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

壁面湍流的模拟已经成为风电场和飞机设计周期中的一个关键因素，也是模拟大气流动预测能力的一个主要因素。由于与这些流动相关的高雷诺数，以目前的计算能力无法模拟解决所有尺度的运动。具体来说，壁面模型对于克服近壁面区域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为湍流建模工作提供了推动力。然而，由于启发式和监督学习无法准确地模拟近壁动力学，湍流的模拟仍然受到阻碍。本项目的主要目的是建立一个能够准确预测近壁动力学的鲁棒壁面模型。该项目还将包括重要的教育活动，包括为代表性不足的少数群体提供的多年本科夏季研究项目。该项目的目标是通过强化学习开发用于大涡模拟的鲁棒壁面模型。目前，最先进的壁面模型的发展依赖于雷诺平均Navier-Stokes参数化，并明确或隐含地假设壁面附近的特定流动状态。这些假设限制了模型的鲁棒性和适用性，并经常导致分离和层流到湍流过渡的错误预测，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的壁面模型，该模型可以根据瞬时流量输入适应各种流量配置。壁面建模问题将被视为一个控制问题，其中发现的模型被优化，通过自动化探索相关的流动物理来精确地再现感兴趣的数量。所提出的壁面模型的发展将推动复杂外部气动应用中高雷诺数湍流模拟的发展。这将提供一种廉价而可靠的模拟复杂气流的方法，例如飞机上空的气流。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.