Predicting the ignition delay of turbulent methane jets using Conditional Source-term Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A predictive simulation of the autoignition process of non-premixed methane in a turbulent jet configuration was performed. Closure for the chemical source-term was obtained using Conditional Source-term Estimation with Laminar Flamelet Decomposition (CSE-LFD). The ambient oxidizer conditions – the high pressure and moderate temperatures characteristic of compression ignition engines – were chosen with the intent to validate the combustion model used under engine-relevant conditions. Validation was obtained by comparison of the predicted ignition delay to experimental results obtained from a shock-tube facility at several initial temperatures. Overall, the combination of full chemistry that has been carefully tuned to predict autoignition of premixed methane–air mixtures under similar temperature/pressure conditions with the CSE-LFD model is able to successfully predict the autoignition delay time of methane–air jets well within the scatter in the experimental data.
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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 | 0.001 | 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 it