Numerical simulations of train aerodynamic noise using hybrid grids
Numerical simulations of train aerodynamic noise using hybrid grids lead image
At approximately 300 km/h, the aerodynamic noise of a train starts to exceed the rolling noise generated by the wheels and tracks. To reduce noise pollution to the environment, and to ensure ride comfort, it is necessary to analyze components of aerodynamic noise.
Researchers have identified the bogie — the chassis containing the wheelset — as the highest contributor to aerodynamic noise. However, numerical simulations employing computational grids struggle to efficiently provide accurate results. Either the simulation’s boundary layer grid is not sufficiently refined, or the bogie geometry lacks detail.
Through the use of a hybrid grid, He et al. addressed challenges in numerical simulations of bogie aerodynamic noise.
“While some studies have attempted to simulate bogie noise by simplifying the geometry or reducing mesh density in the boundary layer, these approaches often compromise simulation accuracy,” author Yuan He said. “The methodology proposed in this paper offers a balanced solution, maintaining high numerical accuracy while managing computational costs.”
The authors generated a structured hexahedral mesh near solid surfaces and an unstructured polyhedral grid for the remaining domain. The two grids are then merged at the interface. In this way, both the quality of the boundary layer and the flexibility of the unstructured grid are preserved, enabling investigation of the flow field around the bogie and its noise generation mechanism.
In the future, the authors plan to apply their methods to more complex high-speed train models.
“Having identified the noise sources in this study, the logical next step would be to focus on noise mitigation strategies, specifically targeting the reduction of aerodynamic noise generated by the train bogie,” said He.
Source: “Numerical approach for the simulation of flow-induced noise around a structure with complex geometry: High-speed train bogie in a cavity,” by Yuan He, David Thompson, and Zhiwei Hu, International Journal of Fluid Engineering (2024). The article can be accessed at https://doi.org/10.1063/5.0212760 .
