In order to obtain the aerodynamic loads of the vibrating blades efficiently, the eXterme Gradient Boosting (XGBoost) algorithm in machine learning was adopted to establish a three-dimensional unsteady aerodynamic force reduction model. First, the database for the unsteady aerodynamic response during the blade vibration was acquired through the numerical simulation of flow field. Then the obtained data set was trained by the XGBoost algorithm to set up the intelligent model of unsteady aerodynamic force for the three-dimensional blade. Afterwards, the aerodynamic load could be gained at any spatial location during blade vibration. To evaluate and verify the reliability of the intelligent model for the blade aerodynamic load, the prediction results of the machine learning model were compared with the results of Computation Fluid Dynamics (CFD). The determination coefficient R2 and the Root Mean Square Error (RMSE) were introduced as the model evaluation indicators. The results show that the prediction results based on the machine learning model are in good agreement with the CFD results, and the calculation efficiency is significantly improved. The results also indicate that the aerodynamic intelligent model based on the machine learning method is worthy of further study in evaluating the blade vibration stability.

The road traffic system is a time-varying, complex nonlinear system. Real-time and accurate road short-term traffic flow prediction is the key to realizing the traffic flow guidance system. In order to improve the prediction accuracy of short-term traffic flow, this paper proposes an algorithm based on the fusion model of differential evolution algorithm (DE) and radial basis function (RBF). This method takes the fitness function as the measurement standard, and uses the DE algorithm to optimize the RBF parameters to obtain the optimal short-term traffic flow prediction value. Through MATLAB simulation experiments, a relatively accurate prediction of the short-term traffic flow of the DE-RBF fusion model is realized. The mean square error (MSE) and the average absolute error percentage of actual and predicted values (MAPE) analysis index are introduced as the evaluation index of the prediction model. After comparing with the two prediction network models of radial basis function (RBF) and wavelet function (WNN), the results show that the DE-RBF fusion model proposed in this paper is effective and feasible for short-term traffic flow prediction.