Fluid–structure interaction analysis of the aeroelastic response in the flexible outer windshield of a high-speed train considering installation location
Bibliographic record
Abstract
As train speeds increase, the outer windshields become susceptible to vibration deformation and fatigue damage under aerodynamic forces. Previous research on outer windshields treated them as rigid structures, neglecting the interaction between aerodynamic excitation and windshield vibration. This paper proposes the unsteady Reynolds-averaged Navier–Stokes turbulence model and the fluid–structure interaction (FSI) method to study the vibration of outer windshields. The numerical method's accuracy and reliability are validated through full-scale test data and videos. Step heights between the outer windshields and the train body are utilized to suppress the vibration of the outer windshield. The results indicate that the upstream outer windshield (W1) deforms outward first, followed by the downstream outer windshield (W2) deforming outward during W1's rebound process, resulting in periodic vibration. The step causes the flow separation region of the upstream fluid to lag when the step height increases from 0 to 30 mm. Thus, the average vibration displacements of W1 and W2 decrease by 96.8% and 94.9%, respectively. The dominant frequencies of the corresponding displacement power spectra both increase by 0.5 Hz, reducing the likelihood of flow-induced vibration in the outer windshields. Additionally, the average stresses of W1 and W2 decrease by 82.9% and 34.4%, respectively, significantly enhancing the fatigue life of the outer windshields. The results of this study provide important guidance for the design of windshield structures for the next-generation high-speed trains. The FSI simulation method offers new insights for the study of flow-induced vibration phenomena in thin-walled structures of trains.
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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 | 0.002 |
| 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".