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

含颗粒流在自然界和工业中普遍存在，从地球和海洋科学中的河流中的沉积物输送，过程工程中的流化床化学反应器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输送。虽然大量的知识已经存在于颗粒负载的流动动力学，占主导地位的相间动量传递的复杂性是这类多相流的研究人员和工程师仍然无法完全理解的主要原因。当悬浮液不再被视为稀释时，这是许多应用中的常见情况，颗粒强烈地干扰邻近颗粒周围的流场，导致实质性的颗粒间流体动力和扭矩波动，其大小通常与平均值相当。大规模的数值模型，如中尺度的欧拉-拉格朗日（EL）模型和宏观尺度的欧拉-欧拉（EE）模型，依赖于封闭法律的相间动量和热量传递。目前的封闭法律是无法预测的显着粒子-粒子波动的EL和EE模拟的保真度的关键。提高EL和EE模拟的保真度是非常重要的，因为这两个模型是实际和工业用途。该项目侧重于EL模型。高保真微尺度粒子分辨模拟（PRS）提供了详细的信息，而不需要任何封闭，但需要非常大的计算资源，只能模拟小系统。该项目的目标是设计完全新颖的相间传递模型，预测的平均值和颗粒到颗粒的波动。要做到这一点，我们分析大数据集产生的微尺度PRS和转移的知识，我们从这个分析中学习到的中尺度EL模型的形式的数据驱动物理-知情（DDPI）模型的相间转移。虽然用传统方法分析大型PRS数据集仍然是一条有价值的研究路径，但我们使用机器学习和神经网络（NN）来从数据中推断出更多的理解，并设计更先进的相间转移模型。这一方法代表了这一领域范式的彻底改变。DDPI模型代表了下一代的相间传输模型在颗粒负载流和目标保真度水平，以前无法达到。作为混合模型，它们汇集了两个世界中最好的：物理理解和机器学习。在自然界和工业中，粒子流的无所不在支持了持续研究的必要性。我们特别感兴趣的应用领域是过程强化和绿色能源生产，包括减少这些过程的环境足迹，以及开发涉及木材和阳光等可再生材料的可靠高效的新技术。

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.