Power and Design Optimization of Electro-Thermal Anti-Icing Systems via FENSAP-ICE
Bibliographic record
Abstract
A general methodology for the power and design optimization of electro-thermal in-flight anti-icing systems is presented. The optimization goal is 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 (POD), in conjunction with Kriging. The numerical results obtained for some optimization cases show the benefits of the methodology in substantially reducing the cost of high-fidelity optimization of electrothermal anti-icing systems, in particular, and other types of thermal-based ice protection systems, in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".