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Identification methods of charged particles based on aero-engine exhaust gas electrostatic sensor array.
Guo, Jiachen; Zhong, Zhirong; Jiang, Heng; Zuo, Hongfu.
Afiliação
  • Guo J; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Zhong Z; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Jiang H; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Zuo H; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Sci Prog ; 104(2): 368504211023691, 2021.
Article em En | MEDLINE | ID: mdl-34100331
This paper presents a study of aero-engine exhaust gas electrostatic sensor array to estimate the spatial position, charge amount and velocity of charged particle. Firstly, this study establishes a mathematical model to analyze the inducing characteristics and obtain the spatial sensitivity distribution of sensor array. Then, Tikhonov regularization and compressed sensing are used to estimate the spatial position and charge amount of particle based on the obtained sensitivity distribution; cross-correlation algorithm is used to determine particle's velocity. An oil calibration test rig is established to verify the proposed methods. Thirteen spatial positions are selected as the test points. The estimation errors of spatial positions and charge amounts are both within 5% when the particles are locating at central area. The errors are higher when the particles are closer to the wall and may exceed 10%. The estimation errors of velocities by using cross-correlation are all within 2%. An air-gun test rig is further established to simulate the high velocity condition and distinguish different kinds of particles such as metal particles and non-metal particles.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article