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.
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.
Today is the big release day for Crædl, the Collaborative Research Administration Environment and Data Library. It is a tool designed to work alongside researchers in their labs to simplify data management. It assists researchers in sharing work with collaborators and discovering work made available within the larger research community.
The first Crædl (hemi.craedl.org) belongs to the Hopkins Extreme Materials Institute (HEMI), which funded its development. It was inspired by the difficulties HEMI researchers were having with collaboration and data sharing, especially the sharing of large (think terabytes) files. Crædl provides a framework for managing data that automates the process of attaching metadata (the information describing the data), which is essential for enabling data search and discovery. Finally, Crædl helps research groups manage their projects and their researchers (HEMI is a rather large institute) to improve research outcomes.
If you’re interested in learning more about Crædl and its features, head over to craedl.org. If you’re interested in trying out Crædl in your own research group, send an email to email@example.com describing your group. We’re not currently open to new research groups, but we are working towards expanding our reach in the future.
I’ve personally lead the Crædl project from its inception about a year ago, and I’m excited to release it into the wild. I know it currently provides some valuable tools to researchers, but it’s still early days. I have many big ideas for features yet to come, so keep an eye out for more updates.
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.
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.
We present work on a new implementation of the Physalis method for resolved-particle disperse two-phase flow simulations. We discuss specifically our GPU-centric programming model that avoids all device-host data communication during the simulation. Summarizing the details underlying the implementation of the Physalis method, we illustrate the application of two GPU-centric parallelization paradigms and record insights on how to best leverage the GPU’s prioritization of bandwidth over latency. We perform a comparison of the computational efficiency between the current GPU-centric implementation and a legacy serial-CPU-optimized code and conclude that the GPU hardware accounts for run time improvements up to a factor of 60 by carefully normalizing the run times of both codes.
I 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.
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!
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.
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.