Due to the complexity of influence factors in the nanoparticles adsorption method and the limitation of data samples, the support vector machine （SVM） was used in the prediction method for the drag reduction effect....Due to the complexity of influence factors in the nanoparticles adsorption method and the limitation of data samples, the support vector machine （SVM） was used in the prediction method for the drag reduction effect. The basic concept of SVM was introduced, and the e - SVR programming for the kemel function on the radial basis was established firstly with the help of the MATLAB software. Then, an analysis was made for the influencing factors of the drag reduction effect in nanoparticles adsorption. Finally, a prediction model for the drag reduction effect of nanoparticles was established, and the accuracy of training sample and prediction sample was analyzed. The result shows that the SVM has good availability and can be used as a rapid evaluation method of the drag reduction effect prediction of nanoparticles adsorption method.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.50874071)the Chinese National Programs for High Technology Research and Development(Grant No.SS2013AA061104)+1 种基金Shanghai Program for Innovative Research Team in Universities,Shanghai Leading Academic Discipline Project(Grant No.S30106)the Shanghai Leading Talents Project and the Key Program of Science and Technology Commission of Shanghai Municipality(Grant No.12160500200)
文摘Due to the complexity of influence factors in the nanoparticles adsorption method and the limitation of data samples, the support vector machine （SVM） was used in the prediction method for the drag reduction effect. The basic concept of SVM was introduced, and the e - SVR programming for the kemel function on the radial basis was established firstly with the help of the MATLAB software. Then, an analysis was made for the influencing factors of the drag reduction effect in nanoparticles adsorption. Finally, a prediction model for the drag reduction effect of nanoparticles was established, and the accuracy of training sample and prediction sample was analyzed. The result shows that the SVM has good availability and can be used as a rapid evaluation method of the drag reduction effect prediction of nanoparticles adsorption method.
