Radar Cross Section Reduction and Shape Optimization using Adjoint Method and Automatic Differentiation

Authors

  • Ming Li School of Aeronautics Northwestern Polytechnical University, Xi’an, 710072, China
  • Junqiang Bai School of Aeronautics Northwestern Polytechnical University, Xi’an, 710072, China
  • Feng Qu School of Aeronautics Northwestern Polytechnical University, Xi’an, 710072, China

Keywords:

Adjoint method, automatic differentiation, method of moments, sensitivity, shape optimization

Abstract

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.

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Author Biographies

Ming Li, School of Aeronautics Northwestern Polytechnical University, Xi’an, 710072, China

Ming Li was born in Liuzhou, Guangxi, China, in 1992. He received the B.S. degree in Aircraft Design and Engineering from Beijing Institute of Technology, Beijing, in 2014, and the M.S. degree in Aircraft Design from Northwestern Polytechnical University, Xi’an, in 2017, where he is currently pursuing the Ph.D. degree in Aircraft Design with the School of Aeronautics. His current research interests include computational electromagnetics, aerodynamics, and multidisciplinary optimization design of flight vehicles.

Junqiang Bai, School of Aeronautics Northwestern Polytechnical University, Xi’an, 710072, China

Junqiang Bai was born in Xinxiang, Hernan, China, in 1971. He received the B.S. in Aerodynamics from the National University of Defense Technology, Changsha, in 1991, the M.S. degree in Aerodynamics from Northwestern Polytechnical University, Xi’an, in 1994, and the Ph.D. degree in Aircraft Design from Northwestern Polytechnical University, Xi’an, in 1999. From 1999 to 2004, he was an Associate Professor with the Department of Aircraft Design Engineering, Northwestern Polytechnical University, Xi’an. He was a Visiting Scholar with the Institute of Aerodynamic and Flow Technology, German Aerospace Center (DLR), Braunschweig, Germany, in 2006. Since 2004, he has been a Professor with the Department of Aircraft Design Engineering, Northwestern Polytechnical University, Xi’an. Since 2017, he has been a Deputy Dean with the Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China. He has authored or co-authored over 100 papers and he holds or has applied for 5 Chinese patents. His current research interests include the conceptual design of aircraft, the aircraft aerodynamic shape design, and the aircraft multidisciplinary optimization.

Feng Qu , School of Aeronautics Northwestern Polytechnical University, Xi’an, 710072, China

Feng Qu was born in Pizhou, Jiangsu, China, in 1988. He received the B.Eng. degree in Mathematics and Applied Mathematics from China University of Petroleum, Beijing, in 2010, and the Ph.D. degree in Fluid Mechanics from Beihang University, Beijing, in 2015. From 2015 to 2017, he was an Engineer with the Institute of Manned Space System Engineering, China. Since 2018, he has been an Associate Professor with the Northwestern Polytechnical University, Xi’an, and he is currently a Director of fluid mechanics in National Natural Science Foundation of China. His current research interests involve the flux schemes for all speeds, the high-order schemes, and the turbulence modeling. He is authoring a structured CFD software which is capable of simulating configures of all speeds in both laminar flow and turbulent flow.

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Published

2021-03-08

How to Cite

[1]
Ming Li, Junqiang Bai, and Feng Qu, “Radar Cross Section Reduction and Shape Optimization using Adjoint Method and Automatic Differentiation”, ACES Journal, vol. 36, no. 3, pp. 320–335, Mar. 2021.

Issue

2021: Vol 36 Iss 3

Section

Articles