Ride dynamic evaluations and design optimisation of a torsio-elastic off-road vehicle suspension
Bibliographic record
Abstract
The ride dynamic characteristics of a novel torsio-elastic suspension for off-road vehicle applications are investigated through field measurements and simulations. A prototype suspension was realised and integrated within the rear axle of a forestry skidder for field evaluations. Field measurements were performed on forestry terrains at a constant forward speed of 5 km/h under the loaded and unloaded conditions, and the ride responses were acquired in terms of accelerations along the vertical, lateral, roll, longitudinal and pitch axes. The measurements were also performed on a conventional skidder to investigate the relative ride performance potentials of the proposed suspension. The results revealed that the proposed suspension could yield significant reductions in magnitudes of transmitted vibration to the operator seat. Compared with the unsuspended vehicle, the prototype suspended vehicle resulted in nearly 35%, 43% and 57% reductions in the frequency-weighted rms accelerations along the x-, y- and z-axis, respectively. A 13-degree-of-freedom ride dynamic model of the vehicle with rear-axle torsio-elastic suspension was subsequently derived and validated in order to study the sensitivity of the ride responses to suspension parameters. Optimal suspension parameters were identified using the Pareto technique based on the genetic algorithm to obtain minimal un-weighted and frequency-weighted rms acceleration responses. The optimal solutions resulted in further reduction in the pitch acceleration in the order of 20%, while the reductions in roll and vertical accelerations ranged from 3.5 to 6%.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".