Drone impact assessment on aircraft structure: windshield and leading edge testing and analysis
Bibliographic record
Abstract
Over the last decade, aerial drones have been increasingly used by both professional and inexperienced users resulting in an increased risk of impact incidents throughout the world. Transport Canada has already implemented regulations dealing with the operation of drones, especially in sensitive areas such as airports where a safe distance must be observed. However, the risk of impacting an aircraft at low altitude remains from both malicious and careless operators. NRC- Aerospace Research Centre has been performing bird impact testing both on aircraft structures, windshields as well as on engines since the 1960s where a number of pneumatic guns with various sizes have been developed and run for various clients. Over that period, NRC’s bird guns were used to fire bird carcasses in accordance with ASTM standard (F330 – 16) to certify aircraft materials, gelatin synthetic birds for research purposes as well as steel balls. The latter test aimed at certifying a bullet-proof windshield of a fighter aircraft with a firing ball velocity reaching 1237 km/h (1.036 Mach, 668 knots). The main objective of this collaborative work between Transport Canada, Defence Research and Development Canada and the National Research Council Canada is to perform a series of experiments simulating impacts between a representative quadcopter drone and various aircraft components (windshield and wing sections) at typical operating conditions of both the aircraft and drone (impact velocity, mass and type of projectile). The wings and windshields from a typical AWM 525 (Part 25) commercial aircraft were used for these impact tests. Tests were performed at operating conditions typical for approach and cruising speeds of an aircraft under 10,000 feet (3,048 m). This report gives an overall description of the test setup for windshield and wing leading edge impact testing and provides experimental data along with analysis and discussion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".