Impact of Kinetic Uncertainties on Accurate Prediction of NO Concentrations in Premixed Alkane-Air Flames
Bibliographic record
Abstract
Accurate thermochemical mechanisms that can predict the formation of nitrogen oxides (NO x) are important design tools for low-emissions engines. The lack of accurate direct measurements of reaction rates and the associated measurement scatter have resulted in recommended rate parameters for individual chemical reactions that have large uncertainty intervals. In an effort to quantify the impact of these parametric uncertainties on emissions predictions, forward uncertainty propagation is performed with five spectral methods. Sparse grids are identified as the optimal technique to rapidly construct accurate surrogate models. Subsequent polynomial expansions with sparse grids, performed in one-dimensional atmospheric laminar flames for only the 30 uncertain reactions that greatly affect NO formation, produce uncertainty intervals two orders of magnitude larger than nominal predictions. Primary uncertainty sources were identified with reaction pathway analyses to evaluate the contribution of individual formation routes and the uncertainties in prompt NO were found to propagate mostly from the CH chemistry. These results highlight the necessity of a comprehensive approach, using experimental measurements with uncertainty quantification and inference techniques, to reduce uncertainty and develop predictive NO x models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".