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Shamshirband, S., Petković, D., Amini, A., Anuar, N.B., Nikolić, V., Ćojbašić, Ž., et al. (2014) Support Vector Regression Methodology for Wind Turbine Reaction Torque Prediction with Power-Split Hydrostatic Continuous Variable Transmission. Energy, 67, 623-630.
https://doi.org/10.1016/j.energy.2014.01.111

has been cited by the following article:

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

  AUTHORS: Jie Chen, Yifan Qiu

  KEYWORDS: Support Vector Regression, Performance Prediction, Automotive Torque Converter, Computational Fluid Dynamics, Parametric Geometric Model

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.6, June 30, 2025

  ABSTRACT: 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.