No abstract

In order to improve the fan noise, the multi-objective optimization algorithm was implemented to optimize the fan blade structure. Firstly, the theoretical model with the noise, flow rate and power of fan as the objectives was established and verified; then, the global sensitivity analysis method based on sobol’ method was used to obtain the contributions of each parameter to the performance objectives of the fan by taking the fan blade angle and chord length as the analysis parameters; finally, the sensitive parameters are selected to obtain the best noise-oriented comprehensive performance of the fan by the genetic algorithm. The result shows that the noise is reduced by 3.1dB, the flow rate is increased by 7.7% and the power is reduced by 7.9% after optimizing fan blade structure, and the overall performance of the fan is significantly improved.

Due to the fact that the noise caused by axial fan blades of vehicles is large, which seriously affects ride comfort, and there is no effective mathematical model to quantitatively study the contribution of the various parameters of the blades to the noise, a new method for calculating the load force of the blades is proposed. This method obtains the constant load force of the blade according to the blade element momentum theory and the characteristics of the blade structure of the axial fan for a vehicle. At the same time, this method obtains the non-constant load force of the blade by combining the non-constant thin-wing theory and experimental data and then vectors the constant load force and the non-constant load force to obtain the total load force of the blade to build a mathematical model of the relationship between the noise of the fan and the parameters of the blade. According to the model, the total sound pressure level of a fan is calculated numerically and further compared with the FLUENT software simulation and experimental results. The results show that the error of the total sound pressure level calculated by the numerical value is within 3 dB(A). This method provides an important basis for the study of a high-accuracy noise mathematical model and the optimization of blade parameters of low Mach-number fans.

In this paper ,we consider the problem of CDO pricing in Factor Copula Model with Variance Gamma variables as loading factors.We extend the VG copula model to the case with stochastic correlations, which can effectively model "correlation skews" in CDO pricing problem. Probability generating functions of finite portfolio and loss distributions of large homogeneous portfolios are explicitly derived in the 2-state stochastic correlation VG copula model. Numerical methods of calculating the quantities involving in CDO pricing are also proposed.