Abstract
Flow field and pressure measurements are typically conducted independently in the wind tunnel. Consequently, a comprehensive aerodynamic database, incorporating synchronized flow and pressure fields for rectangular cylinders, is notably lacking despite its paramount importance in both fundamental research and engineering applications. In this study, a synchronized particle image velocimetry (PIV) and multi-point pressure scanning (SPMPS) test method is employed to simultaneously capture the synchronized flow and pressure fields of rectangular cylinders with a low sampling frequency of 15Hz. Rectangular cylinders with multiple side ratios ranging from 0.4 to 5 under a number of wind angles of attack ranging from 0 to 90 with 5-degree interval were tested in close-loop wind tunnel with less than 0.4% turbulence intensity, thereby establishing a unique synchronized aerodynamic database for rectangular cylinders with a total of 299 cases. Additionally, a deep neural network model was trained to predict mean pressure coefficients for rectangular cylinders with arbitrary side ratios less than 5 and various angles of attack, significantly enhancing the comprehensiveness of the database.
| Original language | English |
|---|---|
| Article number | 119506 |
| Journal | Engineering Structures |
| Volume | 326 |
| DOIs | |
| State | Published - 1 Mar 2025 |
| Externally published | Yes |
Keywords
- Database
- Deep learning
- PIV
- Rectangular cylinder
- Wind tunnel testing
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