Evaluation of implicit LES modeling of separated flows in abackward-facing step
Bibliographic record
Abstract
This paper assesses the implicit Large Eddy Simulation (iLES) technique to model a separated flow over a backwardfacing step (BFS), in comparison with results achieved by Direct Numerical Simulation (DNS).The iLES technique, implemented in the open-source code "Incompact3d", relies on introducing an artificial viscosity in the discretization of the viscous term to control spurious oscillations.To the best of the author's knowledge, this method has not been tested on wall-bounded separated flows where modeling wall regions with classical LES techniques is challenging.An internal flow over a BFS is simulated at Reynolds number Re = 5000 and expansion ratio Er = 2. Two inflow conditions are considered upstream of the expansion: a laminar Poiseuille flow and a turbulent inflow.The underestimation of the reattachment location of the primary geometry-induced separation bubble leads to the spatial shift of the secondary pressure-induced separation bubble formed on the top wall in the laminar BFS.Despite this underestimation in iLES, an excellent agreement has been obtained on the mean flow properties and Reynolds stress budgets with 12 times fewer grid points than DNS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".