Toward Real-Time Aero-Icing Simulation of Complete Aircraft via FENSAP-ICE
Bibliographic record
Abstract
Three-dimensional fully viscous turbulent aero-icing flow simulation remains too computationally intensive when broad parametric studies are needed, such as during a certification process. In addition, the introduction of realistic icing effects for training pilots in simulators clearly lags behind in terms of taking advantage of computational fluid dynamics. To make such simulations more practical, this work presents a reduced-order modeling, based on the proper orthogonal decomposition method, that predicts a wide swath of approximate flowfields and ice shapes based on a limited number of obtained from high-fidelity computations. Modes are extracted from these snapshots and used to reconstruct the computational fluid dynamics field, and/or the aerodynamic coefficients, and/ or the ice shapes for other conditions within the range. This reduces calculation times by two to three orders of magnitude from the full three-dimensional ones, enabling a more complete map of the performance of an iced aircraft over a wide range of flight and weather conditions to be used in its certification and pilot training.
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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) | 0.000 | 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 | 0.001 |
| 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".