Comparative Study of Turning Performance Between a Vehicle With Planar Suspension Systems and a Conventional Vehicle
Bibliographic record
Abstract
In a conventional vehicle, the vibration caused by road obstacles can not be effectively isolated in the longitudinal direction due to the fact that the longitudinal connections between the chassis and wheels are typically very stiff compared with the vertical connections. To overcome this limitation, a novel concept design of a planar suspension system (PSS) is proposed. The rather stiff longitudinal linkages are replaced by elastic ones in a PSS so that the vibration along any direction in the wheel plane can be effectively isolated. The soft longitudinal connection can change the wheelbase and the vehicle’s weight distribution at the front and rear wheels, and may further change the handling performance. This paper presents a comparative study of the handling behaviour of a PSS vehicle and a similar conventional vehicle in cases of a combining operation between a turning and acceleration, and a turning on a road with pothole. The study demonstrates that the PSS vehicle has the potential to absorb the vibration in the longitudinal direction without sacrificing the handling performance. The handling behaviour of a PSS vehicle is generally comparable with, and under some conditions, even better than that of a conventional vehicle.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".