Statistically accurate discrete phase modelling of particle cloud generation using Aggregate Steady Random Particle injection
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".