Coupled occupant-suspension-seat system analysis and vehicle-specific design optimization
Bibliographic record
Abstract
A suspension seat is generally designed to achieve attenuation of low frequency whole-body vibration for a broad range of vehicles. Owing to the strong dependence of its vibration isolation performance on the nature of vibration excitation, an optimal performance could be realized via optimal tuning of suspension for specific classes of vehicles. In this study, a coupled occupant suspension seat model is developed and analyzed to identify optimal design parameters for different classes of vehicles. The multibody dynamic model of the air suspension seat is formulated considering kineto-dynamics of the suspension system and the elastic motion-limiters. The component models, namely, air spring with auxiliary air volume, hydraulic damper, and cushion, are identified from the laboratory-measured static and dynamic characteristics. A two-degree-of-freedom occupant model, is integrated to the suspension seat model to account for contribution due to occupant biodynamics. Model validity is examined using the laboratory-measured responses with a rigid load and a human occupant of similar body weight, and analyzed for different body weights and excitation spectra. The results suggest strong dependency of the suspension resonance frequency on nonlinear behavior of the air spring and the seated body mass. The model is subsequently employed to identify optimal coordinates of the air spring to achieve nearly constant natural frequency for a broad range of body masses. Furthermore, the model is used to identify optimal linkage and suspension design parameters with an objective to minimize the SEAT (Seat Effective Amplitude Transmissibility) for different spectral classes of earth moving machines defined in the ISO-7096 standard, while limiting the suspension travel. It is shown that the optimal coordinates of air spring could yield nearly constant natural frequency for different body weights, while damper could help limit the suspension travel, in addition to notable reductions in SEAT, ranging from 9% to 31% for different spectral classes of vehicles.
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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 | 0.001 |
| 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".