Eulerian-Lagrangian CFD-microphysics modeling of Aircraft-Emitted Aerosol Formation at Ground Level
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-1823.vid Aviation-induced particulate matter has a direct impact on climate, atmospheric composition at flight altitudes, and on local air quality in the vicinity of airports. Meeting the environmental regulations is one of the main challenges that requires attention for air transportation development over the coming years. To increase the knowledge of secondary aerosol formation in aircraft plumes, advanced decision-making tools need to be developed. In this context, the present study aims at demonstrating the modelling capabilities of an innovative methodology coupling the flow dynamics in aircraft engine plumes with a detailed microphysical model. For this purpose, 2-D unsteady Reynolds-Averaged Navier-Stokes simulations of jet plume were carried out behind a realistic aircraft engine geometry at ground-level conditions. The CFD model was coupled with a tabulated chemistry (72 reactions and 35 species) and a detailed microphysical model that accounts for soot surface activation as well as condensation of gaseous precursors, i.e. organic vapors and sulfur species (H2SO4 and SO3), on activated-soot particles. The predictive capabilities of the proposed modelling strategy are assessed through the study of engine-plume gaseous and particulate emissions in comparison with available experimental and numerical data. . Further analysis of both volatile and non-volatile particle evolutions in the near-field plume was also performed at idle and take-off power settings. Future studies with the present model can help to better understand the effects of organic/soot emission levels on the evolution of near-field non-volatile and volatile PM emissions from aircraft engines at LTO operating conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".