Particle Image Velocimetry (PIV)

Microvec AI PIV

Artificial Intelligence (AI) PIV is the newest unique innovation from Microvec. With the advances of artificial intelligence and ever wider adoption of deep learning techniques based on Convolutional Neural Network (CNN), common in the computer and machine vision worlds in recent years, PIV methodology based on deep learning has turned out to be very accurate. Microvec’ AI PIV is based on the optical flow neural network which has already proven itself in the computer vision community. It has been successfully applied to fluid mechanics and particle image velocimetry, where global and quantitative velocity field from images can be extracted with improved computational efficiency without reduction of accuracy.

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Technical Specifications

Parameter	Typical Range
Method	Artificial Intelligence (AI) / Deep Learning PIV
Neural Network	Optical flow neural network (CNN-based)
Training Dataset	13,000+ items (256×256 pixels, CFD-generated)
Max Vector Density	Up to one vector per pixel
Calculation Speed	Near real-time (GPU-accelerated)

Key Specifications

•AI-based PIV using convolutional neural network (CNN) optical flow model
•Training dataset: 13,000+ CFD-generated particle images at 256x256 pixels
•Dense velocity field up to one vector per pixel
•GPU-accelerated — near real-time calculation speed
•Accuracy one order of magnitude higher than cross-correlation at single-pixel level

Key Features

•No interrogation window size setting required — easier to use than traditional PIV
•Dense velocity field without outlier detection or velocity filtering steps
•Automatic and accurate restoration of the flow field
•Captures small-scale flow structures invisible to cross-correlation methods
•Faster calculation time when using GPU vs. traditional PIV
•Applicable to jet field, boundary layer flow, shock in hypersonic flows, micro flow channels

Software & Integration

•MicroVec AI PIV Software

Applications

•Jet flow velocity measurement
•Boundary layer analysis
•Hypersonic shock flow
•Micro channel flow
•Stagnant flow analysis requiring single-pixel accuracy

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