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Background

Laser Doppler velocimetry (LDV) has long been a standard technique in experimental fluid mechanics labs. Researchers in Associate Professor Greg Rieker's lab at ÃÛÌÇÖ±²¥ Boulder extend the capabilities of this technique by reshaping the intensity profile of the optical probe beam and by developing a machine learning-based signal processing scheme to analyze the expected signals which can be more complicated than those from LDV.

Technology

The light scattered by a particle passing through a probe beam caries with it a history of the particle’s trajectory through the beam. When the beam is patterned, the scattered light signal is matched with the properties which gave rise to the motion via a machine learning model.

Advantages

• Signal processing technique makes no compromise between spatial and temporal resolution
• Uses readily available seeding particles and requires only a low seeding density
• May function with existing LDV hardware

Applications

• Combustion R&D
• Environmental research
• Flow facilities (wind tunnels, water channels)
• Medical devices
• Microfluidic systems
• Granular flows

What's Next?

This technology is looking for exclusive and non-exclusive licensing.