A methodology to analyze the vehicle vibration response to deformable terrain stiffness and damping properties
Bibliographic record
Abstract
A dynamic soil–wheel interaction model that considers energy loss due to soil compaction during multiple trafficking can potentially yield an enhanced understanding of vibration responses of a vehicle traversing the deformable terrains. This article presents a practical methodology for modeling the vehicle ride vibration responses, while interacting with deformable terrain irregularities. The proposed formulations incorporate adaptive contact patch and tire deflection in addition to soil sinkage using the Bekker’s pressure–sinkage relationship. The effect of repeated passes of the driven as well as driving wheels on effective stiffness and damping of the soil is also incorporated in the proposed formulations considering a tire slip term by adoption of the Holm’s theory. An in-plane 4-degrees-of-freedom vehicle model is formulated considering a generic compliant tire coupled with the deformable soil model and MSC ADAMS multibody dynamic model is employed for the co-simulations and validation purpose. The coupled terrain–vehicle is analyzed to determine chassis vibration responses together with variations in the dynamic tire–terrain contact force in the time and frequency domains. The results suggested that the root mean square vertical and pitch chassis acceleration responses of the vehicle operating on a deformable terrain are lower than those obtained for the undeformable terrain. The ratio of the dynamic tire force to the static load, a measure of road holding of the vehicle, however, tends to be higher for the deformable terrain. Both the road holding and root mean square chassis acceleration responses, invariably, show a significant increase with increase in the vehicle forward speed. The proposed methodology may serve as an important tool for assessing the vibration exposure of operators and for deriving optimal suspension designs for vehicles operating on deformable terrains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".