LAB Announcements

Spotlight

Behnam Bozorgmehr

Behnam Bozorgmehr

Hometown: Bojnord, Iran

Undergraduate: Iran University of Science and Technology (IUST)

Joined EFD Lab: 2017

Current Program: PhD

Research Interests: Environmental Fluid Dynamics, Computational Fluid Dynamics (CFD), Multiphase and Free-Surface Flows, Turbulence Modeling.

Google Scholar Page

Contact: Behnam.Bozorgmehr@utah.edu

Observational and Theoretical Investigations Related to Hydrometeors Settling in Turbulent Air

Red Butte Canyon, UT Video demonstration of the measurement of SWE using the Differential Emissivity Disdrometer (DEID) and imaging for falling snowflakes using an SLR camera and laser for particle tracking velocimetry.

Supported by the National Science Foundation - AGS 1841870

Investigators, Senior Personnel, and Collaborators:
Tim Garrett (University of Utah, Principal Investigator)
Eric Pardyjak (Utah, Co-Principal Investigator)

Postdoctoral Researchers:
Dhiraj Kumar Singh (Mech Eng)
Students:
Spencer Donovan (MS)
Karlie Rees (MS now PhD Atmos Sci)
Ryan Szczerbinski (PhD Atmos Sci)
Trent Meisenheimer (MS Mech Eng)

Numerical weather and climate models are sensitive to descriptions of how fast frozen precipitation falls. Many models still use calculations that are rooted in measurements taken nearly 50 years ago. This award will provide up-to-date information on precipitation fall speed, including the impact of turbulence. The work will be accomplished using a new instrument called the Airborne Particle Imager which is designed to measure 3D velocity and take high resolution photography of the individual particles. The main broader societal impact of the award will be the confirmation or improvement upon the assumptions made by numerical models, which will potentially lead to improved weather forecasts. The lead researcher has provided significant public outreach through snowflake imagery, and the new instrument should improve upon those images. The instrument also has potential interdisciplinary and technology transfer uses. In addition, the award will provide education and training opportunities, including support for a female military veteran.

The research team plans an observational and theoretical project to improve understanding of how the turbulent atmosphere affects the fall speed of precipitation. A central aspect of the project will be the development of the Airborne Particle Imager (AIP) which is an update to the current Multi-Angle Snowflake Camera (MASC). The AIP will minimize ambient flow disturbance and add 3D velocity measurements to the MASC's existing capabilities. The AIP will be deployed during the 2019-2020 winter in the Complex Hydrometeor Aerial Locomotion and Image Capture Experiment (CHALICE) with other instruments to relate particle type, orientation, and motions to the degree of ambient turbulence. The observations will be compared to numerical models and past studies to explore the hypothesis that particle settling speed is slowed in low turbulence and accelerated in high turbulence situations, with increased deviation of particle orientation from the horizontal.