Successful Ph.D. defense

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My doctoral advisor, Andrea Prosperetti, and I celebrate my successful Ph.D. defense.

On Thursday afternoon, I became the newest Ph.D. in the Mechanical Engineering Department at the Johns Hopkins University after I successfully defended my dissertation, which was entitled Numerical simulation of disperse particle flows on a graphics processing unit. My defense presentation was tremendously well attended by faculty, coworkers, family and friends.

I enjoyed celebrating my successful defense with family and friends in the afternoon and evening. Thank you to everyone who attended my presentation for their wonderful support!

Announcement: Dissertation defense

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.


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.

Announcing a new course for JHU’s Intersession 2016: Applications in Scientific Computing

I am happy to announce that I will be offering a new course for Intersession 2016 at Johns Hopkins University, entitled Applications in Scientific Computing (EN.530.390.13). The interactive two-credit course designed as an introduction to scientific computing for upper-level undergraduate students will take place from 4 through 22 January 2016. New graduate students are also encouraged to attend.

As will all Intersession courses, Applications in Scientific Computing will be offered free of charge to students enrolled at Johns Hopkins University for the fall 2015 semester. All reference textbooks used for the course will be freely available online.

Registration for Intersession 2016 opens 1 December. For more information, submit a comment below or contact me.

Course description

Scientific discovery and computing capability have progressed inseparably for more than the last century, but few theoretically-focused courses find time to discuss this important connection. Guided by various examples borrowed from physics and engineering courses, we will interactively explore methods of solving problems numerically using contemporary computational tools. Example problems will draw from the following fields: dynamical systems, continuum mechanics, molecular dynamics, and robotics.

Prerequisites: calculus, differential equations, linear algebra

Schedule: 13:00-16:00 on Tuesday, Wednesday, and Friday from 4 through 22 January 2016

Relevant topics

  • Computer hardware
  • Data structures
  • Sources of error
  • Sorting and selection
  • Numerical discretization
  • Interpolation and extrapolation
  • Random number generation
  • Solution of linear systems
  • Eigensystems
  • Integration of functions
  • Initial- and boundary-value problems