Vehicle–track interaction with consideration of rail irregularities at three-dimensional space
Bibliographic record
Abstract
Modelling of vehicle–track interaction has long been a hot and interesting topic. In multibody dynamics based on force-equilibrium methods, Hertzian contact and creep theories have been applied in vehicle–track model constructions. In another aspect, the complementarity-based methods have also been widely used in establishing vehicle–track interaction, but still having drawbacks on characterization of wheel–rail contact geometry/creepage in three-dimensional space. In this study, we draw essences from methodologies of refined wheel–rail coupling models and energy-variational principle, and a model for vehicle–track three-dimensional interactions with inclusion of rail irregularity excitations is newly developed. This model possesses high accuracy compared with Hertzian contact, FastSim, and vehicle–track coupled model in the middle-low frequency domain, and also, the advantages in computational stability are possessed. In this model, the unevenness of rail irregularities at the three-dimensional space is preliminarily considered by taking a hypothesis of normal distribution and accordingly, the wheel–rail three-dimensional constraint equations are presented. Extensively, a series of numerical examples are shown to verify the effectiveness and engineering practicability of this model. Besides, the influence of rail three-dimensional irregularities on the dynamic performance of vehicle–track systems is further explored, which shows when the trochoid of the wheel–rail contact points changes rapidly, the additional inertial effects brought out by rail irregularities might exert great influence on wheel–rail forces.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".