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 firstname.lastname@example.org 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.
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.
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
No prior experience is required. Please bring a laptop to participate in the tutorials.
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.
I am happy to announce that I will be offering a new course for Intersession 2017 at Johns Hopkins University, entitled Introduction to Scientific Computing (EN.530.391.13). The interactive two-credit course designed to teach upper-level undergraduate students and new graduate students about the true capabilities of their computers–beyond email and internet–will take place from 10 through 26 January 2017.
As with all Intersession courses, Introduction to Scientific Computing will be offered free of charge to students enrolled at Johns Hopkins University for the fall 2015 semester.
Registration for Intersession 2017 opens 6 December 2016 at 07:00. Have a question about the course? Submit a comment below or contact me.
The most important tool in science and engineering, computers find applications in all aspects of academia and industry alike. Though expected to employ this tool effectively, few scientists or 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 course will introduce many computational tools, tricks, and tips that would otherwise require years of trial and error to learn.
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!
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.
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.
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