Systematic Assessment of Turbulence–Chemistry Interaction for Partially- Premixed Acetylene/Air Flames
Bibliographic record
Abstract
To validate a CFD modeling approach for the assessment of the combustion efficiency in partially premixed acetylene/air flames, this study integrates experimental and numerical methods to investigate flame structure, flow field characteristics, and heat transfer behaviour.Measurements of the global heat flux and stagnation pressure, alongside OH* chemiluminescence imaging, are employed to analyse the primary and secondary oxidation front of flames with high heat release rates in the primary oxidation.The experimental setup includes a water-cooled calorimeter with a capillary bore for static pressure extraction and a UV-sensitive camera with a bandpass filter to capture averaged OH* intensity.These results are compared to CFD simulations using the Flamelet-Generated-Manifold (FGM) and Reynolds-Stress-Model (RSM) frameworks.While the experimentally determined combustion efficiency is reproduced sufficiently for practical applications, the CFD model fails to predict the characteristic heat transfer maximum observed at a firing rate/Reynolds number specific torch-totarget distance.Conversely, the stagnation pressure is accurately captured across all flame configurations.The OH* imaging reveals flame quenching near the flame/wall interface at low burner-to-target distances, attributed to curvatureinduced strain and cold wall effects-phenomena not adequately represented in the current CFD model.These discrepancies significantly impact the heat transfer predictions, specifically for low torch-to-target distances with an impinged primary reaction front.Future work should incorporate diffusion flamelets rather than premixed flamelets to explore their sensitivity to strain and scalar dissipation rates to enhance the fidelity of heat transfer modelling in similar flame impingement systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".