FENSAP-ICE in Aid of Certification: From CFD to Flight Testing
Bibliographic record
Abstract
CFD-Icing (CFD-I) is a powerful companion to CFD-Aero (CFD-A) in the design and certification of new aircraft, rotorcraft and jet engines. It can drastically reduce the number of tunnel and flight tests, and their associated costs, by simulating on computers the full Appendix C and beyond such as is proposed in new Appendices D and O. It can also predict performance and moment coefficients in roll, pitch and yaw. These predictions can then be used in original certification or supplemental certifications to the type design, allowing mitigating potential hazards of flight-testing. This work presents an example of the application of FENSAP-ICE to predict 45 minutes of ice accretion on a RC-26B aircraft fuselage retrofitted by the addition of a FLIR sensor and a SATCOM antenna. The predicted aerodynamic penalties are compared with recorded flight test data obtained with simulated ice shapes. Of further interest is that two similar FLIR installations were tested with post-processed data to account for minor differences in the vertical dimension of the FLIR sensor installation. This validation campaign shows the ability of such second-generation CFD-I tools to properly model the impact of ice accretion, starting from clean aerodynamics, to water impingement, to ice accretion, to aerodynamic characteristics of contaminated aircraft.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".