Development of an Icing Test Facility for Rotors and Propellers of Remotely Piloted Aircraft Systems (RPAS)
Bibliographic record
Abstract
The development and calibration of a new facility to test medium size rotors for Remotely Piloted Aircraft Systems (RPAS) under in-flight icing conditions is described. This facility has made use of a 3 m x 6 m cold room available at the NRC which includes a spray system to provide the icing cloud as well as a dedicated rotor stand assembly that incorporates a load cell and dynamometer. Calibration data of the spray drop sizes and liquid water content are provided and compared to conditions of the natural environment as detailed in icing regulations for transport category airplanes, i.e., CFR 14 Part 25 Appendix C and O. Data to examine the sensitivity of rotor performance, under a constant liquid water content to various droplet sizes are provided for a medium sized rotor. Tests have also been performed that examine the ability of the rotor to maintain predefined thrust, torque and power performance throughout an icing encounter of fixed duration. For the purposes of this study, the rotor performance is determined by considering a hypothetical quadcopter RPAS concept with a baseline vehicle weight (15 kg), and a minimum operation time (122 seconds) during which rotor performance, and hence controlled flight, must be maintained.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".