Robust Aerodynamic Design of Mars Exploratory Airplane Wing: With a New Optimization Method

The use of airplanes for Mars exploration is a new and attractive approach because it provides high resolution power and large spatial coverage. However, it is also a challenging approach in engineering viewpoint. Mars airplanes are required to fly in lower Reynolds number and higher subsonic Mach number conditions due to thinner atmosphere and smaller speed of sound on the Mars, compared to typical commercial Earth airplanes. Some studies of Mars exploratory airplanes have been already reported by many researchers. However, these airplanes were designed only by utilizing and modifying existing design approaches for conventional Earth airplanes. Therefore, the use of a design optimization approach is desirable to realize more effective and global search for better design of Mars airplane and establish a new design concept for Mars airplanes. In addition, it is well known that there exist large wind variations on the Mars. Such wind variations may lead to drastic deterioration in performance, and thus failure in expected Mars exploratory mission. Therefore, it is desirable to consider not only the performance at design point but also robustness of performance against wind variations for more realistic and reliable design of Mars airplane. One of solutions to realize such design is use of a robust design optimization approach. However, traditional robust optimization approaches had lack of capability to reveal trade-off information between optimality and robustness which is useful for real-world robust designs. In this dissertation, a new robust design optimization approach “design for multiobjective six sigma (DFMOSS)” has been developed to solve the drawbacks of a conventional robust optimization approach “design for six sigma (DFSS)” for more efficient and more useful robust design optimizations, and applied to simple robust optimization problems to investigate efficiency and usefulness of the DFMOSS. This study showed that the DFMOSS has some advantages over the DFSS. First, the DFMOSS does not require the advance specification of input parameters such as weighting factors and sigma level. Second, the DFMOSS obtains multiple robust