Hunting Characteristics of a Freight Car in Presence of Secondary Suspension Non-Smooth Contact Dynamics
Bibliographic record
Abstract
In this study, the nonlinear damping characteristics of friction wedges in the secondary suspension of a freight truck are investigated considering non-smooth contact, geometry, loss of contact and multi-axis motions. The friction wedge model is integrated to a nonlinear multi-body dynamic model of a three-piece truck to study its hunting characteristics. The 114-degrees-of-freedom model also integrates constraints due to side bearings, axle boxes and center plates, while the wheel/rail contact forces are obtained using FASTSIM algorithm considering non-elliptical contact. The parameters of contact pairs within the suspension are identified to achieve smooth and efficient numerical solutions, while ensuring adequate accuracy. The simulation results are presented to illustrate the hunting properties of the truck in terms of critical speed and oscillation frequency. The results showed subcritical Hopf-bifurcation in the lateral dynamic responses.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".