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Prasenjit Dey,Ajoy K. Das 한국원자력학회 2016 Nuclear Engineering and Technology Vol.48 No.6
The present study aims to predict the heat transfer characteristics around a square cylinderwith different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by thepresent authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predictthe unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further,the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heattransfer characteristics. It has also been found that MARS is more efficient than artificialneural network and gene expression programming in predicting the forced convectiondata, and also particle swarm optimization can efficiently optimize the heat transfer rate.