Numerical Prediction of Gaseous Aerosol Precursors and Particles in an Aircraft Engine
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
Aviation-produced particulate matter has a direct impact on climate, atmospheric composition at flight altitudes, and local air quality in the vicinity of airports. The formation of soot and gaseous aerosol precursors inside the combustor and during gas expansion in turbine stages and nozzles must be addressed before the real impact of aircraft engines with respect to particulate matter emissions can be assessed. To design strategies to reduce particulate matter emissions, the development of a zero-/one-dimensional gas-turbine model is proposed, taking into account combustor and postcombustor flow operating over the landing/takeoff cycles with a detailed kerosene jet-A1 kinetics scheme, including a soot-dynamics model. This approach is very efficient computationally and may be clearly satisfying for parametric studies or in a predesign step. First, the model’s predictive capacity for capturing the main features of gas-turbine combustion as well as the expansion of combustion products in the turbine and nozzle has appeared acceptable as concentrations of International Civil Aviation Organization standard emissions and sulfur-species conversion agree reasonably well with measurements, whatever the operating conditions. In particular, the results showed that and concentrations still exhibited variations in the postcombustor zone until exiting the engine nozzle. Using a revised surface-growth mechanism combined with the condensation of six major polycyclic aromatic hydrocarbons has significantly improved predictions of computed particles diameters. Such values now agree very closely with experimental data collected over the landing/takeoff cycle, whereas the concentration of polycyclic aromatic hydrocarbons, as well as ethylene and benzene, were better predicted for the highest power setting (i.e., takeoff and climb configurations).
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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 | 0.000 | 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 it