Conditional Moment Closure (CMC) applied to autoignition of high pressure methane jets in a shock tube
Bibliographic record
Abstract
Autoignition of non-premixed methane–air mixtures is investigated using first-order Conditional Moment Closure (CMC). Turbulent velocity and mixing fields simulations are decoupled from the CMC calculations due to low temperature changes until ignition occurs. The CMC equations are cross-stream averaged and finite differences are applied to discretize the equations. A three-step fractional method is implemented to treat separately the stiff chemical source term. Two detailed chemical kinetics mechanisms are tested as well as two mixing models. The present results show good agreement with published experimental measurements for the magnitude of both ignition delay and kernel location. The slope of the predicted ignition delay is overpredicted and possible sources of discrepancy are identified. Both scalar dissipation rate models produce comparable results due to the turbulent flow homogeneity assumption. Further, ignition always occurs at low scalar dissipation rates, much lower than the flamelet critical value of ignition. Ignition is found to take place in lean mixtures for a value of mixture fraction around 0.02. The conditional species concentrations are in qualitative agreement with previous research. Homogeneous and inhomogeneous CMC calculations are also performed in order to investigate the role of physical transport in the present autoignition study. It is found that spatial transport is small at ignition time. Predicted ignition delays are shown to be sensitive to the chemical kinetics. Reasonable agreement with previous simulations is found. Improved formulations for the mixing model based on non-homogeneous turbulence are expected to have an impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".