In-Flight Icing of UAVs - The Influence of Reynolds Number on the Ice Accretion Process
Bibliographic record
Abstract
The intensive deployment of UAVs for surveillance and reconnaissance missions during the last couple of decades has revealed their vulnerability to icing conditions. At present, a common icing avoidance strategy is simply not to fly when icing is forecast. Consequently, UAV missions in cold seasons and cold regions can be delayed for days when icing conditions persist. While this approach limits substantially the failure of UAV missions as a result of icing, there is obviously a need to develop all-weather capabilities. A key step in accomplishing this objective is to understand better the influence of a smaller geometry and a lower speed on the ice accretion process, relative to the extensively researched area of in-flight icing for traditional aircraft configurations characterized by high Reynolds number. Our analysis of the influence of Reynolds number on the ice accretion process is performed for the NACA0012 airfoil. Analytical analysis of the integrated mass and energy balance equations along the airfoil surface allows the identification of regimes of rime and glaze formation, as well as the ice accretion extent as a function of static air temperature and liquid water content. For each Reynolds number, a CFD solver computes the airflow field, and the distributions of Stanton number and static air pressure along the airfoil surface. Next, a drop trajectory solver computes the distribution of collection efficiency along the airfoil for a given drop size. Finally, a morphogenetic model is used to predict the ice accretion shape and its extent over the entire Reynolds number range under consideration. Our analysis highlights the differences between ice accretions on components of traditional aircraft and UAVs, arising from their differences in cruising speed and airfoil dimensions.
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 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".