On the Prediction of Pollutant Emission Indices From Gas Turbine Combustion Chambers
Bibliographic record
Abstract
This paper surveys existing emissions models used in the prediction of NOx. The prediction of jet engine emission indices from fundamental principles have proven to be difficult due to the complex physical and chemical interactions occurring within their combustion chambers. Present day prediction of engine emission indices during engine development relies on published models, which are based on limited sets of data measured on older combustion chambers where minimizing pollutant emissions was not a major design criteria. Such empirical and semi-empirical models can, however, provide upper emission limits for new engine designs. A database comprising a wider range of experimental data (over 2000 measured points) taken from the literature was used to test the models. Advanced techniques were applied to optimize the coefficients of proportionality of governing equations of the best models in the literature. Most models tend to consistently over or under predict the measured values. In most cases, even though the standard deviation of the predicted values was not reduced, the correlation error was improved by removing this bias.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".