Integrated Optimization of Aerodynamic and Electromagnetic Characteristics for Nozzle Central Cone

Abstract

With the development and enhancement of anti-stealth technology and airborne radar, fighter jets will increasingly be threatened by the detection of airborne radar and ground-based radar. The exhaust system is a major radar scattering source in the backward direction of the aircraft due to its large open-ended property. A Radial-Based Function Neural Network Multi-Objective Particle Swarm Optimization (RBFNN-MOPSO) method is presented to reduce the nozzle’s radar cross section (RCS). Considering the geometric constraints and aerodynamic characteristics of the nozzle, 21 nozzle models with different types of cones are established by using uniform design (UD). The electromagnetic scattering characteristics of all the nozzles are simulated by Forward-Backward Iterative Physical Optics (FBIPO) method, and the Computational Fluid Dynamics (CFD) simulations have been conducted to obtain the flow field in the nozzle. After comprehensive evaluation and design by RBFNN-MOPSO, the mean value of RCS and thrust coefficient of the model have achieved good results. The process of optimization and design is proved to be effective and efficient for nozzle aerodynamic/electromagnetic integrated stealth design.