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Study of the Radar Cross-Section of Turbofan Engine with Biaxial Multirotor Based on Dynamic Scattering Method

Author

Listed:
  • Zeyang Zhou

    (School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China)

  • Jun Huang

    (School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China)

Abstract

With the continuous advancement of rotor dynamic electromagnetic scattering research, the radar cross-section (RCS) of turbofan engines has attracted more and more attention. In order to solve the electromagnetic scattering characteristics of a biaxial multirotor turbofan engine, a dynamic scattering method (DSM) based on dynamic simulation and grid transformation is presented, where the static RCS of the engine and its components is calculated by physical optics and physical theory of diffraction. The results show that the electromagnetic scattering of the engine is periodic when the engine is working stably, while the rotors such as fans and turbines are the main factors affecting the dynamic electromagnetic scattering and the ducts greatly increase the overall RCS level of the engine. The proposed DSM is effective and efficient for studying the dynamic electromagnetic scattering characteristic of the turbofan engine.

Suggested Citation

  • Zeyang Zhou & Jun Huang, 2020. "Study of the Radar Cross-Section of Turbofan Engine with Biaxial Multirotor Based on Dynamic Scattering Method," Energies, MDPI, vol. 13(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5802-:d:440669
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    References listed on IDEAS

    as
    1. Weiyu Lu & Guoping Huang & Xin Xiang & Jinchun Wang & Yuxuan Yang, 2019. "Thermodynamic and Aerodynamic Analysis of an Air-Driven Fan System in Low-Cost High-Bypass-Ratio Turbofan Engine," Energies, MDPI, vol. 12(10), pages 1-17, May.
    2. Krzysztof Sobczak & Damian Obidowski & Piotr Reorowicz & Emil Marchewka, 2020. "Numerical Investigations of the Savonius Turbine with Deformable Blades," Energies, MDPI, vol. 13(14), pages 1-20, July.
    3. Lorenzo Fedele & Luca Di Vito & Fulvio Enzo Ramundo, 2020. "Increasing Efficiency in an Aeronautical Engine through Maintenance Evaluation and Upgrades: Analysis of the Reliability and Performance Improvements under Financial Issues," Energies, MDPI, vol. 13(12), pages 1-17, June.
    4. Qianjing Chen & Jinquan Huang & Muxuan Pan & Feng Lu, 2019. "A Novel Real-Time Mechanism Modeling Approach for Turbofan Engine," Energies, MDPI, vol. 12(19), pages 1-18, October.
    5. Jiakun Qin & Muxuan Pan & Wenhao Xu & Jinquan Huang, 2019. "An Output-Based Limit Protection Strategy for Turbofan Engine Propulsion Control with Output Constraints," Energies, MDPI, vol. 12(21), pages 1-24, October.
    6. Giovanni Aiello & Salvatore Alfonzetti & Santi Agatino Rizzo & Nunzio Salerno, 2017. "Multi-Objective Optimization of Thin-Film Silicon Solar Cells with Metallic and Dielectric Nanoparticles," Energies, MDPI, vol. 10(1), pages 1-10, January.
    7. Ruochi Pan & Zhaoyun Song & Bo Liu, 2020. "Optimization Design and Analysis of Supersonic Tandem Rotor Blades," Energies, MDPI, vol. 13(12), pages 1-15, June.
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    Cited by:

    1. Tadao Ohtani & Yasushi Kanai & Nikolaos V. Kantartzis, 2022. "A Nonstandard Path Integral Model for Curved Surface Analysis," Energies, MDPI, vol. 15(12), pages 1-21, June.

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