A scalable parallel algorithm for the direct numerical simulation of three-dimensional incompressible particulate flow
Why this work is in the frame
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Bibliographic record
Abstract
Particulate flow is of great importance from both the scientific and engineering points of view. Owing to the complexity of particle-flow interactions, direct numerical simulations (DNS) of inertial particulate flow with finite-size particles have been limited to a very small number of particles, while the industrial applications involve larger numbers with many orders of magnitude. This article presents a parallel implementation of a fictitious domain method for the DNS of particulate flows. The method is thoroughly tested and its parallel performance on distributed memory clusters is evaluated on a large-scale problem. Finally, we present the results for the separation of 21,336 particles of two different densities in a viscous fluid. Although there is still a significant gap between DNS and the industrial applications, the present algorithm allows to simulate significantly large number of particles so that a meaningful statistical analysis can be performed. This will help in the development of new closure relations for the averaged models of multiphase flows.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it