Experimental Study on the Dynamic Characteristics of Hydro-Pneumatic Semi-Active Suspensions for Agricultural Tractor Cabins
Bibliographic record
Abstract
This study aims to establish a test method to obtain the dynamic characteristics of hydraulic-pneumatic semi-active suspensions used in tractor cabins. Because dynamic characteristics are utilized in simulation models for developing suspension control logic and must be secured to improve control performance, an accurate test method must be established. The dynamic characteristics of the suspension, i.e., the spring constant and damping coefficient, were obtained by changing the current and velocity conditions. An exciter was used as a test device to control the displacement and velocity of the hydraulic cylinder. In order to derive the spring constant of the suspension, a low-speed reciprocating motion test was performed to obtain the force-displacement diagram and to derive the damping coefficient; 48 tests were performed under 6 velocity conditions and 8 current conditions to obtain a force-velocity diagram for each result. The spring constant of the suspension was confirmed using the slope of the trend line in the force-displacement diagram obtained through the low-speed reciprocating motion test of the suspension. In addition, the damping coefficient was calculated using the force-velocity diagram obtained through the reciprocating motion test of the suspension under various current and velocity conditions.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".