Numeric Simulation of Single Passage Ternary Turbulence Model in Hydraulic Torque Converter

Based on the renormalization group theory, a hydraulic torque converter 3 D turbulent single flow passage model is constructed and boundary condition is determined for analyzing the influence of the fluid field characteristic and parameters on the macroscopic model. Numerical simulation of the single fluid path is processed by computational fluid dynamics and the calculated results approach to experimental data well, and especially in low transmission ratio the torque and head results are more close to experimental data than the calculated results of beam theory. This shows that the appropriate ternary analysis method and reasonable assumption of boundary condition may analyze the flow field more precisely and predict the performance of torque converter more accurately.

Design and Optimization of Bionic Janus Blade in Hydraulic Torque Converter for Drag Reduction

The chief aim of the present work was to achieve drag reduction in stator blades with liquid using boundary layer control. A stator blade of hydraulic torque converter with bionic grooves in suction side and hydrophobic surface in pressure side was designed. The hydrophobic surface was created using anodic oxidation method and irregular A1203 thin films were found on the surface. They formed hierarchical structure consisting of mini porous structures and microscopic pore spaces, resulting in the hydrophobicity. The bionic groove was designed by Computational Fluids Dynamics （CFD） method. Multi-Island Genetic Algorithm （MIGA） was adopted for groove multi-objective optimization. Through optimization, the maximum drag reduction was close to 12% in oil. In addition, the drag reduction calculation was verified by closed channel experiment and ＂Tire Vortex＂ was proposed to explain the drag reduction mechanism. The bionic Janus blade could maintain its initial profile without any additional device, which had lower risk and less cost. The results are encouraging and show great potential to apply in other flow machineries.

Hybrid Loader Automatic Shift Strategy Based on Neural Network

Hybrid loader 's comprehensive performance mainly depends on the performance of hydraulic torque converter during its driving and working. Hybrid loader and hydraulic torque converter are taken for the research objects. The primary characteristic curve of hydraulic torque converter and the traction curve of hybrid loader are acquired by analyzing the characteristic parameters of hydraulic torque converter, the characteristic parameters of engine, the characteristic parameters of battery pack and geometric parameters of hybrid loader. The gear shift curves based on the best energy saving performance and the best power performance are acquired respectively with the opening of throttle,the speed of pump wheel and the speed of turbine as parameters. Then the two curves are combined to get the comprehensive gear shift curve. Radical basis function( RBF) neural network is applied to building the gear shift strategy to keep hybrid loader with the best power performance and energy saving performance. The experimental bench is set up for experimental verification. It proves that both of the power performance and energy saving performance of hybrid loader are improved effectively by using the automatic shift strategy.