Motivation This practical modelling course is designed to provide doctoral students with a comprehensive understanding of modelling and simulation techniques for studying and predicting the behaviour and properties of powder and granular materials, fluids, and suspensions. Objectives The course will cover three main areas of modelling: i) the discrete element method (DEM) for powders and granular materials; ii) computational fluid dynamics (CFD) for aerodynamics and hydrodynamics; and iii) the modelling of suspensions by combining CFD and DEM. Acquired skills and knowledge Through practical examples, readings, and hands-on assignments, students will develop the skills needed to model and analyse complex systems at various scales. 1. Concepts of computational modelling: an overview of computational modelling techniques, basic concepts, and applications in particle materials and fluid dynamics. 2. Discrete element method (DEM): fundamentals of DEM, particle interaction models, time integration schemes, parallelization strategies, and applications in powder and granular material simulations. 3. Computational fluid dynamics (CFD): governing equations for fluid flow, numerical methods for solving fluid flow problems, turbulence modelling, boundary conditions, and applications in aerodynamics and hydrodynamics. 4. Coupling DEM and CFD: basics of coupling DEM and CFD for modelling particle-fluid interactions, algorithms for two-way coupling, and applications in suspension and multiphase flow simulations. 5. Modelling of suspensions: particle-fluid interaction forces, drag models, lift forces, virtual mass forces, and applications in simulating suspensions. 6. Boundary conditions and mesh generation: techniques for creating appropriate boundary conditions and mesh generation for complex geometries in both DEM and CFD simulations. 7. Simulation software and programming: Working with popular DEM and CFD software packages, writing custom code for specific problems, and parallelization techniques for high-performance computing. 8. Model validation and verification: techniques for validating and verifying computational models, comparing simulation results with experimental data, and assessing model accuracy and limitations. 9. Advanced topics in DEM and CFD: recent developments and research trends in DEM and CFD, including multiscale modelling, adaptive mesh refinement, and machine learning-assisted modelling. 10. Applications and case studies: practical applications and case studies of computational modelling in various industries, such as pharmaceuticals, food processing, energy, and materials science. 11. Hands-on assignments and projects: students will complete hands-on assignments and projects throughout the course, applying the concepts and techniques learned to real-world problems and simulations. Literature: 1. S. Jayanti, Computational Fluid Dynamics for Engineers and Scientists, Springer, (2018). 2. C. R. Maliska, Fundamentals of Computational Fluid Dynamics - The Finite Volume Method, Springer International Publishing, (2023). 3. J. H. Ferziger, M. Perić, R. L. Street, Computational Methods for Fluid Dynamics, Springer International Publishing, (2019). 4. T. Pöschel, T. Schwager, Computational Granular Dynamics: Models and Algorithms, Springer-Verlag, (2005). 5. F. Radjai, F. Dubois, Discrete-Element Modeling of Granular Materials, Wiley, (2011). 6. D. Gelnar, J. Zegzulka, Discrete Element Method in the Design of Transport Systems Verification and Validation of 3D Models, Springer International Publishing, (2019). 7. W.-H. Zhou, Z.-Y. Yin, Practice of Discrete Element Method in Soil-Structure Interface Modelling, Springer Nature Singapore, (2022). 8. X. Wang, B. Li, R. Xia, H. Ma, Engineering Applications of Discrete Element Method, Springer Nature Singapore, (2020).
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