Abstract
Due to the complex flow characteristics such as strong nonlinearity and strong non-equilibrium of turbulence, there are significant errors in using the initial velocity field generated based on cross-correlation algorithm in mixed particle image velocimetry methods. This can result in inaccurate offset images generated, leading to errors in generating the final fine velocity field using optical flow method. This article adds a window function to weight the pixels in the window before operating based on the cross-correlation algorithm, and generates a velocity field through the cross-correlation algorithm. It is then compared and analyzed with the cross-correlation algorithm without adding a window function and the mixed particle image velocity measurement method before and after adding a window function. Subsequently, uniform flow field particle images are used for testing to verify the effectiveness of the method proposed in this paper. The results confirm that the method proposed in this article has a significant effect on turbulent particle images, and is always more accurate in generating the initial velocity field of turbulence than based on cross-correlation algorithms. It can provide a more accurate initial velocity field in mixed particle image velocity measurement methods.
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Acknowledgement
This work is supported by the Natural Science Foundation of Jilin Province of China (Grant Nos. 20230101318JC, 20230101319JC).
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Guo, S., Gao, M., Xiao, B., Xie, Z., Ping, W. (2024). Application of a Hybrid Particle Image Velocimetry Method Based on Window Function in the Field of Turbulence. In: Huang, DS., Chen, W., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14869. Springer, Singapore. https://doi.org/10.1007/978-981-97-5603-2_6
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DOI: https://doi.org/10.1007/978-981-97-5603-2_6
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