The Rolling Quarter Car Model a Method to Incorporate Dynamic Tire Response in Grip Optimization
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Selection of springs and dampers is one of the most important considerations when finalizing a race car suspension design. It is also one of most complex due to the dynamic interaction of the vehicle with the ground. Current tuning methods for spring and dampers' effect on vehicle ride can be based on simplified dynamic models of the vehicle, such as the quarter-car model. While efficient computationally, the traditional quarter-car model does not account for the non-linear variation in grip seen by a fluctuating contact-patch. Both amplitude and frequency of suspension oscillation contribute to loss of tire grip. The method can be improved by incorporation of a dynamic tire model, though resulting in non-linear effects. An improved ‘rolling quarter-car’ model is created, which includes the effect of dynamic tire forces in the analysis of improved grip. Using typical Formula SAE race car, characteristics as a test case, a linearized dynamic model is made. The effect of suspension parameters on the dynamic tire forces produced are surveyed. Improvements made by the new model are presented.</div></div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".