EAGER: Embedded Deep Neural Nets for Predicting Reynolds Stresses in Complex Flows

EAGER：用于预测复杂流中雷诺应力的嵌入式深度神经网络

• 批准号: 1940551
• 负责人: John Eaton
• 金额: $ 29.94万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940551&HistoricalAwards=false
• 关键词: EAGER; Embedded; Deep; Neural; Nets

Engineers rely on computational simulation of turbulent flows prior to costly experimental testing to design automobiles, ships, jet engines, wind turbine arrays, and many other flow systems. Realistic turn-around times from concept to solution requires using approximate models to represent the effects of turbulence in the design process. Direct numerical simulations capturing all details of the flow are too expensive for practical full-scale systems, and standard turbulence models are unable to accurately predict the complex, three-dimensional flows pervasive throughout engineering systems. Contemporary machine learning algorithms are creating a paradigm shift in the information that can be gleaned from data and the scale of the data sets that can be efficiently processed. The goal of this project is to improve turbulence models using a data-driven approach which leverages the massive data sets produced by direct numerical simulation of the flows. The fundamental hypothesis of this research is that a machine learning model which accurately predicts the turbulent stresses for a given mean flow field will improve simulation results when implemented in a Reynolds-Averaged Navier-Stokes code. This project will develop improved models for the anisotropy tensor and terms in the turbulent kinetic energy transport equation using deep neural networks. Neural networks will be trained using only direct numerical simulation data and implemented directly in a computational fluid dynamics solver so that the predictions are independent of errors in any baseline model: a substantial advancement over existing discrepancy-based methods. Development of interpretability methods will elevate neural networks from black box tools to trustable models with clearer links between predictions and the underlying flow structures. Techniques for identifying flow regions where the neural net is poorly trained will produce robust machine-learned models that do not degrade computational fluid dynamics predictions below baseline model performance. Our approach also introduces corrective terms into the basic governing equations. Therefore, the lessons learned will provide a framework for future modeling work in any mechanics-based engineering discipline. The models will be tested using experimental data for a number of industrially relevant flows that have caused difficulty for conventional models.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.

工程师们在昂贵的实验测试之前依靠湍流的计算模拟来设计汽车、船舶、喷气发动机、风力涡轮机阵列和许多其他流动系统。从概念到解决方案的实际周转时间需要使用近似模型来表示设计过程中湍流的影响。直接数值模拟捕捉所有细节的流动是太昂贵的实际全尺寸的系统，标准的湍流模型无法准确预测复杂的，三维流动遍布整个工程系统。当代机器学习算法正在从数据中收集的信息以及可以有效处理的数据集规模中创造范式转变。该项目的目标是使用数据驱动方法改进湍流模型，该方法利用直接数值模拟产生的大量数据集。 本研究的基本假设是，一个机器学习模型，准确地预测湍流应力为一个给定的平均流场将改善模拟结果时，在一个平均Navier-Stokes代码实现。该项目将使用深度神经网络开发湍流动能传输方程中各向异性张量和项的改进模型。神经网络将仅使用直接数值模拟数据进行训练，并直接在计算流体动力学求解器中实现，以便预测与任何基线模型中的误差无关：这是对现有基于差异的方法的重大进步。 可解释性方法的发展将把神经网络从黑盒工具提升到可信赖的模型，在预测和底层流结构之间建立更清晰的联系。用于识别神经网络训练不良的流动区域的技术将产生鲁棒的机器学习模型，该模型不会将计算流体动力学预测降低到基线模型性能以下。我们的方法还引入了校正项的基本控制方程。因此，经验教训将提供一个框架，为未来的建模工作，在任何机械为基础的工程学科。 该模型将使用实验数据进行测试，用于一些工业相关的流动，这些流动给传统模型带来了困难。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。