NOx Emissions Modeling and Uncertainty for Exhaust-Gas-Diluted Flames
Bibliographic record
Abstract
Oxides of nitrogen (NOx) are pollutants emitted by combustion processes during power generation and transportation that are subject to increasingly-stringent regulations due to their impact on human health and the environment. One NOx reduction technology being investigated for gas-turbine engines is exhaust gas recirculation (EGR), either through external exhaust-gas recycling or staged combustion. In this study, the effects of different percentages of EGR on NOx production will be investigated for methane-air and propane-air flames at a selected adiabatic flame temperature of 1800K. The variability and uncertainty of the results obtained by the Gri-Mech 3.0, San-Diego 2005, and the CSE thermochemical mechanisms are assessed. It was found that that key parameters associated with postflame NO emissions can vary up to 192% for peak CH values, 35% for thermal NO production rate, and 81% for flame speed, depending on the mechanism used for the simulation. A linear uncertainty analysis, including both kinetic and thermodynamic parameters, demonstrates that simulated post-flame nitric oxide levels have uncertainties on the order of ±50–60%. The high variability of model predictions, and their relatively-high associated uncertainties, motivates future experiments of NOx formation in exhaust-gas-diluted flames under engine-relevant conditions to improve and validate combustion and NOx design tools.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".