Data Mining for Aerodynamic Design Space

TLDR

Data mining techniques such as ANOVA and SOM are used to analyze aerodynamic design spaces, providing quantitative interaction effects and qualitative trade‑off visualizations among multiple objective functions. The authors apply ANOVA and SOM to identify variable effects, using them on a fly‑back booster with four objectives and 71 variables and on a transonic airfoil optimized via an adaptive search region method. This information helps designers select final designs from non‑dominated solutions of multi‑objective problems.

Abstract

Analysis of variance (ANOVA) and self-organizing map (SOM) were applied to data mining for aerodynamic design space. These methods make it possible to identify the effect of each design variable on objective functions. ANOVA shows the information quantitatively, while SOM shows it qualitatively. Furthermore, ANOVA can show the effects of interaction between design variables on objective functions and SOM can visualize the trade-offs among objective functions. This information will be helpful for designers to determine the final design from non-dominated solutions of multi-objective problems. These methods were applied to two design results: a fly-back booster in reusable launch vehicle design, which has 4 objective functions and 71 design variables, and a transonic airfoil design performed with the adaptive search region method.