Computational fluid dynamics simulation of rail vehicles in crosswind: Application in norms and standards
Bibliographic record
Abstract
The application of computational fluid dynamics (CFD) to the determination of aerodynamic coefficients for crosswind stability in the context of vehicle assessment has been studied as part of the AeroTRAIN project. The work consisted in establishing best practice guidelines for the use of standard Reynolds-averaged Navier–Stokes (RANS) approaches using a streamlined and less-streamlined vehicle, project partners applying different computational codes and turbulence models to a common vehicle, and then application to further vehicles in order to cover a range of different vehicles and yaw angles. The simulations were complimented with wind tunnel measurements to allow the accuracy of standard RANS approaches to be judged for various vehicle shapes and yaw angles. This paper summarises the overall results and the recommendations made for the use of CFD in vehicle assessment of crosswind stability in relation to the EN 14067-6: 2010 standard. The main aspects of the guidelines are reported in a separate paper in this special issue. The considered standard allows the use of CFD for vehicle speeds up to a maximum of 200 km/h whereas the HS RST TSI (2008) only allows aerodynamic coefficients to be determined using wind tunnel measurements. The obtained results show that a well-performed RANS CFD can predict the aerodynamic coefficient of streamlined trains with a relatively high accuracy. The challenges increase for blunter-shaped trains and may be further influenced by equipment installed on the roof of a train. Combined with the developed simulation guidelines it is considered that CFD can be used as an alternative to wind tunnel tests in all cases provided that the accuracy of the approach is validated on a benchmark train with similar features to those of the simulated train.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".