Numerical Investigation of the Effects of Sand Collision on the Aerodynamic Behaviour of a High-Speed Train Subjected to Yaw Angles
Bibliographic record
Abstract
In this paper, the aerodynamic performance of the head car of a CRH2 train running in sandstorms was investigated. A numerical simulation method based on Realizable k-ε turbulence model was used to explore the flow features around the high-speed train. The accuracy of mesh resolution and methodology of CFD was validated by wind tunnel tests. A discrete phase model (DPM) was adopted to investigate the effects of sand particle properties (diameter and restitution coefficient) on the aerodynamic performance of the head car. Yaw angle effects with the sand-laden flow on the aerodynamic coefficient were also discussed. The results show that the drag force, lift force, lateral force, and overturning moment of the head car increase significantly due to the sand, and the sand particle properties have dominant effects on the aerodynamic performance of the head car. The impact probability of sand particles on the vehicle increases with the sand particle diameter and the yaw angle increasing. Larger restitution coefficients lead to lager forces of the head car, resulting in more contribution to the aerodynamic coefficients. Owing to the sand collision, a larger yaw angle causes more contribution to the aerodynamic performance of the head car, and the influence of sand properties on the drag force, lateral force and overturning moment are enhanced with the increase of the yaw angle. Using appropriate coatings around the high-speed train can not only reduce the energy consumption, but also improve the lateral stability and the critical operational speed of the high-speed train in the sandstorms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".