Application of support vector regression cooperated with modified artificial fish swarm algorithm for wind tunnel performance prediction of automotive radiators

Abstract

As an important part of automotive thermal management system, finned-tube heat exchangers (FTHEs) are widely used as automotive radiators because of the compact configuration and strong heat transfer capacity. Wind tunnel test is a usual method to investigate the characteristics of heat exchangers, however, its application is limited due to the high cost. Therefore, the authors try to use intelligent algorithm to predict the performance of heat exchangers with different geometrical parameters to decrease the number of wind tunnel tests in this paper. Firstly, the authors choose representative FTHEs with different geometrical parameters, including core width, core height, core thickness, tube depth, fin height and fin pitch, of which the heat transfer capacity are tested on wind tunnel experimental setup. Then the database is established using the obtained experimental data. After data processing, the support vector regression (SVR) model to predict the heat transfer capacity of heat exchangers is established on MATLAB software platform. A modified artificial fish swarm algorithm (MAFSA) of which the effectiveness has been verified by a representative multimodal function is used to improve the accuracy of the SVR model. Thus, the MAFSA-SVR model to predict the heat transfer capacity is established. The root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the MAFSA-SVR model is respectively 1.89 and 1.83%, both of which are better than the SVR model, the Back propagation neural networks (BPNN) model as well as the linear regression (LR) model. It is concluded that there are high accuracy and generalization of the MAFSA-SVR prediction model, by which the heat transfer capacity of a specific heat exchanger can be predicted with a small number wind tunnel tests to reduce exhausting and expensive experimental studies.