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Goyal, Aashish

University of British Columbia

Flows laden with rigid bodies are ubiquitous in both industrial and natural environments. A comprehensive knowledge of particle-laden flows facilitates the effective functioning of numerous industrial processes. However, in order to accurately resolve flows laden with 𝛰(1000) particles in a computational model, 𝛰(10⁹) spatial variables are required. Numerical computing of these variables on parallel platforms over 𝛰(1000) cores is challenging. We begin this dissertation by proposing a scalable Direction-Splitting solver for flows containing non-spherical moving and fixed rigid bodies. We enhance the discretizations of advection and divergence terms in order to correctly capture the sharp changes in flow that occur in the vicinity of a fluid-solid interface. We rigorously verify the effectiveness of the numerical scheme through a comprehensive comparison of our outcomes with numerous benchmark simulations documented in the literature. We demonstrate the accuracy, speed, and scalability up to 6,400 cores. By expanding the functionality of our solver to incorporate intricate three-dimensional geometries defined by a collection of triangles in Standard Triangle Language (STL) files, we enhance the solver suitability for geometries that depict industrial processes. We also demonstrate that using an STL file does not affect the spatial accuracy of the computed flow field. Following this, we analyze the impact of a wall on the pairwise interaction of two spheres using our solver. Our calculations are verified against data from the literature in order to establish the accuracy of the simulations. We propose a Fourier Predictive Model to represent the modulations in the streamwise and spanwise components of force using the data produced by our fast and accurate solver. The model exhibits exceptional performance, as evidenced by the coefficient of determination of ~ 0.99. Next, we examine the effect of particle shape on the hydrodynamic forces and torques in a random array of Platonic polyhedrons. In order to illustrate the effect of particle sphericity, we depict the variation of forces and torques by comparing our outcomes to a random array of spheres. The effect of sphericity on a random array is illustrated through the use of sophisticated models, including a probability-driven model and physics-inspired neural networks.

Text

2024-01-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87214

Chemical and Biological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/