Numerical study on aerodynamic noise reduction of bogies with cylindrical leading-edge disturbances
Bibliographic record
Abstract
This study proposes a passive noise reduction method using cylindrical leading-edge perturbation structures in the bogie region of high-speed trains. Large Eddy Simulations combined with the Ffowcs Williams–Hawkings acoustic analogy were performed on a 1:8 scale simplified bogie model at 400 km/h to assess the effectiveness of this approach. Cylindrical leading-edge structures with diameters of 0.5δ, 0.625δ, 0.75δ, and 0.875δ (δ: local boundary layer thickness) were assessed for their impact on flow disturbances, Spectral Proper Orthogonal Decomposition modes, dipole source power, and far-field noise. The 0.875δ cylindrical leading-edge structure achieved the greatest noise reduction, lowering far-field Overall Sound Pressure Level by up to 3.7 dB. This reduction is primarily due to the suppression of tonal peaks near 250 and 500 Hz. The 250 Hz tonal peak primarily originates from a large-scale recirculating feedback flow (L1) within the bogie cavity. The cylindrical leading-edge disturbance modifies the shear layer separation angle, reduces flow impingement on the rear cavity wall, and displaces the recirculation zone (L2) downward, thereby weakening the tonal feedback loop. The 500 Hz tonal peak arises from strong dipole sources observed near the front axle, bogie frame, and the lower surfaces of both front and rear wheels. The cylindrical leading-edge disturbance modifies the dominant flow modes, reducing their interaction with the axle and bogie frame and thereby disrupting flow-structure coupling. This results in a substantial reduction in noise energy, with small-scale feedback structures (S1 and S2) nearly eliminated and the intensity of S3 significantly reduced.
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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 | 0.001 |
| 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".