Bibliographic record
Abstract
Accurate temperature prediction is vital for the development of advanced combustion systems. The Reynolds analogy concept has been almost exclusively used in current turbulent reacting flow RANS simulations. In this paper, this hypothesis applied to a diffusion flame model combustor is discussed and assessed. Some of the numerical results obtained from a flamelet combustion model with the turbulence Prandtl/Schmidt number from 0.25 to 0.85 are presented, and compared with a benchmark experimental database. It is found that the turbulence Prandtl/Schmidt number has significant effect on the predicted temperature and species fields inside the combustor, as well as the temperature profile at the combustor wall. In contrast, its effect on the velocity field is insignificant in the range assessed. With the optimized turbulence Prandtl/Schmidt number, both velocity and scalar fields can be reasonably and quantitatively predicted. For the present configuration and operating conditions, the optimal Prandtl/Schmidt number is 0.5, lower than the commonly accepted values, ∼0.70. This study suggests that for accurate prediction of scalar transfers in turbulent reacting flows, the Reynolds analogy concept should be improved and new approaches should be developed.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".