基于数据挖掘的翼型气动隐身多学科分析

In the multidisciplinary design of aerodynamic stealth for airfoil profiles，the diversity and coupling relationships among objectives and variables increased the computational cost and development cycle of the optimization design. Focusing on data mining using four types of algorithms: random forest，adaptive boosting algorithm，self-organizing maps and isometric mapping，the data mining considered six objectives: aerodynamic lift coefficient，drag coefficient，pitching moment coefficient，and lift-to-drag ratio，as well as vertical polarized radar cross-section and horizontal polarized radar cross-section. Result showed that， in the analysis of objectives and design variables， the aerodynamic and stealth performance of the airfoil profiles was greatly influenced by the curvature of the leading and trailing edges， followed by the chord length. Larger curvature of the leading edge reduced the drag and improved the stealth performance but increased the pitching moment coefficient. Smaller curvature of the trailing edge improved the lift coefficient， lift-to-drag ratio， and stealth performance while reducing the pitching moment coefficient. Through data mining， specific reference ranges for design variables could be provided to obtain airfoil profiles with superior aerodynamic stealth performance.