Optimization of Asymmetric Damper Parameters of an Automotive Suspension for Minimal Camber Angle Variations
Bibliographic record
Abstract
Asymmetric dampers invariably employed in automotive suspensions are known to cause ‘damper jacking’. The influence of the damper jacking on the suspension kinematic responses, particularly variations in the camber angle, are generally ignored while synthesizing a damper. This study presents 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. Simulation studies employing a kineto-dynamic quarter-car model comprising a bilinear damper revealed increase in the camber angle variations with an increase in the damper asymmetry, while this increment showed nonlinear relation with the suspension deflection. This study further investigates synthesis of an optimal two-stage asymmetric damper to yield a compromise between the conflicting performance measures. A composite performance index comprising the ride comfort and road holding measures with limit constraint on camber angle variation is formulated to seek optimal damper parameters. The results are presented so as to yield design guidance for synthesis of asymmetric dampers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".