Use of PIV III — Lesson 4

This lesson covers the Particle Image Velocimetry (PIV) measurement technique, focusing on the cross-correlation algorithm. It explains how images are captured and correlated to obtain the velocity of particles, which is then used to predict the velocity of the fluid. The lesson also discusses the advantages of the cross-correlation algorithm over the auto-correlation algorithm. It further delves into the importance of the seeding particles and the characteristics they should possess for effective PIV analysis. The lesson concludes with a discussion on the role of the lighting system in PIV analysis.

Video Highlights

01:09 - Image capturing and correlation
06:24 - Cross-correlation algorithm
20:26 - Comparison between cross-correlation and auto-correlation algorithms
52:46 - Importance of seeding particles in PIV analysis
57:07 - Role of the lighting system in PIV analysis

Key Takeaways

- The cross-correlation algorithm is advantageous over the auto-correlation algorithm as it provides more robust velocity results, especially in low-density seeding cases.
- Seeding particles should be neutrally buoyant, able to scatter light effectively, non-toxic, non-corrosive, non-abrasive, non-volatile, chemically inactive, and clean.
- The lighting system in PIV analysis should ensure sufficient light intensity for the camera to detect the scattered light from seeding particles.
- The duration of the light pulse and the time between successive light pulses should be controlled to prevent significant movement of particles and the flow field.