Contrail Formation Simulation via FANS-Based Turbulence Modelling Combined With Two-Equation Soot/Ice Particle Transport Modelling
Bibliographic record
Abstract
Depending on the fuel, aviation engines exhaust a range of hot gases, including carbon dioxide (CO2), water vapour (H2O), nitrogen oxide (NOx), sulphur oxide (SOx) as well as unburnt hydrocarbons, along with carbonaceous particle matter or soot. Furthermore, depending on the flight altitude and atmospheric conditions, the engine exhaust gases and particulate matter can lead to the formation of condensation trails or so-called contrails. It is now recognized that aviation induced contrail cirrus are a major source of all aviation driven climate forcing, contributing, by some estimates, to greater radiative forcing than even that of aviation emitted carbon dioxide. Accordingly, the present study will explore by numerical simulation the influences on contrail formation and, in particular, the interaction of the engine exhaust plume with the ambient air leading to contrail formation at typical cruise conditions in the near-field or jet regime in a controlled, systematic, and comprehensive fashion. For the latter, high-fidelity solutions of the Favre-averaged Navier-Stokes equations for turbulent flows of a compressible mixture of reactive gases and solid soot and ice aerosols are used to describe the engine exhaust (including soot), secondary bypass, and cold ambient air flows. In the proposed description, the solid particulates (both soot and ice) are taken to be in dynamical and thermodynamic equilibrium with the gaseous mixture. A previously developed Eulerian-based semi-empirical two-equation model, accounting for the prediction of soot particles growth and transport in non-premixed laminar and turbulent sooting flames, is then extended herein to treat the nucleation and transport of ice crystals associated with the contrails. A second-order-accurate finite-volume scheme and parallel anisotropic adaptive mesh refinement (AMR) algorithm is used to solve the governing equations on two-dimensional axisymmetric domains of multi-block, body-fitted, quadrilateral meshes, thereby affording computational accuracy of the solutions at a reasonable computational cost. The simulations considered here will also focus on flow conditions and geometry representative of a new experimental contrail facility or tunnel currently under development that will allow the study of contrail formation at flight temperatures, pressures, and humidity experienced by commercial aircraft at cruising altitudes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".