Optics and Precision Engineering, Volume. 31, Issue 16, 2352(2023)
Research on velocity measurement method of rocket engine tail flame particle based on quantum filter and tracking algorithm
Measuring the ejected particle’s velocity is crucial for rocket motor development design. Because of the intense light background radiation of the rocket motor flame, the conventional filter device and moving object detection algorithm cannot be used. To address these problems, this study presents a novel quantum filter technology. Considering the characteristics of quantum high signal-to-noise ratio and low background noise, taking the atomic filter as the core, the ultra-narrow band quantum filter technology is applied to particle image velocimetry (PIV), which makes up the quantum filter PIV system. The filter bandwidth is on the order of magnitude of MHz to GHz. Simultaneously, based on the quantum filter PIV system, a new virtual particle image tracer algorithm based on image gray cross-correlation is proposed. This algorithm obtains the trajectory of particle motion by tracking and marking to characterize the particle motion in the flow field. The results indicate that the quantum filter technology exhibited strong suppression of complex background interference, the signal-to-noise ratio was improved by 30 dB compared with the conventional filter device, and the filtering effect was significant. The algorithm had high accuracy, the particle velocity measurement error was less than 0.5 m/s, and the calculation measurement accuracy was better than 0.06%. The relevant system had already been used in national research institutes.
Get Citation
Copy Citation Text
Chen GUO, Shengli CHANG, Wenjie ZHANG, Guangyi XIAO, Fei WANG, Tong BAO. Research on velocity measurement method of rocket engine tail flame particle based on quantum filter and tracking algorithm[J]. Optics and Precision Engineering, 2023, 31(16): 2352
Category: Micro/Nano Technology and Fine Mechanics
Received: Nov. 1, 2022
Accepted: --
Published Online: Sep. 5, 2023
The Author Email: CHANG Shengli (slchang@hnu.edu.cn)