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.

壁挂式湍流的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。由于与这些流量相关的较高的雷诺数，因此解决所有运动尺度的模拟无法实现当前的计算能力。具体而言，墙模型对于克服近壁区域的过度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为湍流建模工作提供了推动力。然而，由于启发式方法和监督学习以准确对近壁动力学建模的学习，湍流的模拟仍然阻碍了。该项目的主要目的是开发一个可以准确预测近壁动力学的强大墙模型。该项目还将涵盖重大的教育活动，包括针对代表性不足的少数群体的多年本科夏季研究计划。该项目的目的是通过增强学习开发一个强大的大涡模拟墙模型。目前，最先进的墙模型的开发依赖于雷诺平均的Navier-Stokes参数化，具有明确或隐式的假设，即靠近壁的特定流量状态。这些假设限制了模型的鲁棒性和适用性，并且通常会导致分离和椎间盘向肿胀的跃迁的错误预测，这两者都是外部空气动力学中的关键组成部分。通过利用增强学习方法，该项目将允许开发基于瞬时流入输入的各种流程配置的新型墙模型。墙壁建模问题将被施加为一个控制问题，在其中优化了发现的模型，以通过自动探索相关流体物理学来准确地重现感兴趣的数量。在复杂的外部空气动力学应用中，提出的壁模型的开发将推进模拟高雷诺数驱动流动的最先进。这将提供一种手段，以获取对飞机流量等复杂流量的廉价模拟。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持的。