Support Vector Regression Methodology for Performance Prediction of an Automotive Torque Converter with Turbine Parametric Model

Support vector regression (SVR) and computational fluid dynamics (CFD) techniques are applied to predict the performance of an automotive torque converter in the design process of turbine geometry. A new parametric geometric model of turbine is proposed by means of parametric equations and Creo software to improve the design efficiency. The validity of the parametric design method and the accurateness of numerical analysis are verified by comparing simulation results with experimental data. Orthogonal design and latin hypercube design (LHD) methods are used to obtain the train data and test data, respectively, for SVR. To build an effective SVR model, the SVR parameters are optimized employing cross-validation and grid search methods. Polynomial and radial basis function (RBF) are applied as the kernel function of SVR for predicting converter performance characteristics, including stall torque ratio and peak efficiency. Instead of minimizing the observed training error, SVR with polynomial kernel and SVR with RBF kernel attempt to minimize the generalization error bound so as to achieve generalized performance. The results show that SVR methodology can serve as an effective approach to predict the performance of torque converters.