Optimization via FENSAP-ICE of Aircraft Hot-Air Anti-Icing Systems
Bibliographic record
Abstract
This paper presents a methodology for the optimization of hot-bleed-air anti-icing systems, known as Piccolo tubes. Such systems are widely used to anti-ice the wings of many commercial aircrafts, ranging from regional to wide-body jet aircrafts. Having identified the most critical in-flight icing conditions, as well as any anti-icing system constraints as inputs, the ideal aim is to achieve fully-evaporative conditions over the heated surfaces. To do so, an optimization method based on three-dimensional computational fluid dynamics, reduced-order models, and genetic algorithms was constructed to determine the optimal geometric configuration of the Piccolo tube (jet angles, spacing of jets, and distance from leading edge). The external and internal airflows are computed using the finite element Navier–Stokes applications package (FENSAP-ICE). The methodology leads to significantly-improved configurations for threeto five-dimensional design spaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".