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

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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.