Statistically accurate discrete phase modelling of particle cloud generation using Aggregate Steady Random Particle injection
Mark J. Parker, Eric Savory, Anthony G. Straatman
Abstract
A novel approach called Aggregate STEady Random Particle (A-STERP) injection is introduced to characterize the injection of a random particle cloud into a continuous phase using existing discrete phase modelling (DPM). A-STERP takes advantage of the short computational time of steady DPM simulations and introduces temporal randomization by considering the aggregate, cumulative average of results obtained from sequential steady simulations using files of randomized injection points and particle sizes. A-STERP is shown by computational validation to converge to a steady value of, for example, total collection efficiency in a particle separation device, with increased numbers of randomized injection locations and numbers of injection files. A-STERP works within the framework of existing CFD software and was validated by computational modelling using ANSYS FLUENT of a generic collection chamber, a baffled pre-separator, and a cyclone for its ability to predict total collection efficiency and fractional collection efficiency of a defined distribution of particles. The results yielded by A-STERP were compared to those obtained from a randomized transient injection method and shown in all cases to be just as accurate, while requiring only a small fraction of the computational time – seconds/min compared to hours/days. The application of A-STERP is shown to provide accurate results for both stationary and time-periodic flows, and, by extension, to non-stationary flows. To this end, A-STERP makes it practical to conduct accurate DPM calculations of particle injection in large-scale simulations of complex devices, something that is not always practical using randomized transient DPM.