Optimization of Dual-Turbine Hydraulic Torque Converter Based on Meta-model of Optimal Prognosis

Abstract The internal flow field of a specific model of hydraulic torque converter exhibits turbulence, resulting in suboptimal efficiency under the designed operating condition. This study aimed to improve the peak efficiency of the hydraulic torque converter by optimizing its design parameters. Computational fluid dynamics (CFD) analysis identified turbulence predominantly within the pump impeller region. Parameterized modeling of the pump blades was conducted, and an automatic optimization process was implemented. A Meta-model of Optimal Prognosis (MOP) was developed based on 250 sample points, and was then utilized to continuously optimize the blade parameters through evolutionary algorithm (EA). The results indicate an increase in the efficiency of the optimized torque converter from 82.2% to 84.34%, accompanied by a widening of the high-efficiency range ( η ≥75%) from 2.26 to 2.44. Furthermore, the torque coefficient of the impeller was enhanced.