Prediction of film-cooling effectiveness based on support vector machine

作     者：Wang, Chunhua Zhang, Jingzhou Zhou, Junhui Alting, Siti Aisyah 

作者机构：Nanjing Univ Aeronaut & Astronaut Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China Collaborat Innovat Ctr Adv Aeroengine Beijing 100191 Peoples R China 

出 版 物：《APPLIED THERMAL ENGINEERING》 (实用热力工程)

年 卷 期：2015年第84卷

页      面：82-93页

核心收录：

学科分类：0820[工学-石油与天然气工程] 080702[工学-热能工程] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：National Natural Science Fund Program of China 

主　　题：Turbine Film-cooling effectiveness Support vector machine Chaos optimization algorithm 

摘      要：Least square support vector machine (LS-SVM) model is applied to predict the lateral averaged adiabatic film-cooling effectiveness on a flat plate surface downstream of a row of cylindrical holes. The dataset used to develop and validate the presented model is obtained from the public literature. The input parameters of LS-SVM include dimensionless downstream distance, pitch-to-diameter ratio, hole incline angle, hole compound angle, length-to-diameter ratio, blowing ratio, density ratio, and mainstream turbulence intensity. The predicted results are found to be in good agreement with the experimental results (the mean relative error is about 17.5%). The comparison between LS-SVM model and existing semi-empirical correlations is carried out, and the prediction performance of LS-SVM model is much better. Moreover, the effects of LS-SVM input parameters on film-cooling effectiveness are discussed in detail. LS-SVM is a promising model to predict the film-cooling effectiveness. (C) 2015 Elsevier Ltd. All rights reserved.