Supercritical Airfoil Design Based on SOM Neural Network

A supercritical airfoil design method was brought forwards based on self-organizing feature map(SOM) neural network.The SOM network was used to classify the different airfoils according to their geometry characteristics and aerodynamic characteristics,and then a supercritical airfoil expert database was built.A reference group of airfoils was automatically selected from the expert data base by the well-trained network according to the design requirement.A certainty factor inference method was used to build the relationship between the geometry characteristics and aerodynamic characteristics which gave the basic principles to design the airfoil geometry using gradient descent optimization method.The design results indicate that the SOM network can efficiently classify the airfoils by the same characteristics which can provide the professional guidance to the design work,and the designed airfoil has higher lift-to-drag ratio than the referenced airfoils,so that the design results have better integrated aerodynamic performance.