Large Eddy Simulation of Premixed Stratified Swirling Flame Using the Finite Rate Chemistry Approach
Bibliographic record
Abstract
Large eddy simulations of a stratified swirling flow of a Cambridge swirl burner for both nonreacting and reacting cases are conducted using a finite rate chemistry approach represented by a partially stirred reactor model. The large eddy simulation predictions are compared with experimental measurements for velocity, temperature, and concentrations of major species. The agreement is found in overall trend of velocity prediction, but temperature and concentration of major species show slight discrepancies in the central region. Two reduced chemical mechanisms are examined in the present paper with the objective of assessing their capabilities in predicting swirling flame characteristics, and the distinct difference using two mechanisms is found in CO distribution profiles, which is considered the consequence of different kinetics of CO-CO2 equilibrium. Flow structures are qualitatively and quantitatively analyzed with numerical results. Large-scale vortex structures and precession motions are observed in both nonreacting and reacting cases. Frequency of vortex shedding is identified from the point data of instantaneous velocity in the discharging stream-induced shear layer. On this basis, the intensity and frequency of precession motion are shown to be enhanced in the presence of combustion. Large-scale wrinkling of the flame surface is resolved and characterized in the flame zone, and the effect of mixture stratification is then further discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".