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.

计算模拟越来越多地支持更轻、更高效和更高性能的飞行器的设计。当前的计算能力已经成功地促进了航空航天设计的许多进步，但由于实际的限制，在选择用于表示湍流的模型方面仍然存在挑战。由于计算资源有限，工程设计的计算模拟通常会忽略湍流的复杂特征，用于近似缺失物理场的模型包含必须根据数据进行校准的参数，这对于未知流动来说是一个挑战，限制了其准确性。最近，高效使用机器学习和约束优化技术开发了在流动模拟过程中校准复杂模型参数的数值方法，这些方法在简单的湍流流动中取得了成功，但尚未应用于空气动力学中遇到的复杂流动。该项目旨在开发用于校准实际空气动力学流动模拟的湍流模型的方法，这将提高其对具有挑战性的配置的预测准确性。要开发的优化方法将广泛适用于整个工程领域，而不仅限于。空气动力学，并将以开源、高性能软件包的形式公开提供。该项目将通过开发用于湍流分离的大涡流模拟的深度学习闭包和优化方法来满足对准确、高效的计算流体动力学模型的需求该模型将使用基于伴随的方法在可压缩纳维-斯托克斯方程上进行优化，这将通过避免构建高维梯度来实现有效的数据同化。由此产生的模型将实现显着的结果。与具有可比成本的最先进模型相比，精度得到提高，或者同等地，在可比精度下显着降低计算成本 将生成多个尾流几何形状和分离翼型流的高保真数值数据集作为优化过程的目标数据。此外，还将开发一类新型在线优化方法，以实现动态、无数据的闭合模型，该模型将直接从控制方程中学习，并且将开发混合、多尺度深度学习公式来模拟近壁湍流。科学界更广泛地感兴趣利用大型数据集和机器学习技术；因此，该项目有可能开发出跨学科广泛采用的方法，所产生的算法、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。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。

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.