Integrated Global Wing and Local Panel Optimization of Aircraft Wing

Abstract

The structural optimization of a cantilever aircraft wing with curvilinear spars and ribs and stiffeners is described. The design concept of reinforcing the wing structure using curvilinear stiffening members has been explored due to the development of novel manufacturing technologies like electron-beam-free-form-fabrication (EBF 3 ). For the optimization of a complex wing, a common strategy is to divide the optimization procedure into two subsystems: the global wing optimization which optimizes the geometry of spars, ribs and wing skins; and the local panel optimization which optimizes the design variables of local panels bordered by spars and ribs. The stiffeners are placed on the local panels to increase the stiffness and buckling resistance. The panel thickness, size and shape of stiffeners are optimized to minimize the structural weight. The geometry of spars and ribs greatly influences the design of stiffened panels. The interaction between the global wing optimization and the local panel optimization is usually computationally expensive. An approximate approach is implemented for the stiffened panel optimization to obtain approximate optimal panels using the polynomial curve fitting of a series of optimized panel thicknesses, so as to reduce the computational cost. The aircraft design is characterized by multiple disciplines: structures, aeroelasticity and buckling. Particle swarm optimization is used in the integration of global/local optimization to optimize the SpaRibs. A parallel computing technology has been developed in Python programming to reduce the CPU time. The license cycle-check method and memory self-adjustment method are two approaches that have been applied in the parallel framework in order to optimize the use of the resources by reducing the license and memory limitations and making the code robust. The integrated global-local optimization approach has been applied to subsonic NASA common research model (CRM) wing, which proves the methodology’s application scaling with medium fidelity FEM analysis. Both the global wing design variables and local panel design variables are optimized to minimize the wing weight at an acceptable computational cost. The structural weight of the wing has been, therefore, reduced by 40% and the parallel implementation allowed a reduction in the CPU time by 89%.