Efficient Identification of Naval High-Speed Craft Shock Mitigation Seat Modal Parameters From Drop-Test Data
Bibliographic record
Abstract
Naval high-speed craft (HSC) operating in moderate to high seas experiences high-g and repeated shock loading at the seat–deck interface. These conditions are known to pose a serious potential for injury to the occupants. While various shock-mitigating seats are commercially available; their designs are in many cases quite different, and quantifying their shock attenuation characteristics can be challenging. The need for a standard test platform and experimental analysis methodology to investigate HSC seat effectiveness is a major objective of research being conducted 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 for testing HSC seats in order to characterize their shock-mitigating effectiveness by simulating the severe conditions of a slam impact at sea. Further, in order to identify seat dynamic parameters from drop-test data, the eigensystem realization algorithm (ERA), a modal-analysis-based system identification method, was applied to efficiently extract the modal parameters. 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 evaluated dynamic properties of the seats can subsequently inform decisions related to the design and/or procurement of commercially available seats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".