Aerodynamic Effects on Railway Vehicle-Track Vertical Interactions Coupled With Wheel Flat
Bibliographic record
Abstract
Wheel flats are known to cause large impact forces at the wheel-rail interface, which can induce fatigue damage and failure of vehicle and track components. This large impact force can further increase in the presence of wind load. With demands for increased wheel load and speed, the issue of wheel derailment that can be caused by the wind load has become an serious concern for heavy haul operators. The accurate prediction of the wheel-rail impact force in the presence of wind load is thus necessary in order to timely remove the defective wheel in order to ensure effective operation and safety. This paper presents dynamic response of the railway vehicle running on a track in cross-wind condition in the presence of a single wheel flat. In this study, the vehicle system is modeled as a 5-DOF lumped mass model comprising a quarter of the car body and half of the bogie coupled to two wheels through the primary suspension. The track is modeled as a continuous beam supported by a discrete-elastic foundation of three layers with sleepers and ballast masses. The vehicle subsystem and the track subsystem are coupled through the contacts between the wheels and the rails based on the nonlinear Hertzian contact theory. The Rayleigh-Ritz method is employed to solve the coupled partial and ordinary differential equations of the vehicle-track system. The steady state aerodynamic forces on a moving railway vehicle in cross-wind condition are derived and simulated in time domain. Forces arising due to aerodynamic effect in the presence of wheel flat are investigated in terms of the wheel-rail impact force, the bearing force, the railpad force and the ballast force. Finally, the effects of vehicle and wind speeds on wheel-rail impact load are investigated. This study shows that wind has significant effect on both dynamic and static wheel-rail impact load.
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.
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 | 0.001 |
| 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".