2022
DOI: 10.32604/fdmp.2022.017508
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Numerical Investigation on the Aerodynamic Noise Generated by a Simplified Double-Strip Pantograph

Abstract: In order to understand the mechanism by which a pantograph can generate aerodynamic noise and grasp its farfield characteristics, a simplified double-strip pantograph is analyzed numerically. Firstly, the unsteady flow field around the pantograph is simulated in the frame of a large eddy simulation (LES) technique. Then the location of the main noise source is determined using surface fluctuating pressure data and the vortex structures in the pantograph flow field are analyzed by means of the Q-criterion. Base… Show more

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Cited by 2 publications
(2 citation statements)
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“…Since the TSPL of T-shaped reducing tee is mainly affected by the inlet flow velocity and pipe diameter ratio [27]. And to further predict the flow-induced noise characteristics of T-shaped reducing tee, the TSPL with different flow velocities and pipe diameter ratios are calculated, which is shown in Table 4.…”
Section: Prediction Of the Flow-induced Noisementioning
confidence: 99%
“…Since the TSPL of T-shaped reducing tee is mainly affected by the inlet flow velocity and pipe diameter ratio [27]. And to further predict the flow-induced noise characteristics of T-shaped reducing tee, the TSPL with different flow velocities and pipe diameter ratios are calculated, which is shown in Table 4.…”
Section: Prediction Of the Flow-induced Noisementioning
confidence: 99%
“…23 By comparing the commonly used k-e and k-v models and performing multiple calculations, it is found that the SST k-v model has better accuracy and algorithm stability than the k-e model in the near-wall region and has high accuracy in simulating separated flows. 24,25 The SST k-v model has advantages in predicting near-wall and vortex flow and is suitable for boundary layer flow, separation, and transition in adverse pressure gradients. 26 Therefore, the SST k-v model can better handle wall-confined flows with high strain rates and large curvature of streamlines.…”
Section: Turbulence Modelmentioning
confidence: 99%