Mechanical and Civil Engineering Seminar
Mechanical and Civil Engineering Seminar Series
Title: New experimental approaches for measuring and controlling fluid flows
Abstract: Multidisciplinary approaches can provide a unique experimental toolbox for resolving, understanding, and controlling fluid behavior. In this talk, I will present two such tools enabled by advancements in micro/nanoscale fabrication and describe how they can be applied to fundamental fluid mechanics research. The first investigation considers the behavior of a new family of surface modifications inspired by the Nepenthes pitcher plant and reveals a novel, passive method of turbulent drag reduction. Both experimental and numerical approaches are used to examine how these surfaces respond to and interact with turbulent shear flow. The second investigation describes a selective, strain-based velocimetry technique that utilizes the bending of a free-standing, electrically-conductive nanoribbon under fluid forcing. The sensor behavior is experimentally and theoretically characterized and found to be suitable for a wide range of applications, including turbulent flow measurements.
Bio: Matt Fu is a Postdoctoral Scholar Research Associate working with Prof. John Dabiri in the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT). He received his Ph.D. in Mechanical and Aerospace Engineering from Princeton University in 2018 and B.Sc. in Mechanical Engineering from Caltech in 2013. Prior to rejoining Caltech, Matt held postdoctoral positions at Stanford University, the University of Melbourne, and Princeton University. He co-founded a seed-stage start-up company, Tendo Technologies, Inc., in 2017 that specializes in scalable, precision solutions for liquid manufacturing and dispensing. His research interests include various problems related to turbulence, sensing, and flow control, focusing on multidisciplinary approaches and instrumentation
Please click the link below to join the webinar:
https://caltech.zoom.us/j/83332206698?pwd=aWVpTzFWNlYvNjV4ZUxHZ0tGdnQxdz09
Passcode: 161520