Nonsmooth Dynamics of a Gear–Wheelset System of Railway Vehicles Under Traction/Braking Conditions
Bibliographic record
Abstract
Abstract Gear–wheelset system is a crucial substructure in railway vehicles which affects the operation safety and system reliability, especially in the process of traction and braking conditions. Unlike the general gear transmission system, the gear–wheelset system of railway vehicles operates under an environment with several nonsmooth factors; therefore, it is necessary to analyze the nonsmooth dynamics of the gear–wheelset system for understanding dynamic characteristics better. Herein, a planar dynamic model of the gear–wheelset system of railway vehicles considering motor-driving torque, braking torque, wheel–rail nonlinear interaction forces, nonlinear meshing damping, and piecewise continuous time-varying meshing stiffness is proposed. Then, the proposed model is validated by a simpack model using wheelset's longitudinal velocity. Subsequently, two numerical simulations were performed to reveal the nonsmooth dynamic characteristics under traction and braking conditions. The simulation results indicate that the dynamic stationary point exists in nonsmooth dynamics under traction and braking conditions, which is a critical boundary for transiting any state to a dynamic equilibrium. Besides, the results exhibit the inseparable relationship between time-frequency dynamic characteristics, slip velocity, and wheel–rail nonlinear interaction forces. The effects of harmonic torque under traction conditions and compound braking behavior under braking conditions significantly affect these dynamic characteristics. Additionally, sufficient driving torque can increase the proportion of forward contact and improve the smoothness of the rotation, and the intermittent gear contact phenomenon occurs alternately and frequently in the traction condition. Conversely, only reverse contact occurs in the braking condition.
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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 | 0.000 |
| 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) | 0.001 | 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".