Hybrid Machine-Learning and Computational Fluid Dynamics Methods in the Energy Industry

能源行业中的混合机器学习和计算流体动力学方法

• 批准号: 2367735
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2367735
• 关键词: Hybrid; Machine; Learning; Computational; Fluid

Improving the modelling and simulation of turbulent and multiphase flows is a key issue within the energy industry due to their relevance and impact within the field. Three-phase flows involving air, oil and water are particularly common but are poorly understood due to their complexity. Gaining a better understanding of these flows is crucial in order to accurately predict the occurrence of specific multiphase flow regimes and increase efficiency in practical applications.Most analysis relies on Computational Fluid Dynamics (CFD) modelling, however the large computational costs associated with this can often be impractical for industrial uses involving large number of hyperparameters. Consequently, there is a need for hybrid models which combine physics based CFD models with statistical methods in order to reduce computational costs and account for uncertainty. This can be achieved through the use of Machine Learning (ML) algorithms. This project focuses on both the development of hybrid methods coupling CFD and ML, and their implementation on relevant industrial applications.CFD will be carried out with Fluidity, which is an open source multi-phase CFD code capable of numerically solving the Navier-Stokes and field equations. Fluidity uses a moving Finite Element/Control Volume method which enables anisotropic mesh adaptivity on unstructured meshes for time dependant problems (AMCG, 2015). To modify the mesh, Fluidity uses hr-adaptivity, which is a combination of h-adaptivity and r-adaptivity, with the former changing the connectivity of the mesh and the latter relocating its vertices. In practice, this mean that the resolution can be increased or decreased in certain areas depending on the field of interest, e.g. pressure or velocity. For example, in a multiphase flow with a moving interface, a high resolution can be obtained near the interface as it moves and a lower resolution further away from it. In the case of flow past a body, this method can maintain a high mesh resolution in turbulent areas in the wake while retaining a coarse mesh in the far field. This results in improved computational and storage savings compared to fixed mesh methods.In recent years, ML has been applied to a range of CFD applications. A major field of interest is applying ML to turbulence closure models. Beck et al. (2019) used Deep Neural Networks (DNN) to develop subgrid-scale (SGS) models by training them on DNS data, as opposed to physics based SGS models. While it was found that purely data-based SGS models were not applicable to practical applications, they confirmed that "data-informed" closure models have potential and suggest further work in that area.Another particularly relevant application with multiphase flows is through the use of ML classification algorithms. Guillén-Rondon et al. (2018) used support vector machines (SVM) to predict flow patterns in two-phase gas-liquid flow, usingexperimental training data. The project will be entirely computational and will aim to relate ML and CFD for multiphase flow problems. This will be achieved through the simulation of multiphase flows using Fluidity, the implementation of ML algorithms with scikit-learn, and the development of codes coupling the two together.

由于其在该领域的相关性和影响，改进湍流和多相流的建模和模拟是能源行业的一个关键问题。涉及空气、油和水的三相流尤其常见，但由于其复杂性，人们对其了解甚少。为了准确预测特定多相流流型的发生并提高实际应用中的效率，更好地了解这些流动是至关重要的。大多数分析依赖于计算流体动力学（CFD）建模，然而，与此相关的大量计算成本对于涉及大量超参数的工业应用通常是不切实际的。因此，需要将基于物理的CFD模型与统计方法相结合的混合模型，以降低计算成本并考虑不确定性。这可以通过使用机器学习（ML）算法实现。本项目侧重于CFD和ML耦合的混合方法的开发，以及它们在相关工业应用中的实现。CFD将使用Fluidity进行，这是一个开源的多相CFD代码，能够数值求解Navier-Stokes方程和场方程。流动性使用移动有限元/控制体积方法，该方法可以在非结构化网格上实现各向异性网格自适应，以解决时间相关问题（AMCG, 2015）。为了修改网格，Fluidity使用了h-adaptivity，它是h-adaptivity和r-adaptivity的组合，前者改变网格的连通性，后者重新定位其顶点。在实践中，这意味着分辨率可以根据感兴趣的领域（例如压力或速度）在某些区域增加或减少。例如，在具有移动界面的多相流中，当界面移动时，可以在界面附近获得高分辨率，而远离界面时分辨率较低。在流过物体的情况下，该方法可以在尾迹湍流区域保持高网格分辨率，而在远场保持粗网格。与固定网格方法相比，这可以改善计算和存储节省。近年来，机器学习已应用于一系列CFD应用。一个主要的兴趣领域是将ML应用于湍流闭合模型。Beck等人（2019）使用深度神经网络（DNN）通过对DNS数据进行训练来开发子电网规模（SGS）模型，而不是基于物理的SGS模型。虽然发现纯粹基于数据的SGS模型不适用于实际应用，但他们确认了“数据通知”闭井模型的潜力，并建议在该领域开展进一步的工作。另一个与多相流特别相关的应用是通过使用ML分类算法。guill<s:1> - rondon等人（2018）利用实验训练数据，使用支持向量机（SVM）预测气液两相流的流动模式。该项目将完全是计算性的，旨在将ML和CFD与多相流问题联系起来。这将通过使用Fluidity模拟多相流、使用scikit-learn实现ML算法以及开发将两者耦合在一起的代码来实现。