Experimental velocity data estimation for imperfect particle images using machine learning

Understand

We propose a method using supervised machine learning to estimate velocity fields from particle images having missing regions due to experimental limitations.

• As a first example, a velocity field around a square cylinder at Reynolds number of ${\rm Re}_D=300$ is considered.
• To train machine learning models, we utilize artificial particle images (APIs) as the input data, which mimic the images of the particle image velocimetry (PIV).
• The output data are the velocity fields, and the correct answers for them are given by a direct numerical simulation (DNS).