Aerodynamic performance and flow dynamics of High-Speed train under crosswind: Effect of turbulence model
Bibliographic record
Abstract
This study employs Reynolds-averaged Navier–Stokes (RANS), improved delayed detached eddy simulation (IDDES), scale-resolving hybrid (SRH), and large eddy simulation (LES) turbulence models to conduct a comparative numerical analysis of the aerodynamic load characteristics and surrounding flow field structure of a high-speed train at a Reynolds number of 250 000 and a yaw angle of 30°. The influence of the Gamma Re-Theta transition model and grid resolution on the computational results is investigated. The findings indicate that the LES method based on the wall-adapting local eddy-viscosity (WALE) model performs well with sufficiently refined grids, exhibiting small errors compared to wind tunnel tests. Without using a transition model, the RANS, IDDES, and SRH turbulence models are unable to accurately capture the near-wall flow characteristics at any grid resolution, consequently failing to accurately predict the pressure and pressure fluctuation distribution on the train surface. This fundamental deficiency stems from the models' difficulty in capturing instabilities within the free shear layer. When a transition model was employed, both the IDDES and SRH turbulence models demonstrated improved capability in predicting the train's aerodynamic performance. Furthermore, SRH exhibited superior grid adaptability, and it maintained excellent performance even on coarse grids and achieved computational accuracy comparable to that of the LES model. The core conclusion of this study confirms that the SRH model, leveraging its robust performance and potential for significant computational cost reduction, emerges as a promising approach for studying highly separated external flows at high Reynolds numbers.
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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 | 0.001 |
| 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".