Optimal synthesis of a two-stage asymmetric damper of an automotive suspension considering wheel camber variations
Bibliographic record
Abstract
The influences of damper asymmetry on the camber angle variations of a double wishbone type of suspension, together with the dynamic responses under measured urban road inputs at different vehicle velocities, were investigated. Simulation studies employing a kineto-dynamic quarter-car model comprising a bilinear damper revealed complex dependence of the dynamic and kinematic responses on the vehicle forward velocity. The study further revealed an increase in camber angle variations with an increase in damper asymmetry, while this increment showed a non-linear relationship with the suspension deflection. The camber angle variation under road inputs thus poses an additional design compromise apart from those associated with the well-established conflicting measures of ride comfort, rattle space, and road-holding properties. The study also investigated the synthesis of an optimal two-stage asymmetric damper to yield a compromise between these conflicting performance measures under road inputs at different vehicle forward velocities with consideration of minimal camber angle variations. A composite performance index, comprising the ride comfort and road-holding measures with limit constraint on camber angle variation, was formulated to seek optimal damper parameters. The results showed that an optimal asymmetric damper could be identified to yield acceptable design compromise over a wide speed range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".