Phase-Plane Analysis for Evaluating the Lateral Stability of Articulated Vehicles
Bibliographic record
Abstract
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 (β − dβ/dt), the relation between the trailing unit's side-slip angle and side-slip angular velocity (β′ − dβ′/dt), 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 (β − r − ψ), 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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".