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

计算模拟越来越多地使设计更轻，更高效，更高性能的飞行器成为可能。目前的计算能力已经成功地帮助了航空航天设计的许多进步，但在选择用于表示湍流的模型方面仍然存在挑战。由于计算资源的实际限制，工程设计的计算模拟通常忽略湍流的复杂特征。用于近似缺失物理的模型包含必须根据数据进行校准的参数，这对于未知流来说是具有挑战性的，并且通常具有简单的数学形式，这限制了它们的准确性。最近，已经开发了使用来自机器学习和约束优化的技术来在流动模拟期间校准复杂模型的参数的有效数值方法。这些方法对于简单的紊流是成功的，但对于空气动力学中遇到的复杂流动还没有应用。该项目的主要目标是开发用于实际空气动力学流模拟的湍流模型校准方法，这将提高其对具有挑战性的配置的预测精度。开发的优化方法将广泛应用于工程领域，不限于空气动力学，并将以开源的高性能软件包公开提供。该项目将通过开发用于湍流分离流和再循环流的大涡模拟的深度学习闭包和优化方法，满足对精确、高效的计算流体动力学模型的需求。这些模型将使用基于伴随的方法在可压缩纳维尔-斯托克斯方程上进行优化，这将通过避免需要构建高维梯度来实现有效的数据同化。所得到的模型将使显着的精度改进相比，国家的最先进的模型，可比的成本，或等效的，显着降低计算成本可比的精度。将生成几种尾流几何形状和分离翼型流的高保真数值数据集，作为优化程序的目标数据。此外，还将开发一类新的在线优化方法，以实现直接从控制方程学习的动态无数据闭合模型，并将开发一种混合多尺度深度学习公式来模拟近壁湍流。科学界更广泛地对利用大型数据集和机器学习技术感兴趣;因此，该项目有可能开发出跨学科广泛采用的方法。由此产生的算法，方法，数据集和代码将被传播，以促进在空气动力学社区和跨学科的采用。

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, and often have simple mathematical forms that 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.