Modeling impact of drones on flat plates
Bibliographic record
Abstract
Experiments on the impact of cannon-launched Phantom DJI 3 quadcopters onto 1-m square aircraft-grade aluminum flat plates (1.6 mm and 6.35 mm thick) at velocities of 130 m/s (250 knots) and 70 m/s (140 knots) are presented, and finite element modeling of the impacts is also described. Load histories at the corners of the plate, central deflection , and possible perforation of the plate are modeled and compared with experimental results. Failure of drone components was modeled, as they were significantly damaged in all of the tests. Failure of the plate was also modeled, as in the high-speed tests with thin plates, the drone perforated the plate. Predictions of the total peak load on the plates are within 20% of the experimental values and the central deflections are within 10% of the experimental values. Additionally, modal analysis reveals that the characteristic half period of 5-6 ms observed in the load histories corresponds to the natural frequencies of the structure that holds the plate in the test. Using the insights gained from the simulations, simple analytical models, wherein the components of the drone are modeled as blunt, rigid objects and the target is modeled as mass and dashpot, were developed. These yield second-order ordinary differential equations whose solutions provide rapid estimates of the peak load and deflection in all tests to within 15% of the experimental values. To estimate the threshold impact velocity to perforate the plate, an analytical model is presented. The major contributions of this article are validated work flows to develop drone finite element models that do not require extensive characterization of drone components, and simplified analytical models for rapid assessment of drone impacts.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".