Optimization of Quarter Car Suspension Dynamics Using Power Spectral Density of Irregular Road Profile
Bibliographic record
Abstract
Vibrations are present in all types of vehicles and are very important for comfort and safety during travel. In this work, based on random road profiles obtained from the ISO 8608 standard of 2016, power spectral density (PSD) functions are obtained in terms of displacement and acceleration. Therewith, a methodology to obtain the variance and rms acceleration of the sprung mass is described. In sequence, it is proposed an ideal suspension design methodology that employs a multi-objective optimization technique based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). In the computational implementation, the design criterion is defined as the minimization of the sprung mass vertical variance displacement, as well as the vertical rms acceleration of the sprung mass. Using a quarter car model, the damping and stiffness of the sprung mass are defined as design vectors. As a result, the NSGA II algorithm provides the Pareto front whose numerical values correspond to a set of feasible designs. Comparisons of the results with some methodologies described in the literature are made. The methodology proposed here leads to a decrease in the vibration amplitudes both in the frequency and time domains.
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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 | 0.001 | 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".