Optimization of Suspension Settings Using Genetic Algorithms for Improved Handling and Ride Comfort on Different Terrains: A Quarter Car Model
Bibliographic record
Abstract
This paper presents an optimization approach using genetic algorithms to improve the handling and ride comfort of a quarter car model on different terrains. The suspension settings are optimized with the objective of minimizing the root mean square (RMS) value of the vertical acceleration of the sprung mass, while also reducing the lateral acceleration of the vehicle during cornering. The quarter-car model is simulated on three different terrains, including a smooth road, a rough road, and a sinusoidal road, to evaluate the performance of the optimized suspension settings. The results indicate that the optimized suspension settings significantly improve the ride comfort and handling of the vehicle on all three terrains. The RMS value of the vertical acceleration of the sprung mass is reduced by an average of 0.38, 0.21, and 0.42 on the smooth, rough, and sinusoidal roads, respectively, compared to the unoptimized suspension settings. Moreover, the lateral acceleration of the vehicle during cornering is reduced by 0.2, 1.2, and 1.6 on the smooth, rough, and sinusoidal roads, respectively, demonstrating improved handling. The optimization of suspension settings using genetic algorithms proves to be an effective approach for improving the ride comfort and handling of a quarter car model on different terrains. The results demonstrate a significant reduction in the RMS value of the vertical acceleration of the vehicle and lateral acceleration during cornering, which translates to a more comfortable and stable ride for passengers.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".