Efficient 3D Artificial Ice Shapes Simulations with 2D Ice Accretion Codes using a 3-Level Correction
Bibliographic record
Abstract
3D ice accretion codes have been available for a few decades but, depending on the specific application, their use may be cumbersome, time consuming and requiring a great deal of expertise in using the code. In particular, simulations of large 3D glaze ice accretions using multiple layers of ice is a very challenging and time consuming task. There are several reasons why 2D icing simulations tools are still widely used in the aircraft industry to produce realistic glaze ice shapes. 2D codes are very fast and robust, with a very short turn-around time. They produce adequate results in areas of the aircraft where 3D effects on airflow or droplets concentration can be neglected. Their use can be extended to other areas of the aircraft if relevant 3D effects can be taken into account. This paper proposes a simulation methodology that includes three levels of corrections to extend the use of 2D icing codes to most of the aircraft surfaces. The first correction addresses the well-known methodology to match the sectional angle of attack and with a novel approach for horizontal stabilizers. The second correction accounts for the impact of the leading edge sweep into the water collection efficiency. The methodology proposed by Dorsch and Brun in a normal plane to the leading edge, which is part of the SAE Aerospace Recommended Practice ARP5903 report, is reviewed in detail. Also, a new formulation in the streamwise plane is proposed and compared to Dorsch methodology. Finally an innovative methodology to account for 3D upstream aircraft components effects on local water distribution is presented. Examples of 3D ice shapes simulated with the proposed methodology are presented.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".