Evaluating Bypass Effects of Advanced Turbofan Engines on Contrail Formation Using Large Eddy Simulations
Bibliographic record
Abstract
Condensation trails formed behind cruising aircraft engines contribute to climate change through radiative forcing. While most contrail studies consider parameterized single-jet profiles, this CFD study investigates dual-stream jets to characterize turbofan bypass effects on contrail microphysics. High-resolution large-eddy simulations refined early plume dynamics for a dual-stream jet, consisting of a bypass jet and a core jet of air and water vapor (Eulerian phase) laden with soot particles (Lagrangian phase). Microphysical modeling employs a solubility model for soot activation, while the subsequent water vapor condensation processes on activated soot particles and ice growth on freezing particles are computed using a deposition model. The first comparison of a turbofan with its equivalent turbojet showed that the bypass jet enhances the mixing and cooling of the core jet, with a mean particle radius increase of up to 30%. Thereafter, three realistic turbofans—CFM56-5B3/3, LEAP-1A/33, and UHBR—classified, respectively, as medium-bypass-ratio, high-bypass-ratio, and ultrahigh-bypass-ratio engines, were compared at ambient temperatures of 215, 220, and 225 K. The analysis showed that higher bypass ratios enhance jet mixing in the near field, increasing soot activation and droplet freezing processes and resulting in initial contrail properties that are less sensitive to ambient temperatures.
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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) | 0.001 | 0.000 |
| 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".