Analysis of a Flow Volume Regulated Frame Steering System and Experimental Verifications
Bibliographic record
Abstract
The articulated frame steering (AFS) systems are widely implemented in construction, forestry and mining vehicles to achieve enhanced maneuverability and traction performances. The kinematic and dynamic performances of articulated steered vehicles are strongly influenced by properties of the frame steering system. In this paper, a flow volume regulated frame steering system is described and analytically modelled. The analytical model of the steering system is formulated in conjunction with yaw-plane model of a 35 tonnes mining vehicle to investigate steady as well as transient responses of the steering system and the vehicle. A field test program was undertaken to measure responses of the steering system and the vehicle under nearly constant speed turning as well as path-change maneuvers. The validity of the vehicle and the steering system model is demonstrated on the basis of the measured data in terms of steering wheel angle, articulation angle, hydraulic struts pressure, struts displacements and vehicle yaw rate. The results revealed reasonably good agreements between the measured and model responses under the maneuvers considered. The model could thus serve as an important tool to study the design optimization of steering system components so as to achieve minimal yaw oscillations and higher critical speeds. This is demonstrated through a design parameter sensitivity analyses to assess the effect of steering valve time constant on the vehicle response. The results suggested that reducing the valve time constant can increase the response speed of both the AFS system and the vehicle, while reducing the overshoot in the articulation angle response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".