CDS&E: Reinforcement learning for robust wall models in large-eddy simulations

CDS

基本信息

批准号：
2152705
负责人：
Jane Bae
金额：
$ 33.53万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152705&HistoricalAwards=false
关键词：
CDS&amp Reinforcement learning robust wall

项目摘要

Simulations of wall-bounded turbulent flows have become a key element in the design cycle of wind farms and aircraft, and a major factor in the predictive capabilities of simulations of atmospheric flows. Due to the high Reynolds numbers associated with these flows, simulations resolving all scales of motion are not attainable with current computing capabilities. Specifically, wall models are necessary to overcome the prohibitive grid resolution requirements in the near-wall region. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to accurately model the near-wall dynamics. The principal aim of this project is to develop a robust wall model that can accurately predict the near-wall dynamics. The project will also encompass significant educational activities, including a multi-year undergraduate summer research program for the under-represented minority groups.The goal of the project is to develop a robust wall model for large-eddy simulations through reinforcement learning. Presently, the development of the state-of-the-art wall models relies on Reynolds-averaged Navier-Stokes parametrizations with an explicit or implicit assumption of a particular flow state close to the wall. These assumptions limit the robustness and applicability of the model and often lead to erroneous predictions of separation and laminar-to-turbulent transition, both of which are crucial components in external aerodynamics. By utilizing reinforcement learning methods, the project will allow the development of novel wall models that can adapt to various flow configurations based on the instantaneous flow input. The wall modeling problem will be cast as a control problem, where the discovered model is optimized to accurately reproduce the quantities of interest by automating the exploration of the relevant flow physics. The development of the proposed wall model will advance the state-of-the-art in the simulation of high-Reynolds-number turbulent flows in complex external aerodynamic applications. This will provide a means to obtain cheap and reliable simulations of complex flows such as flow over an aircraft.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

壁面湍流模拟已成为风电场和飞机设计周期中的关键要素，也是大气流模拟预测能力的主要因素。由于与这些流动相关的高雷诺数，当前的计算能力无法实现解析所有运动尺度的模拟。具体来说，墙模型对于克服近墙区域中令人望而却步的网格分辨率要求是必要的。来自实验和模拟的大量数据以及机器学习的出现推动了湍流建模工作。然而，由于启发式和监督学习无法准确模拟近壁动力学，湍流的模拟仍然受到阻碍。该项目的主要目的是开发一个稳健的壁模型，可以准确预测近壁动力学。该项目还将包括重要的教育活动，包括针对代表性不足的少数群体的多年本科生暑期研究计划。该项目的目标是通过强化学习开发用于大涡流模拟的稳健墙模型。目前，最先进的壁模型的开发依赖于雷诺平均纳维斯托克斯参数化，并对靠近壁的特定流动状态进行显式或隐式假设。这些假设限制了模型的稳健性和适用性，并且经常导致分离和层流到湍流转变的错误预测，而这两者都是外部空气动力学的关键组成部分。通过利用强化学习方法，该项目将允许开发新颖的墙模型，该模型可以根据瞬时流量输入适应各种流量配置。壁建模问题将被转化为控制问题，其中发现的模型被优化，通过自动探索相关的流动物理来准确地再现感兴趣的数量。所提出的壁模型的开发将推进复杂外部空气动力学应用中高雷诺数湍流模拟的最先进水平。这将为获得复杂流动（例如飞机上的流动）进行廉价且可靠的模拟提供一种方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。