Influence of friction wedge characteristics on lateral response and hunting of freight wagons with three-piece bogies
Bibliographic record
Abstract
In this study, the nonlinear damping characteristics of friction wedges in the secondary suspension of a freight wagon are investigated considering nonsmooth unilateral contact, multiaxis motions, slip–stick conditions, and geometry of the wedges. The parameters of the contact pairs within the suspension were identified to achieve smooth and efficient numerical solutions, while ensuring adequate accuracy. A simulation model of the friction wedge was formulated and analyzed, which revealed highly nonlinear dependence on vertical, roll, and lateral motions between the bolster and the side frames. The friction wedge model was integrated into the multibody dynamic model of a three-piece bogie to study the effects of wedge properties on hunting characteristics. The resulting 114-degrees-of-freedom wagon model incorporated constraints due to side bearings, axle boxes, and the center plates, while the wheel–rail contact forces were obtained using the FASTSIM algorithm. The simulation results were obtained to study hunting properties of the wagon in terms of critical speed and the predominant oscillation frequency, and the effects of wedge friction and geometry on stability characteristics of the freight car. The results showed subcritical Hopf bifurcation of dynamic responses of the wagon. Moreover, an increase in the wedge angle, friction coefficient, and springs free length resulted in a higher critical speed.
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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) | 0.001 | 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".