CBET-EPSRC: Deep Learning Closure Models for Large-Eddy Simulation of Unsteady Aerodynamics

CBET-EPSRC：用于非定常空气动力学大涡模拟的深度学习收敛模型

• 批准号: 2215472
• 负责人: Jonathan MacArt
• 金额: $ 36.3万
• 依托单位: University of Notre Dame
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215472&HistoricalAwards=false
• 关键词: CBET; EPSRC; Deep; Learning; Closure

Computational simulations increasingly enable the design of lighter, more efficient, and higher-performance flight vehicles. Current computational capabilities have successfully aided many advances in aerospace design, but challenges remain in the selection of the models used to represent turbulence. Due to practical limits on computing resources, computational simulations for engineering design typically neglect the intricate features of turbulence. The models used to approximate the missing physics contain parameters that must be calibrated to data, which is challenging for unknown flows. Simple mathematical forms in these models limit their accuracy. Recently, efficient numerical methods to calibrate the parameters of complex models during flow simulations have been developed using techniques from machine learning and constrained optimization. These methods have been successful for simple turbulent flows but have not been applied to the complex flows encountered in aerodynamics. The principal objective of this project is to develop methods by which to calibrate turbulence models for simulations of practical aerodynamic flows, which will enhance their predictive accuracy for challenging configurations. The optimization methods to be developed will be broadly applicable across engineering fields, not limited to aerodynamics, and will be made publicly available in an open-source, high-performance software package.This project will address the need for accurate, efficient computational fluid dynamics models by developing deep learning closures and optimization methods for large-eddy simulations of turbulent separated and recirculating flows. The models will be optimized over the compressible Navier–Stokes equations using an adjoint-based approach, which will enable efficient data assimilation by avoiding the need to construct high-dimensional gradients. The resulting models will enable significant accuracy improvements compared to state-of-the-art models for comparable cost, or equivalently, significantly reduced computational cost for comparable accuracy. High-fidelity numerical datasets for several wake geometries and separated airfoil flows will be generated as target data for the optimization procedure. Additionally, a new class of online optimization methods will be developed to enable dynamic, data-free closure models that will learn directly from the governing equations, and a hybrid, multiscale deep learning formulation will be developed to model near-wall turbulent flows. The scientific community more broadly is interested in leveraging large datasets and machine learning techniques; this project therefore has potential to develop methods to be widely adopted across disciplines. The resulting algorithms, methods, datasets, and codes will be disseminated to foster adoption within the aerodynamics community and across scientific disciplines.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.

计算模拟越来越能够设计更轻，更高效和高性能的飞行车辆。当前的计算能力已成功地帮助了航空航天设计的许多进步，但是在选择代表湍流的模型的选择中仍然存在挑战。由于计算资源的实际限制，工程设计的计算模拟通常忽略了湍流的复杂特征。用于近似丢失物理的模型包含必须校准数据的参数，该参数对于未知流量面临挑战。这些模型中的简单数学形式限制了它们的准确性。最近，使用机器学习和约束优化的技术开发了流动模拟过程中复杂模型参数的有效数值方法。这些方法已成功用于简单的湍流，但尚未应用于空气动力学中遇到的复杂流动。该项目的主要目的是开发方法，通过该方法来校准湍流模型，以模拟实用空气动力学流，这将提高其对挑战配置的预测准确性。要开发的优化方法将在工程领域（不仅限于空气动力学）广泛地适用，并将在开源的高性能软件包中公开提供。该项目将通过为大型模拟的大型模拟和RecReclated Spairtated Glows的大型模拟而开发出准确，有效的计算流体动态模型的需求。这些模型将使用基于伴随的方法在可理解的Navier-Stokes方程上进行优化，这将通过避免构造高维梯度的需求来实现有效的数据同化。与最先进的成本相比，与最先进的模型相比，最终的模型将可以提高准确性，或者同等的，显着降低了计算成本以达到可比精度。将生成用于优化过程的目标数据的几种唤醒几何和分离的机翼流的高保真数值数据集。此外，将开发一类新的在线优化方法，以启用动态，无数据的闭合模型，该模型将直接从管理方程式中学习，并将开发一个混合的，多尺度的深度学习公式来建模近乎墙的动荡流。科学界对利用大型数据集和机器学习技巧感兴趣。因此，该项目有可能开发在跨学科中广泛采用的方法。由此产生的算法，方法，数据集和代码将被传播，以促进空气动力学界和跨科学学科的采用。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审查标准通过评估来通过评估来支持的。

项目成果

Deep learning closure models for large-eddy simulation of flows around bluff bodies

用于钝体周围流动大涡流模拟的深度学习闭合模型

• DOI: 10.1017/jfm.2023.446
• 发表时间: 2023
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Sirignano, Justin;MacArt, Jonathan F.
• 通讯作者: MacArt, Jonathan F.