Implementation of roughness and elastic-plastic behavior in a wheel-rail contact modeling for locomotive traction studies
Bibliographic record
Abstract
All engineering surfaces are rough on the microscale, and the actual contact area is only a fraction of the geometrical area. Thus, it is essential to include roughness parameters in the model to simulate the real contact scenarios. An algorithm for the calculation of the realistic contact stresses with different surface roughness parameters considering elastic and plastic deformations and tribological behaviour at the wheel-rail interface was further developed and implemented in the wheel-rail coupling in the Gensys railway vehicle multibody software platform. Locomotive multibody model simulations using the developed wheel-rail coupling approach were performed under traction conditions that confirm the workability of the proposed algorithm by means of a comparison with results obtained with the original Extended CONTACT wheel-rail couplings under the same operational and simulation conditions. The results obtained allow an understanding of the difference in contact stress results between rough (Wheel Ra = 0.7 μm, Rail Ra = 0.4 μm) and ‘ideal’ (i.e., perfectly Smooth) contacts, and some limitations are stated in this paper.
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 | 0.000 |
| 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".