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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 ε- SVR programming for the kernel 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.
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 ε- SVR programming for the kernel function on the radial basis was established 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 the results 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.