Investigation of cloud droplets velocity extraction based on depth expansion and self-fusion of reconstructed hologram
Investigation of cloud droplets velocity extraction based on depth expansion and self-fusion of reconstructed hologram
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基于深度扩展和重构全息图自融合的云滴速度提取研究
DOI：
10.1364/oe.458947
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Dengxin Hua
中科院分区：
文献类型：
--
作者：
Pan Gao;Jun Wang;Jiabin Tang;Yangzi Gao;Jingjing Liu;Qing Yan;Dengxin Hua
云滴速度对湍流-云微物理相互作用机制的研究有重要影响。提出了一种基于深度扩展和自融合算法的同轴数字全息干涉测量技术，可以同时从8幅全息图中提取粒子速度。与双帧曝光法相比，速度提取效率提高了3倍，用于粒子配准的参考粒子数增加到8个。在云室中的实验结果表明，从稳定阶段到耗散阶段，云滴的速度增加了四倍。两相测量误差分别为1.138和1.153 mm/S。此外，该方法为湍流堆积和云滴碰撞的三维粒子测速研究提供了一种快速的解决方案。
The velocity of cloud droplets has a significant effect on the investigation of the turbulence-cloud microphysics interaction mechanism. The paper proposes an in-line digital holographic interferometry (DHI) technique based on depth expansion and self-fusion algorithm to simultaneously extract particle velocity from eight holograms. In comparison to the two-frame exposure method, the extraction efficiency of velocity is raised by threefold, and the number of reference particles used for particle registration is increased to eight. The experimental results obtained in the cloud chamber show that the velocity of cloud droplets increases fourfold from the stabilization phase to the dissipation phase. The measurement deviations of two phases are 1.138 and 1.153 mm/s, respectively. Additionally, this method provides a rapid solution for three-dimensional particle velocimetry investigation of turbulent field stacking and cloud droplets collisions.