Collaborative Research: CDS&E: Generalizable RANS Turbulence Models through Scientific Multi-Agent Reinforcement Learning

合作研究：CDS

• 批准号: 2347422
• 负责人: Robert Moser
• 金额: $ 35万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347422&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS&E; Generalizable; RANS

Reliable, predictive computational simulations of turbulent flows are essential for the advancement of key technologies in sectors ranging from aerospace and automotive to power generation. However, the utility of currently available simulations is limited, sometimes severely, by the unreliability of turbulence models. The most common computations of turbulence are intended to simulate only the mean or average velocity, with the effects of turbulence modeled, because in many applications this is the primary quantity of interest. However, the deployed models of turbulence are widely recognized as having limited reliability for complex flows of technological interest. The core reason for this challenge is the lack of universally applicable closure terms. This project addresses this shortcoming by formulating turbulence models to account for a more general description of the characteristics of the turbulence. This will yield models that are generally applicable, including complex turbulent flows. It will also yield technological advances in many important domains, including aeronautics, propulsion, power generation and wind energy that are presently hindered by the lack of reliable models of complex, turbulent flows. By developing reliable, predictive, and broadly applicable turbulence models, this project will have profound impacts on these fields, with the potential of enabling ground-breaking improvements in technologies important to our country and society.The objective of the project is to develop reliable Reynolds averaged Navier-Stokes models that generalize to complex turbulent flows. The approach is based on the hypothesis that current turbulence models do not retain a sufficiently rich representation of the statistical state of turbulence. A set of structure tensors that characterize the anisotropy and inhomogeneity of turbulence are proposed as a candidate for a sufficient statistical turbulence state space. Evolution equations for these structure tensors will be developed which will necessarily include unknown scalar functions of scalar invariants. These functions will be learned from turbulence statistical data using scientific multi-agent reinforcement learning. The required data will be obtained from direct numerical simulations and experiments. Models resulting from this process will be tested on a variety of complex turbulent flows. The potential impact of reliable Reynolds averaged turbulence models is hard to over-state, as they would greatly increase the value of computational fluid dynamics as a tool of science and engineering. In addition, the training of two graduate students under this project in the development of mathematical and data-informed models for complex systems will contribute to the highly skilled workforce required to address a broad class of complex problems facing our country and the world.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.

湍流的可靠，预测计算模拟对于从航空航天和汽车到发电的行业的关键技术的发展至关重要。但是，由于湍流模型的不可靠性，当前可用模拟的实用性有时受到严重限制。湍流的最常见计算旨在仅模拟湍流的效果，因为在许多应用中，这是利益的主要量。但是，部署的湍流模型被广泛认为是对复杂的技术感兴趣流的可靠性有限。造成这一挑战的核心原因是缺乏普遍适用的封闭条款。该项目通过制定湍流模型来解决这一缺点，以说明湍流特征的更一般描述。这将产生通常适用的模型，包括复杂的湍流。它还将在许多重要领域中产生技术进步，包括航空，推进，发电和风能，这些能源目前由于缺乏复杂，湍流的可靠模型而阻碍。通过开发可靠，预测性和广泛适用的湍流模型，该项目将对这些领域产生深远的影响，并有可能对我们的国家和社会重要的技术进行突破性的改进。该项目的目的是开发可靠的雷诺，平均雷诺，平均Navier-Stokes模型，从而将复杂的湍流流概述为复杂的湍流。该方法基于以下假设：当前的湍流模型没有保留湍流统计状态的足够丰富的表示。提出了一组表征各向异性和湍流不均匀性的结构张量，以作为足够统计的湍流状态空间的候选者。这些结构张量的演化方程将开发出来，这必然包括标量不变的未知标量函数。这些功能将使用科学多代理增强学习从湍流统计数据中学到。所需的数据将从直接的数值模拟和实验中获得。该过程产生的模型将在各种复杂的湍流上进行测试。可靠的雷诺平均湍流模型的潜在影响很难过于统计，因为它们将大大提高计算流体动力学的价值，作为科学和工程的工具。此外，在该项目的开发中，对复杂系统的数学和数据知识模型的两名研究生的培训将有助于解决我们国家和世界面临的广泛的复杂问题所需的高技能劳动力。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛影响来评估NSF的法定任务，并被认为是值得的支持。