Performance Analysis of Suspension Seats under High Magnitude Vibration Excitations: Part 1: Model Development and Validation
Bibliographic record
Abstract
Performance analyses of suspension seats subject to high magnitude excitations require particular modeling considerations associated with impacts against the motion limiting elastic buffers, possible loss of contact between the seat and the occupant, and component characterization over a wide range of inputs. In this study, three different suspension seats are considered to formulate a generalized model that would be applicable under low to high magnitude excitations. The static and dynamic characteristics are evaluated in the laboratory under a wide range of excitations and seat preloads. General model structures are proposed to characterize the components of the selected suspension seats as functions of the preload and nature of excitation. A general model for the suspension seats is formulated upon integration of the component models and consideration of the potential body-hop motions. The validity of the proposed model is examined under a number of excitations representing continuous random vibration environment of different vehicles, such as urban buses and class-1 construction machinery (EM1), and transient sprung mass oscillations in the vicinity of the suspension seat natural frequency. The validity of the model is further examined for all three seats subject to amplified excitations of the urban buses and the construction machinery. The results of the study suggest that the proposed model can be effectively applied to assess the suspension performance under high magnitude excitations that induce repetitive impacts with motion limiting buffers. The influences of various design parameters on the shock and vibration isolation performance of the selected suspension seats are presented in the second part of this work.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".