Phase-Plane Analysis for Evaluating the Lateral Stability of Articulated Vehicles
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The phase-plane analysis technique has become a powerful tool for analyzing lateral stability of single-unit vehicles. Articulated vehicles, such as car-trailer combinations, consist of multiple vehicle units. Multi-unit vehicles exhibit unique dynamic features compared against single-unit vehicles. For example, a car-trailer may exhibit one of the three unstable motion modes, i.e., jack-knifing, trailer sway and rollover. Considering the distinguished configurations and dynamic features of articulated vehicles, it is questionable whether the phase-plane analysis method based on single-unit vehicles is applicable for analyzing the lateral stability of multi-unit vehicles. In order to address the problem, case studies are conducted to test the effectiveness of the phase-plane method for analyzing the lateral stability of a car-trailer combination, which is represented by a nonlinear vehicle model generated using the CarSim software package. The phase-plane analysis, which examines the relation between the leading unit's side-slip angle and side-slip angular velocity (<b><i>β − dβ/dt</i></b>), the relation between the trailing unit's side-slip angle and side-slip angular velocity (<b><i>β′ − dβ′/dt</i></b>), and the interrelation of the side-slip angle of the leading unit, the yaw rate of the leading unit, and the articulation angle between the leading and trailing units (<b><i>β − r − ψ</i></b>), is conducted to assess the lateral stability of the car-trailer combination with and without an active trailer differential braking (ATDB) system. Built upon the conventional phase-plane analysis method for single-unit vehicles, an innovative phase-plane analysis technique is developed in order to effectively assess the lateral stability of articulated vehicles. The applicability and effectiveness of the newly developed technique is examined and demonstrated.</div></div>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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