Extracting quantitative three-dimensional unsteady flow direction from tuft flow visualizations
Noriyasu Omata, Susumu Shirayama · Fluid Dynamics Research · 2017
Abstract We focus on the qualitative but widely used method of tuft flow visualization, and propose a method for quantifying it using information technology. By applying stereo image processing and computer vision, the three-dimensional (3D) flow direction in a real environment can be obtained quantitatively. In addition, we show that the flow can be divided temporally by performing appropriate machine learning on the data. Acquisition of flow information in real environments is important for design development, but it is generally considered difficult to apply simulations or quantitative experiments to such environments. Hence, qualitative methods including the tuft method are still in use today. Although attempts have been made previously to quantify such methods, it has not been possible to acquire 3D information. Furthermore, even if quantitative data could be acquired, analysis was often performed empirically or qualitatively. In contrast, we show that our method can acquire 3D information and analyze the measured data quantitatively.