Spatial Simulation of Contrail Formation in Near-Field of Commercial Aircraft
Bibliographic record
Abstract
During this study, three-dimensional numerical simulations of contrails generated by commercial aircraft in cruise conditions were performed. The objective was to study the early development of contrails in the near field of an aircraft, including engine core and bypass flows. Computational fluid dynamics simulations, based on the three-dimensional Reynolds-averaged Navier–Stokes approach, were carried out on a realistic aircraft geometry. A microphysical model was implemented in the computational fluid dynamics code to simulate particle growth using an Eulerian approach. Results showed that the mixing processes in the bulk plume were in good agreement with the literature. Then, the early growth of a contrail in the near wake of the aircraft was investigated for two ambient relative-humidity conditions. This parameter played a significant role in contrail local properties. Increased relative humidity led to an increase in the fraction of particles under the supersaturated condition and condensation rate. This resulted in a higher mean particle radius and, as a consequence, in a higher optical depth, making contrails more distinct. Particle-size distribution shifted toward larger particles due to an increase in available vapor and got narrower as the relative humidity increased because more particles were in favorable conditions for ice growing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".