As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft compo- nents to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that ac- cording to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be cho- sen. The decision-making model is applied to conceptual design process of the high altitude long endurance （HALE） unmanned aerial vehicle （UAV） based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.
To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.
