Vibration suppression using two-terminal flywheel. Part II: application to vehicle passive suspension
Bibliographic record
Abstract
The two-terminal flywheel introduced in Part I is suggested to be attached in parallel to the suspension strut whose vibration suppression performance improvement is accordingly investigated in this paper. The passenger comfort, tire grip and suspension deflection are respectively taken as the vibration suppression indices to design the optimal parameters of the proposed and conventional passive suspensions. The optimal single-objective performances of both suspensions arecompared to each other in actual dynamic parameter ranges. Due to the three conflicting and non-commensurable performance objectives, the Chebyshev goal programming approach is subsequently applied to find a multi-objective compromise for a design case. The non-ideal factors of the inverse screw transmission mechanism, which is modeled in Part I, are also taken into consideration to discuss the influence on the vibration suppression performance. The resultsshow that, owing to the presence of the two-terminal flywheel, the proposed suspension employing this novel component significantly outperforms the conventional one in terms of both passenger comfort and tire grip, with practically identical suspension deflection performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".