准湍流模型是否捕捉到非平衡效应？3.如果现有模型不够充分，如何对它们进行修改以改进对非平衡行为的预测？该项目将利用高保真计算流体力学技术，解决流动中所有(直接数值模拟)或大部分(大涡模拟-LES)尺度范围的湍流结构，提供常规(RANS)CFD或实验所不具备的流动物理细节。通过这项工作产生的数据，将探索在湍流模拟中创新的数据驱动技术，以改进适用于工业设计的低阶方法中的非平衡现象的预测。还将努力改进现有的湍流模型，例如参照域名系统数据调整模型常数，以便更好地预测这些流动。

The current trend in fan design for large civil turbofans is to reduce the speed, and increase the diameter of the fan. This gives a reduced fan pressure ratio and increased bypass ratio, improving the propulsive efficiency of the engine, however these factors present a challenge to the fan designer, with new areas of the design space being explored, well away from the majority of current designs and experience. This makes accurate low order (Reynolds Averaged Navier Stokes - RANS) models crucial, however previous work has identified shortcomings with the prediction of the non-equilibrium region (where turbulence production exceeds dissipation) of turbulent boundary layers by an industry standard turbulence model in subsonic compressor cases.This project aims to gain an understanding of loss mechanisms in the non-equilibrium region of transonic turbulent boundary layers, and investigate how low order models may be improved to better capture these phenomena by answering the following research questions:1. What is the importance of non-equilibrium turbulence for transonic boundary layers? 2. Do current industry standard turbulence models capture non-equilibrium effects? 3. If current models are inadequate, how may they be modified to improve the prediction of non-equilibrium behaviour? The project will utilise high fidelity computational fluid dynamics techniques that resolve all (Direct Numerical Simulation - DNS) or much (Large Eddy Simulation - LES) of the range of scales of turbulent structures in the flow, giving a detailed insight into the flow physics that is not available with conventional (RANS) CFD or experiments. With the data generated with this work, innovative data-driven techniques in turbulence modelling will be explored to improve the prediction of non-equilibrium phenomena in low order methods suitable for use in industry for design. Efforts will also be made to improve existing turbulence models, such as by tuning modelling constants with reference to the DNS data to better predict these flows.