New publication in the journal Physical Review Fluids

Work I’ve recently done with my colleagues has just been published in the journal Physical Review Fluids. The paper, Continuity waves in resolved-particle simulations of fluidized beds, analyzes a series of computational simulations performed by software I developed that look a lot like this, only with more particles:

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1,000 fluidized particles.

Though not at all obvious from this video, these particles are actually bumping into each other and sending waves of high particle density up the column even though the mean particle velocity is zero. This behavior had not previously been investigated in three-dimensional columns of fluid. We found many interesting details about the way these particles move around, including that a theory developed for one-dimensional motion still does a good job of predicting the speed of the high density waves in a three-dimensional setting.

Figure 10
Our 3D results (symbols) support existing 1D theory (lines) predicting the relationship between particle volume fraction (φ) and continuity wave speed (c).

Abstract:

The results of a fully resolved simulation of up to 2000 spheres suspended in a vertical liquid stream are analyzed by a method based on a truncated Fourier series expansion. It is shown that, in this way, it is possible to identify continuity (or kinematic) waves and to determine their velocity, which is found to closely agree with the theory of one-dimensional continuity waves based on the Richardson-Zaki drag correlation.

Click to download (PDF)

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New publication in the Journal of Computational Physics

I’m excited to announce the publication of work with my colleagues in a new paper, Fully resolved simulation of particulate flows with particles-fluid heat transfer, in the Journal of Computational Physics. This paper describes an extension of my previous work, adding the ability to account for heat transfer between particles and the surrounding fluid.

Fig. 10
Time-dependence of the temperature of a particle immersed in a warmer uniform flow with Re = 50. The solid lines are the present results and the dashed lines the results of Balachandar, S., Ha, M. Y., 2001.

This is an especially important new capability because particle flows are so frequently used in industrial chemical processing applications where temperature must be closely controlled. Whether heat is being added to catalyze a chemical reaction or is a result of the chemical reaction itself, our new method is able to simulate this phenomenon accurately and efficiently.

Implemented to run on GPUs, our method can simulate thousands of particles, providing a new window though which we can work to improve our understanding of the behavior of particle flows. By learning more about particle flows, we can make existing chemical processing technologies faster, safer, and less expensive.

Abstract:

The Physalis method for the fully resolved simulation of particulate flows is extended to include heat transfer between the particles and the fluid. The particles are treated in the lumped capacitance approximation. The simulation of several steady and time-dependent situations for which exact solutions or exact balance relations are available illustrates the accuracy and reliability of the method. Some examples including natural convection in the Boussinesq approximation are also described.

Click to download (PDF)

Spring 2017 MARCC Tutorial Series

This spring I will be teaching a series of tutorials through the Maryland Advanced Research Computing Center (MARCC). Here are the details:

Spring 2017: Introduction to Scientific Computing

Computers, one of the most important tools in science and engineering, find applications in all aspects of academia and industry alike. Though expected to employ this tool effectively, few scientist and engineers have been trained to harness the power at their fingertips, and most could benefit significantly from a high-level exposure to scientific computing methodology. This tutorial series will introduce many computational tools, tricks, and tips that would otherwise take years of trial and error to learn.

Schedule

During the spring semester, we will offer the tutorial series at the Johns Hopkins University Homewood campus:

  • Mondays from 3:00 pm to 5:00 pm in Malone G33, beginning 30 January and ending 1 May
  • Tuesdays from 3:00 pm to 5:00 pm in Bloomberg 462, beginning 31 January and ending 2 May

During the summer, we will repeat the tutorial series at the Johns Hopkins University School of Medicine: details to be announced

Prerequisites

No prior experience is required. Please bring a laptop to participate in the tutorials.

Attendance policy

The tutorial series is designed to build on itself as it progresses and we strongly discourage skipping tutorials. For a smaller time commitment, consider attending one of our training workshops.

Topics

These are the topics that we plan to cover:

  • Week 1: Computer hardware basics (single-user, cluster)
  • Week 2: Connecting to a remote system
  • Week 3: Linux basics (navigation, Bash, Vim, file transfer)
  • Week 4: MARCC environment (lmod, Slurm)
  • Week 5: Code repositories (Git)
  • Week 6: C basics (compiling, linking, debugging, profiling)
  • Week 7: Installing packages/libraries
  • Week 8: Python basics
  • Week 9: Python data analysis
  • Week 10: Data visualization (ParaView)
  • Week 11: Parallelization (OpenMP, MPI, Cuda)
  • Week 12: Data management plans
  • Week 13: LaTeX basics

We reserve the right to modify this schedule as the tutorial series progresses.

Register

Click here to register for this free MARCC Training Series.

New appointment: Research Scientist

whiting.logo.small.vertical.blue-croppedI am excited to announce that I have accepted an appointment as an Assistant Research Scientist in the Department of Mechanical Engineering at the Johns Hopkins University. In this new role, I will work extensively within the Maryland Advanced Research Computing Center (MARCC; pronounced Marcy) to support the development and implementation of high-performance computing applications used for transformational research within the University and beyond. As a natural extension of my Ph.D. work, I look forward to developing new computational capabilities and to teaching users about this outstanding high-performance computing resource.

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Some of the hardware that comprises MARCC’s Bluecrab cluster, as viewed during a tour of the facility with my Fall 2015 course.
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An example of a particle-laden flow simulation performed using Bluebottle on MARCC.

Adam to speak at University of Florida

On Monday 14 March, I will present a seminar entitled “Numerical simulation of disperse particle flows on a graphics processing unit” at the Center for Compressible Multiphase Turbulence at the University of Florida.

ccmt

The seminar, presented at the Department of Mechanical & Aerospace Engineering, will take place at 15:00 in the Large Conference Room in the Particle Engineering Research Center.

Abstract

We will discuss the development and validation of a new open-source GPU-centric numerical tool for the resolved simulation of thousands of particles in a viscous flow in order to assist in the search for new closure models for reduced-order disperse particle flow simulation. The new tool, which achieves a throughput up to 90 times faster than its predecessors, implements the Physalis method to introduce the influence of spherical particles to a fixed-grid incompressible Navier-Stokes flow solver using a local analytic solution to the flow equations. We will consider some theoretical and numerical enhancements to the efficiency and stability of Physalis, and will visit two general classes of algorithms central to the effective utilization of a GPU for solving partial differential equations. To appropriately capture the unresolved particle interaction physics during collisions (i.e., lubrication and contact mechanics), we will discuss a new model that incorporates nonlinearly damped Hertzian contact. We will conclude by comparing simulation results to experimental data found in the literature and looking forward into the future of resolved particle simulation using heterogeneous high-performance computing systems.

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.

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