Announcement: Dissertation defense

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A simulation by Bluebottle. Figure Copyright (c) 2016 Adam Sierakowski

On Thursday 10 March, I will defend my PhD dissertation entitled Numerical simulation of disperse particle flows on a graphics processing unit. I will present my work in a seminar open to the public in 228 Malone Hall on the Johns Hopkins University Homewood campus at 10:30 am.

Abstract:

In both nature and technology, we commonly encounter solid particles being carried within fluid flows, from dust storms to sediment erosion and from food processing to energy generation. The motion of uncountably many particles in highly dynamic flow environments characterizes the tremendous complexity of such phenomena. While methods exist for the full-scale numerical simulation of such systems, current computational capabilities require the simplification of the numerical task with significant approximation using closure models widely recognized as insufficient. There is therefore a fundamental need for the investigation of the underlying physical processes governing these disperse particle flows.

In the present work, we develop a new tool based on the Physalis method for the first-principles numerical simulation of thousands of particles (a small fraction of an entire disperse particle flow system) in order to assist in the search for new reduced-order closure models. We discuss numerous enhancements to the efficiency and stability of the Physalis method, which introduces the influence of spherical particles to a fixed-grid incompressible Navier-Stokes flow solver using a local analytic solution to the flow equations.

Our first-principles investigation demands the modeling of unresolved length and time scales associated with particle collisions. We introduce a collision model alongside Physalis, incorporating lubrication effects and proposing a new nonlinearly damped Hertzian contact model. By reproducing experimental studies from the literature, we document extensive validation of the methods.

We discuss the implementation of Physalis for massively parallel computation using a graphics processing unit (GPU). We combine Eulerian grid-based algorithms with Lagrangian particle-based algorithms to achieve computational throughput up to 90 times faster than the legacy implementation of Physalis for a single central processing unit. By avoiding all data communication between the GPU and the host system during the simulation, we utilize with great efficacy the GPU hardware with which many high performance computing systems are currently equipped. We conclude by looking forward to the future of Physalis with multi-GPU parallelization in order to perform resolved disperse flow simulations of more than 100,000 particles and further advance the development of reduced-order closure models.

New publication in the Journal of Computational Physics

I’m excited to announce the publication of my new paper, Resolved-particle simulation by the Physalis method: Enhancements and new capabilities, in the Journal of Computational Physics. The paper summarizes the theory and numerical methods that I, along with my doctoral advisor Andrea Prosperetti, have refined and developed for the simulation of particles in a fluid flow (think sand kicked up by waves on a beach).

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A simulation of 2048 particles falling through a duct. The colors represent velocity magnitude, where blue is slow and red is fast. Image Copyright (c) 2016 Adam Sierakowski

The computer code, freely available for download at PhysalisCFD.org, is the first tool that performs such simulations using a graphics processing unit (GPU) as the primary computing engine. By using a GPU, simulations run up to 90 times faster than before, allowing us to simulate thousands of particles in the same amount of time it used to take to simulate ten.

Abstract:

We present enhancements and new capabilities of the Physalis method for simulating disperse multiphase flows using particle-resolved simulation. The current work enhances the previous method by incorporating a new type of pressure-Poisson solver that couples with a new Physalis particle pressure boundary condition scheme and a new particle interior treatment to significantly improve overall numerical efficiency. Further, we implement a more efficient method of calculating the Physalis scalar products and incorporate short-range particle interaction models. We provide validation and benchmarking for the Physalis method against experiments of a sedimenting particle and of normal wall collisions. We conclude with an illustrative simulation of 2048 particles sedimenting in a duct. In the appendix, we present a complete and self-consistent description of the analytical development and numerical methods.

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