Modelling of the rotational vibrations of the engine front-end accessory drive system: a generic method
Bibliographic record
Abstract
To investigate the rotation vibration dynamics of the pulleys and the tension arms, and to estimate the vibrations of the belts and the slip ratio between the belt and the pulleys in the engine front-end accessory drive systems, a systematic modelling and analytical method is proposed for engine front-end accessory drive systems; this can be used for modelling engine front-end accessory drive systems with different layouts and different numbers of tensioners, including automatic and fixed tensioners. In the modelling, the rotational pulleys are classified as fixed-axis pulleys and moveable-axis pulleys (such as the pulley in the tensioner). Moreover, the belt spans are classified as the belt spans between the two fixed pulleys, and the belt spans adjacent to the pulley of a tensioner. The equations of motion for each type of pulley and the tension calculation equations for each type of belt span are developed. In this way, the equations of motion for all the pulleys and the tensioner arms can be obtained easily, irrespective of the layout of the tensioners. To obtain the dynamic rotational vibration responses of an engine front-end accessory drive system by the conventional Runge–Kutta method, high-efficiency algorithms or methods are also proposed for calculating the tangent-point coordinates between a belt and the adjacent pulleys and the belt length of the contact arc on one pulley. The proposed modelling and analysis methods are validated by modelling different layouts of the engine front-end accessory drive systems with different types and numbers of tensioners, and also by comparisons between the calculated dynamic vibration responses of the pulleys and the belts and the real experimental data.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".