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.
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.
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