CDS&E: Formalisms and Tools for Data-enabled Turbulence Modeling

CDS

• 批准号: 1507928
• 负责人: Karthikeyan Duraisamy
• 金额: $ 40万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1507928&HistoricalAwards=false
• 关键词: CDS& Formalisms; Tools; Data; enabled

The goal of the proposed research is to devise rigorous mathematical techniques that utilize large databases obtained by simulations to develop predictive models of turbulent flow. Even though the proposal is focused on turbulence, the tools to be developed will be of general applicability for data-driven modeling in other areas of science and engineering. Predictive models have not yet taken full advantage of the massive amounts of data being generated by the fluids community. New strategies are needed to extract information and modeling knowledge from data. Adjoint-driven inverse problems will be invoked to extract relevant modeling information from data. An important aspect of this approach is that the data is processed in the context in which it is needed for prediction. Domain-specific machine learning techniques will be used to convert information to modeling knowledge. In essence, the inverse solution infers functional deficiencies in the model and machine learning is used to reconstruct the missing functional form. The co-PIs plan to investigate how to identify and formulate a properly-posed data-driven-turbulence-modeling problem, the implications that these approaches have in more general data-driven computational physics applications, and the most effective ways to use machine learning in a predictive physics setting. Applications to be explored include transition to turbulence, thermal transport, and near-wall turbulent stress closures. The proposed work is expected to result in improved closure models for Reynolds-Averaged as well as hybrid Reynolds-Averaged/Large Eddy simulations. While the focus of the proposed work is on turbulent flow applications, several aspects of the formulation and tools will be of more general value to the field of data-driven physical modeling. Educational activities that would integrate machine learning into fluid dynamics courses are proposed. Tools and technologies will be shared with the community and the industry

拟议研究的目标是设计严格的数学技术，利用模拟获得的大型数据库来开发湍流的预测模型。尽管该提案的重点是湍流，但要开发的工具将普遍适用于其他科学和工程领域的数据驱动建模。预测模型尚未充分利用流体社区产生的大量数据。需要新的策略来从数据中提取信息和建模知识。伴随驱动的逆问题将被调用来从数据中提取相关的建模信息。这种方法的一个重要方面是，数据是在预测所需的上下文中处理的。特定领域的机器学习技术将用于将信息转换为建模知识。本质上，逆解推断模型中的功能缺陷，机器学习用于重建缺失的功能形式。co-PI计划研究如何识别和制定一个适当的数据驱动的随机建模问题，这些方法在更一般的数据驱动的计算物理应用中的影响，以及在预测物理环境中使用机器学习的最有效方法。待探讨的应用包括过渡到湍流，热传输，近壁湍流应力封闭。拟议的工作，预计将导致改进的封闭模型，平均以及混合的平均/大涡模拟。虽然所提出的工作的重点是湍流的应用，制定和工具的几个方面将更普遍的价值，以数据驱动的物理建模领域。提出了将机器学习融入流体动力学课程的教育活动。工具和技术将与社区和行业共享