Shape Optimization of Stratosphere Airship

PRESENTLY, there is a strong interest in developing unmanned stratospheric airship platforms to be utilized as telecommunication relays, for environmental monitoring or surveillance purposes. Because of the large surface area of the hull, an airship shows high drag, which roughly increases with the square of the airspeed. The required propulsion power is proportional to the third power of the airspeed. Therefore, it is of essential interest to minimize the aerodynamic drag and to maximize the propulsion efficiency. The propulsive power required depends mainly on the aerodynamic drag of the airship hull, which accounts for about 2 of the total drag. Even a small reduction in hull drag can result in a significant saving of fuel, which in turn will lead to a greater payload capacity or an increased range of the airship. During the aerodynamic design of an airship, it is therefore especially important to find a drag-minimized envelope for the intended range of missions. The investigations on the shape optimization of airship were conducted by Th. Lutz et al. 1 and Nejati et al. 2 A source distribution on the body axis was chosen to model the body contour and the corresponding inviscid flowfield, with the source strengths and the lengths of respective segments being used as design variables for the optimization process. Boundary-layer calculation is performed by means of a proved integral method for attached laminar or turbulent boundary layers. To determine the transition location, Lutz used a semi-empirical method based on linear stability theory (e n method), Nejati applied forced transition criterion and concluded that the e n -method is desirable for determining the transition location. A commercial optimizer as well as an evolution strategy with covariance matrix adaption of the mutation distribution were applied as optimization algorithm in Lutz’ paper. Nejati used the genetic algorithm as the optimization algorithm and found that genetic algorithm is a powerful method for such a multidimensional, multimodel, and nonlinear objective function. In this Note, we developed a new method for shape optimization of airship body in terms of the previous investigations of shape optimization. 1−11 The airship geometry is expressed analytically as a polynomial function of eight parameters. The inviscid flow