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

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，因为它们在该领域内具有相关性和影响力。涉及空气、油和水的三相流特别常见，但由于其复杂性而知之甚少。为了准确预测特定多相流态的发生并提高实际应用中的效率，更好地了解这些流动至关重要。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来说往往是不切实际的。因此，需要将基于联合收割机物理的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）算法来实现。该项目的重点是开发CFD和ML的混合方法，以及在相关工业应用中的实施。CFD将使用Fluidity进行，Fluidity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。Fluidity使用移动有限元/控制体积方法，可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hr-自适应性，这是h-自适应性和r-自适应性的组合，前者改变网格的连接性，后者重新定位其顶点。在实践中，这意味着可以根据感兴趣的场（例如，压力或速度）来增加或减少某些区域中的分辨率。例如，在一个多相流与移动界面，可以获得高分辨率的界面附近，因为它的移动和较低的分辨率远离它。在流动的情况下，通过一个机构，这种方法可以保持高的网格分辨率在湍流区的尾流，同时保留在远场的粗网格。与固定网格方法相比，ML可以节省计算量和存储空间。近年来，ML已被应用于一系列CFD应用中。感兴趣的一个主要领域是将ML应用于湍流闭合模型。Beck等人（2019）使用深度神经网络（DNN）通过在DNS数据上训练它们来开发子网格规模（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，但他们证实了“数据知情”闭合模型具有潜力，并建议在该领域开展进一步工作。另一个与多相流特别相关的应用是通过使用ML分类算法。Guillén-Rondon等人（2018）使用支持向量机（SVM）来预测两相气液流中的流型，使用实验训练数据。该项目将完全是计算的，旨在将ML和CFD用于多相流问题。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。

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.