Design optimization of an articulated frame steering system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The articulated frame-steered vehicles (AFSV) exhibit enhanced maneuverability but reduced yaw stability and greater steering power consumption. Apart from kinematics of the steering system, the dynamics of the actuating system strongly influence the performance of the AFSV, which is generally neglected in the reported studies. In this study, a yaw-plane model of the articulated vehicle coupled with the kinematic and dynamics properties of the steering struts is formulated to identify objective measures of the AFSV under steering inputs. The results suggest that the vehicle yaw oscillation/stability, steering power efficiency and maneuverability can be objectively measured in terms of the strut length, yaw oscillation frequency and damping ratio, steering gain, and steering response rate and overshoot. The layout of steering struts and properties of the steering valve and hydraulic fluid are optimized while employing the weighted-sum method and a combination of pattern search and sequential quadratic programming algorithms. The relative weights of individual performance measures were obtained using the analytic hierarchy process (AHP) model. The solutions of the optimization problem revealed more compact articulated frame steering (AFS) system design with over 20% reduction in strut length and 24% gain in the yaw oscillation frequency. Increasing the fluid bulk modulus resulted in more compact AFS layout and further increase in the yaw oscillation frequency with lower response overshoot. The optimal design based on weighted sum of various performance measures, however, revealed negligible changes in terms of the steering power efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it