@inproceedings{85dbc3f83ae94f40827977d625be3b78,
title = "An Adjoint-based Shape Optimization Design System for the Radar Cross Section Reduction",
abstract = "An efficient shape optimization design system for the Radar Cross Section (RCS) reduction using the adjoint approach is discussed. The Method of Moments (MoM) and the Multilevel Fast Multipole Algorithm (MLFMA) are applied to solve the Combined Field Integral Equation (CFIE) or the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) equation to calculate the RCS. As for the high-frequency scattering problem, the Physical Optics (PO) method is selected to calculate the RCS due to its efficiency. The gradient of the RCS is obtained by solving the adjoint equations. The code that calculates the derivatives is developed with the help of the Automatic Differentiation (AD) technique. The Free-Form Deformation (FFD) approach is used to parameterize the shape and the Sequential Quadratic Programming (SQP) is chosen as the optimization algorithm. Without a large number of function evaluations, this design system could efficiently obtain the shape that meets the stealth requirements.",
keywords = "Adjoint method, Automatic differentiation, Method of Moments, Multilevel fast multipole algorithm, Physical optics",
author = "Ming Li and Chaoyu Liu and Feng Qu and Junqiang Bai",
note = "Publisher Copyright: {\textcopyright} 2021 Applied Computational Electromagnetics Society (ACES).; 4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021 ; Conference date: 28-07-2021 Through 31-07-2021",
year = "2021",
month = jul,
day = "28",
doi = "10.23919/ACES-China52398.2021.9581674",
language = "英语",
series = "2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings",
}