Numerical investigation of engine position effects on contrail formation and evolution in the near-field of a realistic aircraft configuration
Bibliographic record
Abstract
The present study investigates the impact of engine position on contrail formation and near-field evolution in a realistic three-dimensional aircraft configuration. Detailed numerical simulations are conducted using a Reynolds-Averaged Navier-Stokes (RANS) approach coupled with mesh adaptation techniques. A Eulerian microphysical model is used to characterize contrail ice crystal properties and their evolution under varying dilution conditions. The setup is based on a Boeing 777-like geometry, including fuselage, wings, engines, and tailplane. Two microphysical activation scenarios are considered: one incorporating adsorption-based ice nucleation and the other assuming fully activated soot particles. The latter for two soot number emission indices. The dilution process and wake structure exhibit a strong dependence on engine placement, which significantly influences plume saturation. In highly diluted configurations, enhanced early-stage mixing reduces plume temperature and increases relative humidity, favoring the growth of larger ice crystals. Depending on the soot number concentration, vapor depletion effects may outweigh dilution-driven changes in water vapor availability. In adsorption-limited activation scenarios, increased dilution reduces the concentration of sulfur species, leading to a lower activation fraction and the formation of smaller ice crystals. Additionally, across the scenarios, the modified jet-vortex interaction alters particle distribution and their access to water vapor, further shaping their growth. These effects ultimately impact the contrail's optical properties, particularly its optical thickness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".