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

使用 DNS 和机器学习在设计中利用非平衡湍流

• 批准号: 2447952
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2447952
• 关键词: Exploiting; non; equilibrium; turbulence; design

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.

目前大型民用涡扇发动机风扇的设计趋势是降低转速、增大直径。这使得风扇压力比降低，涵道比增加，提高了发动机的推进效率，然而这些因素对风扇设计者提出了挑战，正在探索设计空间的新领域，远离大多数当前的设计和经验。这使得精确的低阶（雷诺平均Navier Stokes - RANS）模型至关重要，但是以前的工作已经确定了非平衡区域预测的缺点在亚音速压气机情况下，通过工业标准湍流模型对湍流边界层（湍流产生超过耗散）进行了研究。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，研究如何改进低阶模型以更好地捕捉这些现象：1.跨音速边界层非平衡紊流的重要性是什么？2.当前的工业标准湍流模型是否能捕捉到非平衡效应？3.如果目前的模型是不够的，如何修改它们，以改善非平衡行为的预测？该项目将利用高保真计算流体动力学技术，解决所有（直接数值模拟- DNS）或大部分（大涡模拟- LES）的范围内的湍流结构的流动，提供了一个详细的洞察到流动物理，这是不提供与传统的（RANS）CFD或实验。利用这项工作产生的数据，将探索湍流建模中的创新数据驱动技术，以改善适用于工业设计的低阶方法中非平衡现象的预测。还将努力改进现有的湍流模型，例如参照DNS数据调整模型常数，以更好地预测这些流动。