Application of Multivariate Adaptive Regression Spline-Assisted Objective Function on Optimization of Heat Transfer Rate Around a Cylinder

• Main Authors: Prasenjit Dey, Ajoy K. Das
• Format: Article
• Language: English
• Published: Elsevier 2016-12-01
• Series: Nuclear Engineering and Technology
• Subjects: Multivariate Adaptive Regression Splines; Optimized Heat Transfer Rate; Particle Swarm Optimization; Rounded Cornered Square Cylinder
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573316300985

The present study aims to predict the heat transfer characteristics around a square cylinder with 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 the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the 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 heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.