High fidelity micro- and meso-scale computations and data-driven physics-informed models of particle-laden flows

颗粒流在自然界和工业中普遍存在，从地球科学和海洋科学中的河流泥沙传输，到过程工程中的流态化化学反应，到生物医学工程中的静脉和毛细血管的人体血液系统中的药物传输。虽然在颗粒流动力学方面已经有了大量的知识，但主要相间动量传递的复杂性是为什么研究人员和工程师仍然无法完全理解这类多相流的主要原因。当悬浮物不能再被视为稀释物时，这是许多应用中的一种常见情况，颗粒对邻近颗粒周围的流场产生强烈的扰动，导致颗粒之间的流体动力和扭矩波动很大，其量级往往与平均值相当。含颗粒流动的大尺度数值模型，如中尺度Euler-Lagrange(EL)模型和宏观Euler-Euler(EE)模型，依赖于相间动量和热传递的闭合定律。目前的闭合定律不能预测对EL和EE模拟的保真度至关重要的显著的粒子到粒子的波动。提高EL和EE模拟的逼真度是非常重要的，因为这两个模型具有实际和工业用途。该项目的重点是EL模型。高保真微尺度粒子分辨模拟(PRS)提供了详细的信息，不需要任何闭合，但需要非常大的计算资源，并且只能模拟小系统。这个项目的目标是设计完全新颖的相间转移模型，既能预测平均值，也能预测颗粒间的波动。为此，我们分析了由微尺度粗糙集产生的大数据集，并将从这种分析中学到的知识以数据驱动的物理-知情(DDPI)相间转移模型的形式传输到中尺度电致发光模型。虽然用传统方法分析大规模粗糙集仍然是一条有价值的研究途径，但我们使用机器学习和神经网络(NN)从数据中推断出额外的理解，并设计更高级的相间转移模型。这一方法代表了这一领域范式的彻底改变。DDPI模型代表了以前无法达到的颗粒流和目标保真度水平中的下一代相间转移模型。作为混合模型，它们结合了两个世界最好的东西：物理理解和机器学习。自然界和工业中无处不在的颗粒流动支持了持续研究的必要性。我们特别感兴趣的应用领域是工艺强化和绿色能源生产，包括减少这些工艺的环境足迹，以及开发涉及木材和阳光等可再生材料的可靠和有效的新技术。

Particle--laden flows are ubiquitous in nature and industry, ranging from sediment transport in rivers in earth and ocean science, fluidized bed chemical reactors in process engineering to drug transport in the human blood system of veins and capillaries in biomedical engineering. While a large body of knowledge already exists on particle-laden flow dynamics, the complexity of the dominant interphase momentum transfer is the primary reason why a complete understanding of this class of multiphase flow still escapes researchers and engineers. When the suspension cannot be regarded as dilute anymore, a common situation in many applications, particles strongly disturb the flow field around neighboring particles, leading to substantial particle--to--particle hydrodynamic force and torque fluctuations of magnitude often comparable to the mean value. Large-scale numerical models of particle-laden flows, such as the meso-scale Euler-Lagrange (EL) model and the macro-scale Euler-Euler (EE) model, rely on closure laws for the interphase momentum and heat transfer. The current closure laws are incapable of predicting the significant particle--to--particle fluctuations that are key to the fidelity of EL and EE simulations. Improving the fidelity of EL and EE simulations is of tremendous importance as these two models are of practical and industrial use. The project focuses on EL models. High fidelity micro--scale Particle-Resolved Simulation (PRS) supplies detailed information without the need for any closure but require very large computing resources and can simulate small systems only. The objective of this project is to design fully novel interphase transfer models that predict both the mean value and the particle-to-particle fluctuations. To do so, we analyze large data sets produced by micro-scale PRS and transfer the knowledge we learn from this analysis to the meso-scale EL models in the form of Data--Driven Physics--Informed (DDPI) models of interphase transfer. While the analysis of large PRS data sets with traditional methods remains a valuable research path, we use machine learning and neural networks (NN) to infer additional understanding from the data and to design more advanced interphase transfer models. This approach represents a complete change of paradigm in this field. DDPI models represent the next generation of interphase transfer models in particle--laden flows and target fidelity levels that were previously unattainable. As hybrid models, they bring together the best of two worlds: physical understanding and machine learning. The omnipresence of particle--laden flows in nature and industry supports the need for sustained research. Fields of application of particular interest to us are process intensification and green energy production, including the reduction of the environmental footprint of these processes and the development of reliable and efficient new technologies involving renewable materials such as wood and sunlight.