Large Eddy simulation for lean premixed combustion
Bibliographic record
Abstract
Abstract In comparison to previous numerical studies interested in the ORACLES benchmark (One Rig for Accurate Comparisons with Large Eddy Simulations), the present study demonstrates the advantages of LES‐WALE model in both inert and reacting flows using the Fluent‐CFD. So, the confirmation is based on the experimental research effort that was involved in the European Union‐funded research program MOLECULES (Modelling of Low Emissions Combustors Using Large Eddy Simulations), for three parameters: longitudinal velocity, longitudinal velocity fluctuation, and length of recirculation zone. In line with what was observed by the experimental reference study, the dynamic model (LES‐WALE) predicts, respectively, as well as the asymmetry and the symmetry, for both inert and reacting flows. In addition, the simulation succeeds to predict the zones of recirculation and shows the differences between the two cases, inert and reacting flows. Moreover, results have been compared with those of the k–ε model performed by Kurenkov and Obserlack [Kurenkov and Obserlack, Flow Turbulence Combustion 74, 387–407 (2005)] study. © 2012 Canadian Society for Chemical Engineering
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".