CDS&E: Data-driven Discovery of Probabilistic Closures in Turbulent Flows

CDS

基本信息

批准号：
2152803
负责人：
Peyman Givi
金额：
$ 45万
依托单位：
University of Pittsburgh
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：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152803&HistoricalAwards=false
关键词：
CDS&E Data driven Discovery Probabilistic

项目摘要

Reliable predictive modeling of turbulent flows has remained one of the grand challenges of classical physics. This is because turbulent flows are multi-scale problems. Capturing all these scales using direct numerical simulations of turbulent flows remains cost prohibitive for the majority of the scientific and engineering applications. Therefore, in the predictive models for practical turbulent flows the small scales cannot be resolved and as a result the effect of these unresolved scales on the resolved scales must be modeled. However, except for very simple canonical flows, the functional form of these models are not known. This is the main source of uncertainty in turbulent flow predictions. With the recent development of scalable scientific machine learning algorithms as well as the growth of high performance computing resources, it is now possible to generate high-fidelity data and train machine learning models with a very large number of parameters. This opens up new opportunities to discover new turbulence models for some of the practical engineering problems and significantly improve the reliability of predictions in turbulent flows.It is widely known that one of the most accurate frameworks to discover turbulent models is a probabilistic one. However, probabilistic models are not as commonly used as the deterministic models mainly due to their computational costs. These models are expressed versus the evolution of probability density functions (PDFs), which can be very high-dimensional. Discovery of probabilistic turbulence models requires solving both the forward and inverse PDF transport equations. Classical scientific computing techniques are unable to solve this problem due to the their high dimensionality. In the project, a new physics-informed deep learning methodology will be developed and utilized to solve both forward and inverse PDF transport equations. If successful, this project will lead to the discovery of probabilistic turbulence models for diverse turbulent flows. Along with the development of technical tools, this work will also include integration with education, organization of new conferences, interaction with industry and governmental research labs, improvement of gender, ethnic and racial diversity, and expansion through K-12 outreach.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.

湍流的可靠预测建模仍然是古典物理学的巨大挑战之一。这是因为湍流是多尺度问题。使用湍流的直接数值模拟来捕获所有这些量表，对于大多数科学和工程应用程序而言，成本均高。因此，在实用湍流的预测模型中，小尺度无法解析，因此必须对这些未解决的量表对分辨率量表的影响进行建模。但是，除了非常简单的规范流，这些模型的功能形式尚不清楚。这是湍流预测中不确定性的主要来源。随着可扩展的科学机器学习算法以及高性能计算资源的增长的最新发展，现在可以生成具有大量参数的高保真数据和训练机器学习模型。这为发现一些实际工程问题的新湍流模型开辟了新的机会，并显着提高了湍流中预测的可靠性。众所周知，发现湍流模型的最准确的框架之一是概率的框架。但是，概率模型并不像确定性模型那样普遍使用，主要是由于其计算成本。这些模型与概率密度函数（PDF）的演变相比，这可能是非常高的维度。概率湍流模型的发现需要解决正向和逆PDF传输方程。经典的科学计算技术由于其高维度而无法解决此问题。在该项目中，将开发并利用一种新的物理知识深度学习方法来求解前进和逆PDF传输方程。如果成功，该项目将导致发现各种湍流的概率湍流模型。随着技术工具的开发，这项工作还将包括与教育，新会议的组织，与行业和政府研究实验室的互动，性别的改善，种族和种族多样性的改善以及通过K-112宣传的扩展。该奖项反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的crietia criter criter criter criter criter criter criter criteria crietia criteria criter critia criter critia criteria criter critia criteria均值得一提。