Eulerian–Lagrangian CFD-microphysics modeling of aircraft-emitted aerosol formation at ground-level
Bibliographic record
Abstract
Aviation-induced particulate matter directly affects the climate, the atmospheric composition at flight altitudes, and local air quality near airports. Meeting environmental regulations is a key challenge for the future development of air transportation. To enhance the understanding of secondary aerosol formation in aircraft plumes, an innovative methodology combining flow dynamics in aircraft engine plumes with a particle-based microphysical model is proposed. To this end, 2D axisymmetric unsteady Reynolds-Averaged Navier–Stokes simulations were conducted behind a realistic aircraft engine geometry. The CFD model was coupled with a tabulated chemistry and a detailed microphysical model accounting for soot surface activation, condensation of organic vapors and sulfur species (H2SO4, SO3), as well as scavenging of sulfuric acid-water droplets on soot surfaces. The model’s predictive capacity was validated against experimental data from APEX 1–2, encompassing plume aerothermodynamics properties and the evolution of gaseous species from low-idle (4%) to take-off (100%) power settings of the CFM56-2C1 aircraft engine. The predicted size distributions of total and nonvolatile particles matched reasonably well with measurements from APEX-1 within the near field (≤30 m). The model reveals the engine power dependency of soot and the chemical composition of volatile particles, predominantly influenced by organic compounds downstream of the engine. Adsorption of gaseous species of organic compounds and sulfuric acid were identified as the most dominant mechanism for soot particle coatings in the near field, regardless of operating conditions.Copyright © 2024 American Association for Aerosol Research
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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) | 0.001 | 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) | 0.001 | 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".