Reduced mechanisms for methanol oxidation under hydrothermal flames using a directed relation graph with sensitivity analysis and error propagation
Bibliographic record
Abstract
Abstract A revised detailed mechanism (RDM) including 173 species and 1011 reactions for methanol oxidation under hydrothermal flames is developed via modifying a methanol gas‐phase combustion mechanism with pressure and thermodynamic corrections and validated with experimental data. Different skeletal mechanisms are generated via the directed relation graph (DRG), the DRG with error propagation (DRGEP), the DRG with sensitivity analysis (DRGSA), and the DRGEP with sensitivity analysis methods. Although the skeletal mechanisms have different reaction paths, the evolutions of the main species in the RDM are generally reproduced. The most compact mechanism of 12 species and 35 reactions is obtained via the DRGSA method with an error of 7.59%. The temperatures of hydrothermal flames predicted from the skeletal mechanisms with different reduction methods agree well with those from the RDM within most operating ranges except the 12‐species mechanism under hypoxic conditions. The increase in the reduction degree increases the error of the ignition delay time, while the reduced mechanisms have better adaptability in predicting the ignition delay time at higher preheating temperatures. The increases of preheating temperature and methanol concentration decrease the error in the prediction of the laminar flame speed with the reduced mechanisms. Moreover, the mechanisms with different reduction degrees have little effect on the extinction temperature, with an error of 4–8°C.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".