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A Study on Using Location-Information-Based Flow Field Reconstruction to Model the Characteristics of a Discharging Valve in a Hydrodynamic Retarder.

Authors :
Wei, Wei
Wang, Yuze
Tao, Tianlang
Chen, Xiuqi
Hu, Naipeng
Ma, Yuanqing
Yan, Qingdong
Source :
Machines; Apr2023, Vol. 11 Issue 4, p460, 17p
Publication Year :
2023

Abstract

In modeling the characteristics of a discharging valve in a hydrodynamic retarder, it is commonly required to determine the value of the flow area to calculate the force on the spool. However, the flow area often relies heavily on empirical or simulation data, which leads to increased uncertainty and computational cost, especially with the variation in the spool displacement. To overcome these shortcomings, Res-SE-U-Nets (networks that combine residual connections, squeeze-and-excitation blocks, and U-Net) are used to reconstruct the velocity field, and they have shown exceptional performance in image-to-image mapping tasks. The dataset of computational fluid dynamics (CFD) results for the velocity field is collected and verified using particle image velocimetry (PIV). The results show that Res-SE-U-Nets can capture the location information of the flow field using a training set of only 120 data points. By utilizing location information in velocity field reconstruction, the flow area can be directly obtained under different spool displacements and pressures to calculate the spool force. The valve characteristics calculated with this method show an error of less than 2% when compared with CFD results, which confirms the validity and effectiveness of this method. The proposed method, which utilizes location information extracted from flow field prediction results, is capable of calculating valve characteristics. This approach also demonstrates the feasibility of using Res-SE-U-Nets for flow field reconstruction. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20751702
Volume :
11
Issue :
4
Database :
Complementary Index
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
163437037
Full Text :
https://doi.org/10.3390/machines11040460