A lagrangean interactive interface to evaluate ice accretion modeling on a cylinder - a test case for icing modeling on wind turbine airfoils
Bibliographic record
Abstract
In the context of global campaign to mitigate climate change effects, researches on wind turbines industry have known a constant growth during the last few years. Wind velocity is so important for wind turbine productivity and very significant wind resources are available in cold regions where icing effects are also significant. Ice accretion increases the risks of unbalanced mass resulting in losses of annual power production, in vibration problems and in security risks. This coincides with the most abundant days of wind in the year. At the WERL (Wind Energy Research Laboratory), in collaboration with the TechnoCentre Eolien and the AMIL (Anti-icing Materials International Laboratory), numerous studies have been conducted to avoid this problem. In this paper we made use of these studies to simulate a test case of icing around a cylinder as described in Lozowski et al. [1]. We emphasize on the fact that these studies cannot provide very accurate local results due to numerous simplifications. We, therefore, developed at the WERL an interactive interface to simulate the trajectory of water droplets in an airstream until collision on a cylinder. This interface is based on MS-Excel worksheets supported with VBA code (Visual Basic for Applications) using a fourth order Runge-Kutta resolution scheme. The interface provides flexibility to demonstrate various scenarios that can help to validate the subsequent evaluation of collection efficiency based on multiphase CFX simulation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".