Investigation of tolerance for icing of remotely piloted aircraft systems (RPAS) rotors / propellers: phase 6
Bibliographic record
Abstract
Over the last five years, the National Research Council (NRC) and Transport Canada (TC) have entered into collaborative research agreements as part of TC’s Remotely Piloted Aircraft System (RPAS) Task Force with the objective of creating an evidence-based regulatory framework for safe operation of small RPAS in icing. This work has concentrated on the rotors and propellers of these systems and examined the aerodynamic degradation in terms of reduced thrust and increased power requirements resulting from encounters with icing conditions. This phase of the research program examines the data taken from single rotors exposed to icing, and compares them to the operation of full RPAS systems within similar hazardous environments and, in doing so, enables the development of methods that could be employed to demonstrate the means of compliance of the safe flight in icing. Using a combination of established icing envelopes related to general and transport category aircraft and employing data obtained from the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis v5 (ERA5), a range of potentially hazardous icing conditions specific to the operations of small RPAS is presented. These data, along with the aerodynamic degradation observed for small RPAS rotors and propellers, are used to present a framework for the assessment of safe RPAS flight in icing.
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 | 0.001 | 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 | 0.000 | 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".