It is hard to simultaneously improve the peak efficiency (η*) and the width of the high-efficiency region (Gη) for a hydrodynamic torque converter. A combination of comprehensive CFD simulation and multi-objective optimization was pretested. The elaborate CFD simulation calculation included a reasonable mesh layout, a robust algorithm and a correct turbulence model, whose results were also experimentally verified. In our study, the Kriging surrogate model was first used to construct a nonlinear relationship between the inlet and outlet angle and the economic performance index of the hydrodynamic torque converter. To ensure that the accuracy of the surrogate model meet the requirements, we also used 10 sets of sample points to verify the accuracy of our surrogate model. The accuracy is found to meet the requirements, which shows that the accuracy of the constructed surrogate model is relatively high. We choose to apply the second-generation non-dominant sorting genetic algorithm (NSGA-II) to solve our problem. After solving the Pareto frontier solution set, we obtain a set of global optimal solutions on the Pareto frontier solution set. The optimization results show that the η* is increased by 2.49% and that the Gη is increased by 14.23%. We extracted the flow field structure near the turbine region, characterized the difference between original and optimal model from the flow field perspective, and demonstrated the accuracy of our optimization results. Finally, we used CFD to verify our optimization results, further illustrating the accuracy of the optimization results prediction. Literature research indicates that a large amount of experiments to optimize the η* and the Gη of the hydrodynamic torque converter will bring huge trial cost and time cost. We conclude from our research that the proposed calculation method can solve such problems well.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-objective optimization of the cascade parameters of a torque converter based on CFD and a genetic algorithm


    Contributors:
    Ran, Zilin (author) / Ma, Wenxing (author) / Liu, Chunbao (author) / Li, Jing (author)


    Publication date :

    2021-07-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Multi-Objective Optimization Employing Genetic Algorithm for the Torque Converter with Dual-Blade Stator

    Wu, Guangqiang / Wang, Lijun | British Library Conference Proceedings | 2015



    Multi-objective Genetic Algorithm Optimization of a Four-Stage Cascade Vapor Compression Refrigeration System

    Anderson, Kevin R. / McNamara, Christopher / Gross, Thomas J. et al. | AIAA | 2018


    Multi-objective Horizontal Stabilizer Optimization Using Genetic Algorithm

    Sansal, Kaan / Kocak, Goktug / Kargin, Volkan | British Library Conference Proceedings | 2018