Changmin Oh

Mechanical engineering

Hometown: Gwangju, Gwangju, South Korea

Graduation date: Spring 2026

Additional details: Transfer student

FURI | Summer 2025

Visualizing Fluid Dynamics of Flow Around Pin Fins using Particle Image Velocimetry

This research investigates how to improve the accuracy of Particle Image Velocimetry (PIV) for visualizing flow behavior in pin fin arrays under low Reynolds number conditions (Re ≈ 475). Understanding these flows is important for optimizing the performance of thermal-fluid systems in aerospace, electronics cooling, and microfluidics.

The study explores the effects of pulse interval, interrogation area size, seeding concentration, and imaging mode on the accuracy of velocity vector fields. Optimizing pulse interval, interrogation area, and seeding concentration is crucial for accurate PIV measurements. The pulse interval must match the flow velocity to ensure adequate particle displacement without causing loss of correlation. The interrogation area affects spatial resolution and the ability to capture velocity gradients. Proper seeding concentration ensures enough tracer particles for reliable cross-correlation, while avoiding particle overlap or image noise. Together, these parameters directly impact the precision and reliability of velocity field measurements.

Experiments are conducted at a flow velocity of approximately 0.0137 m/s using pulse intervals of 2000–8000 μs and multiple IA configurations. Preliminary results suggest that a 16-pixel grid step with 32×32 to 96×96-pixel IA yields smooth, stable vector fields suitable for subpixel displacement tracking. When the pulse interval and interrogation area were too large, fine flow structures became smeared and less defined. Conversely, when these parameters were too small, the resulting velocity fields showed significant noise and weak correlation due to poor signal quality. Balanced settings produced smooth and stable vector fields suitable for subpixel displacement tracking. These findings enhance experimental reliability and support data-driven improvements in thermal-fluid system design and analysis.

Mentor: Beomjin Kwon