Experimental Fluid Mechanics · Tel Aviv University

Understanding turbulent flows, one particle at a time.

The Turbulence Structure Laboratory develops and applies three-dimensional particle tracking velocimetry, Lagrangian flow analysis, and physics-informed machine learning to understand how turbulent flows organize, mix, and transport energy — from stratified natural flows to real-time experimental diagnostics. We also develop novel microscopic flow sensors using MEMS technology, extending our measurements down to the smallest scales.

About the lab

Measuring flows to understand how they work

To understand a turbulent flow, you have to measure it. Our lab develops and applies quantitative flow-diagnostic techniques — including particle image velocimetry (PIV) and three-dimensional particle tracking velocimetry (3D-PTV) — to resolve the dynamics and structure of turbulent flows, fluid–structure interactions, complex fluid behavior, and the stresses flows exert on objects and surfaces.

We are committed to advancing fluid mechanics through experimental research and open science — building a collaborative environment where advanced technology and interdisciplinary approaches drive turbulence studies, sharing our discoveries with the global scientific community, and training the next generation of engineers.

We combine multi-camera imaging, custom reconstruction algorithms, and — increasingly — physics-informed machine learning to push these measurements toward real time, and we maintain OpenPIV and OpenPTV, open-source tools used by the experimental fluid mechanics community worldwide.

Focus
3D particle tracking velocimetry & Lagrangian turbulence
Also studied
Density-stratified flows & physics-informed ML
Open source
Key maintainer of OpenPIV and OpenPTV
Institution
School of Mechanical Engineering, Tel Aviv University

Interested in collaborating?

We welcome inquiries from prospective students, collaborators, and colleagues working on experimental fluid mechanics, flow diagnostics, or physics-informed machine learning.

Contact the lab