Aerodynamic Optimization of Axial Fan Blade Airfoil based on Multiple Optimization Methods
In order to solve the problem of optimizing the design of axial fan blade airfoil W65,Gaussian process regression,artificial neural network and sequential quadratic programming were selected to opti-mize the blade airfoil.Firstly,the class function/shape function transformation(CST)method was used to represent the blade airfoil and generate a sample set of blade airfoils within a certain interval.B-spline curves were used for smoothing treatment,and the smooth blade airfoil lift-drag ratio data were obtained through computational fluid dynamics(CFD)simulation.Then,the Gaussian process regression,artifi-cial neural network and sequential quadratic programming methods were respectively used to optimize the objective function of multi angle of attack lift-drag ratios with area constraints.The first two optimization methods were combined with genetic algorithm and gradient descent method,while the sequential quadratic programming method did not combine other optimization methods.Under the working condition of the angle of attack varying from 0° to 8°,the Mach number was 0.5.The optimized blade airfoil was validated using CFD method.The results show that the comprehensive lift-drag ratio of the optimized blade airfoil obtained through three methods increase by 8.41%,8.49%and 2.08%,respectively.The estimated relative errors in the optimization methods are 0.25%,-0.39%,and 6.31%,respectively.The Gaussian process re-gression method and artificial neural network method have smaller optimization errors,while the sequential quadratic programming method has larger optimization errors.
airfoil optimizationmachine learningGaussian process regressionartificial neural net-worksequential quadratic programming