On the experimental validation of combustion simulations in turbulent non-premixed jets
Bibliographic record
Abstract
A Reynolds averaged Navier–Stokes (RANS) based combustion model, which incorporated the conditional source-term estimation (CSE) method for the closure of the chemical source term and the trajectory generated low-dimensional manifold (TGLDM) method for the reduction of detailed chemistry, was applied to predict the OH radical distribution in a combusting non-premixed methane jet. The results of the numerical prediction were compared with the results of a complementary experimental study in which the OH radical fields of combusting non-premixed methane jets were visualized using planar laser induced fluorescence (PLIF). It is well known within the modelling community that RANS based models are unable to capture the stochastic nature of turbulent combustion and autoignition, and are therefore unable to predict individual realizations of the flame. In this study, the agreement between the predicted OH field and a well-converged ensemble average of the experimental results was also shown to be poor. The lack of agreement between the numerical results and the ensemble averaged experimental results expose the potential significance of the known weakness in the RANS method. A statistical analysis of the experimental results was also performed. The results of the analysis showed that a minimum of 100 individual realizations was required to provide a well-converged average OH field for the combusting non-premixed jet under investigation. The significance of this result with respect to the validation of large-eddy simulations (LES) of combusting jets is 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".