Influence of friction wedge characteristics on lateral response and hunting of freight wagons with three-piece bogies
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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