Surrogate-Based Optimization of Electrothermal Wing Anti-Icing Systems
Why this work is in the frame
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Bibliographic record
Abstract
The optimization of electrothermal in-flight anti-icing systems is presented by introducing a general methodology. The optimization goal was to achieve an ice-free area over the protected zone by using the lowest energy possible. The power and/or length of the electric pads are considered as design variables. The optimization procedure is performed via a derivative-free method that typically needs many objective-function evaluations. This would be impractical as aero-icing flow simulation remains computationally intensive when coupled with conjugate heat-transfer calculations, as in the case of ice-protection systems. The cost is even more prohibitive for an optimization process, as a large number of simulations are needed. To make it practical, this work presents a surrogate-based optimization approach using proper orthogonal decomposition, in conjunction with kriging. The results obtained show that the methodology is efficient and reliable in optimizing electrothermal ice-protection systems in particular, and a thermal-based one in general.
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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 | 0.000 |
| 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 it