Simulation and Validation of Cavitating Flow in a Torque Converter with Scale-Resolving Methods
Author:
Zhang Jiahua1ORCID, Yan Qingdong12, Liu Cheng13ORCID, Guo Meng14ORCID, Wei Wei13ORCID
Affiliation:
1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China 2. Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250307, China 3. Chongqing Innovation Center, Beijing Institute of Technology, Chongqing 401122, China 4. Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314000, China
Abstract
The purpose of this paper is to study the mechanism and improve the prediction accuracy of transient torque converter cavitation flow by the application of scale-resolving simulation (SRS) methods with particular focus on cavitation vortex flow. Firstly, the numerical analysis of the entire internal flow field of the torque converter was carried out using different turbulence models, and the prediction accuracy of the hydraulic characteristics of the adopted models was analyzed and validated via test data. Secondly, the cavitation and turbulence behavior in the internal flow field were analyzed, and the blade surface pressure according to different turbulence models was compared and validated through test data. Finally, the transient cavitation characteristics of the flow field were studied based on the stress-blended eddy simulation (SBES) model. The prediction accuracy of the cavitation flow field simulation of the torque converter is significantly improved using the SRS model. The maximum error of capacity constant, torque ratio and efficiency are reduced to 3.1%, 2.3%, and 1.3% at stall, respectively. The stator is more prone to cavitation than pump and turbine. The SBES model has the highest prediction accuracy in multiple measurement points, and the maximum deviation can reach 13.32% under stall. Attached cavitation bubbles and periodic shedding cavitation can be found in the stator, and the evolution period is about 0.0036 s, i.e., 279 Hz. The prediction accuracy of different models was compared and analyzed, which has important guiding significance for the high-precision prediction and analysis of fluid machinery.
Funder
National Natural Science Foundation of China Beijing Institute of Technology Research Fund Program for young Scholars Vehicular Transmission Key Laboratory Fund
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
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