Multibody system modelling of unmanned aircraft system collisions with the human head
Bibliographic record
Abstract
Understanding the impact severity of unmanned aircraft system (UAS) collisions with the human body remains a challenge and is essential to the development of safe UAS operations. Complementary to performing experiments of UAS collisions with a crash dummy, a computational impact model is needed in order to capture the large variety of UAS types and impact scenarios. This article presents the development of a multibody system (MBS) model of a collision of one specific UAS type with the human body as well with a crash dummy. This specific UAS type has been chosen because data from experimental drop tests on a crash dummy is available. This allows the validation of the MBS model of UAS impacting a crash dummy versus experimental data. The validation shows that the MBS model closely matches experimental UAS drop tests on a crash dummy. Subsequently, the validated UAS MBS model is applied to predict human body injury using a biomechanical human body model. Head and neck injury from the frontal, side and rear impact on the human head are predicted at various elevation angles and impact velocities. The results show that neck injury is not a concern for this specific UAS type, but a serious head injury is probable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".