Radar Cross Section Reduction and Shape Optimization using Adjoint Method and Automatic Differentiation - Ming Li; Junqiang Bai; Feng Qu , (Applied Computational Electromagnetics Society Journal (ACES), 2021)
An efficient Radar Cross Section (RCS) gradient evaluation method based on the adjoint method is presented. The Method of Moments is employed to solve the Combined Field Integral Equation (CFIE) and the corresponding derivatives computing routines are generated by the program transformation Automatic Differentiation (AD) technique. The differential code is developed using three kinds of AD mode: tangent mode, multidirectional tangent mode, and adjoint mode. The differential code in adjoint mode is modified and optimized by changing the “two-sweeps” architecture into the “inner-loop two-sweeps” architecture. Their efficiency and memory consumption are tested and the differential code using modified adjoint mode demonstrates the great advantages in both efficiency and memory consumption. A gradient-based shape optimization design method is established using the adjoint method and the mechanism of RCS reduction is studied. The results show that the sharp leading can avoid the specular back-scattering and the undulations of the surface could change the phases which result in a further RCS reduction.