Analysis of Electro-Thermal De-Icing on a NACA0012 Airfoil Under Harsh SLD Conditions and Different Angles of Attack
Bibliographic record
Abstract
Ice accretion (icing) on aircraft surfaces is a significant safety risk through airfoil shape modification and reduction of aerodynamic efficiency. This process occurs when an aircraft flies through clouds of supercooled water droplets that freeze upon impact on exposed surfaces. To counter this hazard, electro-thermal de-icing systems integrate heaters in critical regions to melt ice and reduce performance losses. In this study, a multiphysics computational model is used to simulate ice accretion and electro-thermal de-icing on a NACA-0012 airfoil, accounting for factors such as airflow, droplet impingement, phase changes, and heat conduction. The model's predictions are validated against experimental data, confirming its accuracy. A cyclic electro-thermal ice protection system (ETIPS) is then tested under both standard and severe supercooled large droplet (SLD) conditions, examining how droplet size and angle of attack affect de-icing performance. Simulations without an active de-icing system show severe aerodynamic degradation, including an 11.1% loss of lift and a 48.2% increase in drag at a 12°∘ angle of attack. For large droplets (median 200 μm ), the drag coefficient increases by 36.5%. Under harsh icing conditions, the effectiveness of the de-icing system is found to depend on droplet size, angle of attack, and heater placement. Even with continuous heater operation, ice continues to accumulate on the leading edge at higher angles of attack. While the ETIPS performs effectively against large droplets in its protected zones, unheated regions experience significant ice buildup (especially with 200 μm droplets), indicating that additional or extended heaters may be necessary to ensure complete protection in extreme conditions.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".