An Experimental Methodology for Characterizing High Speed Craft Seat Suspension Components
Bibliographic record
Abstract
A generalized test procedure was developed with the aim of experimentally characterizing the static and dynamic properties of Shock Mitigation Seats and their components.The composite procedure comprises static, friction, and dynamic tests.A user-friendly Component Testing Machine was designed and built by Carleton University's Applied Dynamics Laboratory to effectively apply the developed procedure to the strut components of the seats, the seat cushions, and the assembled seats.The developed individual test procedures were applied to three typical seats with passive suspension components and one seat with a semi-active suspension system.The three seats with passive suspension were disassembled and their individual components were tested.The semi-active seat was only tested as a full seat.Displacement, velocity, and force data were recorded throughout the testing.Static test data were used to obtain load-displacement plots, stiffness properties, as well as static and kinetic friction values for all the tested elements.Dynamic test data produced hysteresis force-displacement plots and force-velocity plots, from which damping characteristics were extracted.Variation of the damping coefficient with respect to frequency and amplitude was determined for all the tested components.Developing the test procedure, designing and building the component test machine, Dr. Robert Langlois and Dr. Fred Afagh, for their excellent guidance, patience, and willingness
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".