Simulation of transient turbulent methane jet ignition and combustion under engine-relevant conditions using conditional source-term estimation with detailed chemistry
Bibliographic record
Abstract
The ignition and combustion processes of transient turbulent methane jets under high-pressure and moderate temperature conditions were simulated using a computationally efficient combustion model. Closure for the mean chemical source-terms was obtained with Conditional Source-term Estimation (CSE) using first conditional moment closure in conjunction with a detailed chemical kinetic mechanism, which was reduced to a Trajectory-Generated Low-Dimensional Manifold (TGLDM). The accuracy of the manifold was first validated against the direct integral method by comparing the predicted reactive scalar profiles in three methane–air reaction systems: a laminar premixed flame, a laminar flamelet and a perfectly stirred reactor. Detailed CFD simulations incorporating the CSE-TGLDM model were able to provide reasonably good predictions of the experimental ignition delay and initial ignition kernel locations of the methane jets reported in the literature with relatively low computational cost. Nitrogen oxides formed in the methane jet flame were found to be underpredicted by the model by as much as a factor of 2. The discrepancy may be attributable to the inability of the simulation to account for the effects of the rarefaction wave in the shock-tube experiments.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".