FENSAP-ICE: A CFD Monte Carlo Approach to Shed-Ice Trajectory and Impact
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">A fully CFD-based methodology for ice particle tracking based on a Monte Carlo statistical approach and a six-degrees-of-freedom particle-tracking module has been developed within the FENSAP-ICE in-flight icing system. A one-way aerodynamic coupling between the airflow and the ice particle has been adopted, such that the flowfield determines the forces and moments on the particle at each location on its track, but the particle, being much smaller, has no aerodynamic effect on the aircraft's flowfield. A complete envelope of force and moment coefficients has been computed for a representative ice shape, in order to generate a permanent database. At each time step during the integration of the particle track, the angles of the local flow velocity vector with the principal axes of the particle are determined and used to interpolate the corresponding force and moment coefficients from the particle's database. These 6-DOFs are then used to compute the next particle location. The linear displacement is obtained from the integration of Newton's Second Law, whereas angular displacement is obtained from the numerical integration of Euler's equations of motion using quaternions and the 4-stage Runge-Kutta algorithm. A comparison of the aerodynamic force and moment envelope for one ice shape with experimental data will be presented to demonstrate the reliability of this approach. Shed ice particle trajectory computation is demonstrated for a complete aircraft geometry.</div></div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".