Numerical analysis of the effect of train length on train aerodynamic performance
Bibliographic record
Abstract
The improved delayed detached eddy simulation is adopted in the present study to investigate the influence of the train length on its aerodynamic performance. The low y+ wall treatment and the cubic constitutive relation are adopted to resolve the viscous flows and model the anisotropic turbulence within the boundary layer. The analysis implied that the distribution region and intensity of velocity fluctuation are strengthened, resulting in a larger turbulence kinetic energy distribution and a higher boundary layer thickness as the train length increases. A reduction in the streamwise velocity and the negative pressure with the increasing train length on the tail train is observed, resulting in lower drag and lift coefficients. As the length of the train increases, both the mean and instantaneous slipstream velocities are increased. The boundary layer thickness and the skin friction coefficient are compared with flat plate theory, reduced-scale, and full-scale experiments, proving the ability of numerical simulation to model the boundary layer velocity profile and skin friction coefficient distribution correctly. The wake structures are identified by the Spectral Proper Orthogonal Decomposition method, the dominant mode frequency decreases, and the wavelength becomes larger as the length of the train becomes longer due to the thickening boundary layer.
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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 | 0.000 |
| 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) | 0.001 | 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".