Laser Doppler velocimetry (LDV) has long been a standard technique in experimental fluid mechanics labs. Researchers in Associate Professor Greg Rieker's lab at CU 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.
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.
- 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
- Combustion R&D
- Environmental research
- Flow facilities (wind tunnels, water channels)
- Medical devices
- Microfluidic systems
- Granular flows
This technology is looking for exclusive and non-exclusive licensing.