Implementation of a Drop Test Impact Rig for Dynamic Testing of High Speed Craft Shock Mitigation Seats and Extraction of Modal Parameters
Bibliographic record
Abstract
The motivation for this work is human-factors-influenced vibration attenuation using high-speed craft (HSC) shock-mitigating seats.The high-g loads occurring at the seat-deck interface locations during slam impacts in moderate to high seas results in serious potential for injury to the occupants.Since various designs and types of shockmitigating seats are available, quantifying their shock attenuation characteristics can be challenging.The need for a standard testing platform and experimental analysis to investigate a seat's effectiveness forms the major objective of research being carried out by Carleton University's Applied Dynamics Laboratory (ADL) in partnership with Defence Research and Development Canada-Atlantic (DRDC-Atlantic).A drop tower was designed and manufactured by the ADL for testing seats in order to characterize their shock mitigating effectiveness by simulating the severe conditions of a slam impact at sea through the use of singular impact testing.Further, in order to identify the seats' dynamic parameters from drop test data, the Eigensystem Realization Algorithm (ERA), a modal-analysis-based system identification method, was applied to two commercial shock-mitigating seats provided by DRDC-Atlantic.The technique was shown to successfully extract the damping ratio as well as the damped and undamped natural frequencies of the seats from impact test data.The dynamic properties of the seats derived from the ERA can be tabulated vis-a-vis their vibration performance metrics illustrated in the thesis, which can subsequently inform decisions related to the design and/or procurement of commercial seats.I learned a lot from their expert guidance during some of the difficult aspects encountered in this project.I also thank them for their financial support.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".