Regression model of rotor shape errors based on the ISSA-BP neural network

Yu, Hechun; Fan, Guozhen; Zhang, Guoqing; Wang, Wenbo; Li, Youhua; Zhang, Suxiang; Li, Bin

doi:10.1007/s12206-024-0325-4

J Mech Sci Technol

2024

DOI: 10.1007/s12206-024-0325-4

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Regression model of rotor shape errors based on the ISSA-BP neural network

Hechun Yu,

Guozhen Fan,

Guoqing Zhang

et al.

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Cited by 1 publication

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Research on the aerostatic rotary table for micro-gravity test based on micro-orifice throttling

Yu,

Kou,

Hou

et al. 2024

J. meas. eng.

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This paper addresses the demand for high-precision micro-gravity simulation experiments on large spacecraft and presents the design of a single-axis aerostatic rotary table with a high load capacity and low disturbance torque. Firstly, the structure of the aerostatic rotary table is analyzed, and the physical and mathematical models of the aerostatic pressure thrust bearing are established. Computational fluid dynamics methods are then employed to investigate the impact of three different aperture throttle orifices on the bearing performance. Through extensive research on the flow field of the bearing air film, it is discovered that changes in the aperture of the throttle orifices directly influence the pressure distribution, turbulence intensity, and gas vortex generation, thereby affecting the performance and stability of the bearing. Based on the calculation results, a single-axis aerostatic rotary table with a throttle orifice diameter of 0.1 mm is manufactured, and tests are conducted to measure its disturbance torque and load capacity. The test results demonstrate that the disturbance torque of the aerostatic rotary table is less than 9.2×10-5 N·m, and the rated load capacity exceeds 1000 kg. Finally, the research findings offer a theoretical foundation and data support for the design and development of aerostatic rotary table with high load capacity and low disturbance torque. This advancement paves the way for a superior solution in conducting precise micro-gravity simulation tests on large spacecraft.

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Research on the aerostatic rotary table for micro-gravity test based on micro-orifice throttling

Yu,

Kou,

Hou

et al. 2024

J. meas. eng.

View full text Add to dashboard Cite

show abstract

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Regression model of rotor shape errors based on the ISSA-BP neural network

Cited by 1 publication

References 20 publications

Research on the aerostatic rotary table for micro-gravity test based on micro-orifice throttling

Research on the aerostatic rotary table for micro-gravity test based on micro-orifice throttling

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