A Comprehensive Quarter-Car Model for Kinematic and Dynamic Analysis of a Suspension
Bibliographic record
Abstract
The conflicting dynamic performances of a vehicle such as ride quality, road holding and rattle space requirements have been widely studied using either linear or nonlinear two DOF quarter-car models. Such models, however, cannot account for contributions due to suspension kinematics and joint compliances. Considering the proven simplicity and effectiveness of a quarter-car model for such analyses, this paper discusses a comprehensive quarter-car dynamic model to study the influences of the linkage geometry and flexible joint bushings on selected performance measures. An in-plane two-DOF model was formulated for a double wishbone suspension comprising an upper control arm, a lower control arm, and a strut mounted on the lower control arm. The dynamic responses of the model were evaluated under harmonic and idealized rounded-pulse displacement excitations. The responses of the proposed model with free and flexible joint conditions were compared with those of the conventional quarter-car model to illustrate the contributions due to suspension kinematics and joint bushings. Owing to the asymmetric kinematic behavior of the suspension system, the dynamic responses of the comprehensive model were also observed to be asymmetric about the equilibrium. The responses of the model with flexible joint bushings revealed approximately 5% variations from that of the model with free joints. Furthermore, the upper control arm bushings have exhibited more significant influence on the dynamic responses than that of the lower control arm bushings.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".