Development and Implementation of Algorithms for Large-Scale CFD and Data Analytics

术语“数据分析”泛指从原始数据中提取有意义的结论的多学科科学。这类数据驱动的分析已成为金融、电子商务、天气预报、医疗保健、欺诈检测、社交媒体平台等许多应用领域不可或缺的组成部分。计算流体动力学(CFD)是工程师用来生成大型信息数据集的工具之一，他们试图从这些数据集预测复杂流体流动的行为。在过去的十年里，CFD研究人员已经开始在几个应用领域开发数据分析的力量，如湍流建模、不确定性分析和形状优化。随着工业继续更多地依赖数值模拟作为改进产品设计的工具，对用户友好、可靠和计算高效的方法和算法的需求不断增加，以实现大规模CFD和数据分析的无缝集成。拟议研究的总体目标是结合人工智能(AI)和基于物理的数字模拟的力量，开发一个创新和稳健的统一框架，用于将数据分析方法应用于CFD，特别是工业上感兴趣的流体流动。为了深入理解和制定端到端的方法，将对几个基准流动问题进行高保真的CFD模拟，以产生典型的流动参数大数据集。为此，我们将扩展我们的研究，使用三维Navier-Stokes方程的笛卡尔切割模板有限差分格式来模拟复杂几何形状的流体流动。考虑到CFD和AI的理想集成，我们的内部CFD代码将生成符合AI算法要求的专用格式的数据。这些数据集将用于训练和测试各种神经网络，以了解不同体系结构的优势和劣势。同时，将研究各种统计和机器学习方法，以确定它们在流体力学预测和其他工程科学领域的适用性。

The term "Data Analytics" broadly refers to the multidisciplinary science of extracting meaningful conclusions from raw data. Such data-driven analyses have become an integral component in many application areas such as finance, e-commerce, weather predictions, healthcare, fraud detection, social media platforms, etc. Computational Fluid Dynamics (CFD) is one of the tools that engineers use to generate large datasets of information from which they attempt to predict the behaviour of complex fluid flows. In the last decade, CFD researchers have begun to exploit the power of data analytics in several application areas, such as turbulence modelling, uncertainty analysis and shape optimization. The need for user-friendly, reliable and computationally efficient methodologies and algorithms for seamless integration of large-scale CFD and Data Analytics has increased as industries continue to rely more heavily on numerical simulation as a tool to improve their product design. The overall aim of the proposed research is to combine the power of artificial intelligence (AI) and physics-based digital simulations to develop an innovative and robust unified framework for applying Data Analytics methods in CFD, particularly to fluid flows of industrial interest. To gain a thorough understanding and formulate an end-to-end methodology, high-fidelity CFD simulations will be conducted for several benchmark flow problems to produce typical large datasets of flow parameters. For this purpose, we will extend our research on simulating fluid flows in complex geometries using the Cartesian cut-stencil finite difference formulation of the 3D Navier-Stokes equations. Keeping in mind the desired integration of CFD and AI, our in-house CFD code will generate data in specialized formats that conform to the requirements of the AI algorithms. These datasets will be used to train and test various neural networks to access the strengths and weaknesses of different architectures. In parallel, various statistical and machine learning methods will be investigated to determine their applicability in fluid mechanics predictions and other areas of engineering science.