Data Mining-Based Design Space Exploration and Optimization for Tandem Airfoils

Abstract

The joined-wing configuration has great technical appeal for the development of next-generation SensorCraft. Research based on the simplified tandem airfoil system can improve understanding of the joined-wing configuration’s aerodynamic characteristics. We combine the adjoint-based aerodynamic shape optimization and self-organizing map- (SOM-) based data mining technology to reveal the flow interactions of tandem airfoils and aerodynamic characteristics from the perspective of the entire aerodynamic design space. The SOM is used to explore the correlation between relative position parameters and aerodynamic force coefficients of tandem airfoil systems. Results show that the drag coefficient at the defined range of lift coefficients has obviously positive linear correlation and greatly dependents on the value of decalage. The tandem airfoils with negative decalage around -2.7° have the smallest drag coefficients. Due to variations in the aerodynamic interaction strength, the drag coefficient of each airfoil changes from a linear law to a nonlinear law as airfoils approach each other. We then perform single-point aerodynamic shape optimization based on two sets of relative position parameters with different aerodynamic interaction strengths, and 1.8% and 1.28% drag reductions are obtained, respectively. Based on optimized airfoils, the SOM is used to reveal the distribution of drag variation in the design space constructed by relative position parameters. Results illustrate that the aerodynamic interference strength between the front and rear airfoils significantly affects the drag reduction mechanism, which results in the different distribution patterns of drag variation in design space.