The Coupling Mechanism Effect Of Layer Parameters On The Wind Turbine Blade’s Performance

With the increasing tension of petrochemical energy,wind energy as an alternative energy is becoming more and more attention and attention as the core components of wind turbines,the performance of the blade directly related to wind turbine capture efficiency,safe and stable operation and service life.The performance of the blade is different with different stacking parameters,and there is coupling effect of stacking parameter on the performance of the blade.Therefore,the paper studies the coupling effect of stacking parameters on the performance of wind turbine blades,and probes into its coupling mechanism,in order to obtain an optimized stacking parameter combination scheme.The main work of the thesis is as follows:The uniform test design method,GA-RBFNN(genetic algorithm radial basis function neural network model),the structural form and mechanical properties analysis method of composite fiber laminate and the structure of the blade are discussed,and the numerical analysis model of the blade is established.Taking stacking angle,the ±x° stacking thickness ratio and the stacking sequence as independent variables,taking the Tsai-wu failure factor and Mises stress as the index to measure the blade strength,taking the maximum displacement as the index to measure the blade stiffness,the coupling effect of the stacking parameters on the blade performance of the wind turbine is studied based on the ±x° thickness ratio,plying angel and the stacking sequence.The uniform test design method is used to design the test scheme of laying parameters,and the static strength and stiffness of the blade are analyzed,and the optimum value range of stacking parameters is obtained by means of single factor mean method and interactive term analysis.Based on the polynomial regression analysis method,the quadratic mathematical model between the stacking parameters and the blade performance index was established,and the regression equation and coefficient were tested by variance analysis.The influence of the parameters of pair wises tacking on the blade performance was analyzed.In order to obtain the optimum value of stacking parameters,multiple mixed level uniform design method to refine stacking parameters,to draw up the test plan,analysis of the static strength and stiffness of the blade;according to the simulation results,a radial basis neural network model with predictive capability is constructed,and the reliability of network model is validated by calculating network predictive value and actual analysis value error,mean square variance and root mean square value.In this paper,the genetic algorithm is fused in the radial basis neural network which is trained and optimized,radial basis neural network predictive value as input of genetic algorithm,the values and combinations of the optimal stacking parameters are obtained by optimizing the layup angle,the ±x° stacking thickness ratio and the stacking sequence.The example shows that the performance of the optimized blade is improved,and the feasibility and effectiveness of the method are validated.The research results provide a reference for the structural stacking design and optimization of large composite fiber wind turbine blades.