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Measurement of velocity fields is a fundamental topic in fluid dynamics. Image-based analysis methods such as Particle Image Velocimetry or Laser Doppler Velocimetry are usually used. However, these techniques need complex instrumentation and particular test conditions. In this work, a computer vision-based approach is developed in order to obtain vapour velocity field map in effective, robust and economic way. Moreover, iterative filtering algorithm is applied to improve the results. The implemented method is tested on a suction system for domestic use, and the obtained velocity maps are validated by hot-wire anemometry, leading to totally comparable results, both in terms of profile and mean velocity. Uncertainty analysis shows acceptable results, considering the random nature of the phenomenon.

Suction system vapour velocity map estimation through SIFT-based alghoritm