A Computational "Rheometer" for Turbulent Flows

Speaker: Ali Mani, Stanford University
February 6, 2020

Presentation

Abstract

This study presents a numerical procedure, which we call the macroscopic forcing method (MFM), which reveals the differential operators acting upon the mean fields of quantities transported by underlying fluctuating flows. Specifically, MFM can reveal differential operators associated with turbulent transport of scalars and momentum. We present this methodology by considering canonical problems with increasing complexity. For spatially homogeneous and statistically stationary systems, we observe that eddy diffusivity can be approximated by an operator of the form D/1-l2∇2, where l is a length scale on the order of large-eddy size and D is a coefficient. A validation test shows that this operator leads to significant improvement in RANS prediction of axisymmetric turbulent jets. We show a cost-effective generalization of MFM for analysis of non-homogeneous and wall-bounded flows, where eddy diffusivity is found to be a convolution acting on the macroscopic gradient of transported quantities. We introduce MFM as an effective tool for quantitative understanding of non-Boussinesq effects in turbulence, particularly, the effects associated with anisotropy and non-locality of macroscopic mixing.

Biography

Ali Mani is an associate professor in the Department of Mechanical Engineering at Stanford University. He graduated with a bachelor’s degree in Mechanical Engineering from Sharif University of Technology in 2002, and a Ph.D. from Stanford in 2009. Prior to joining the faculty in 2011, he was an engineering research associate at Stanford from 2009-11 and a senior postdoctoral associate at Massachusetts Institute of Technology in the Department of Chemical Engineering in 2011. He was the recipient of an Office of Naval Research Young Investigator Award in 2015, and an NSF CAREER award in 2016. He is a recipient of Tau Beta Pi Teaching Honor Roll for the 2018-2019 academic year. His research group develops and utilizes large-scale high-fidelity numerical simulations to reveal quantitative understanding of fluid dynamic processes that involve strong coupling with mass-transport phenomena and commonly involve turbulence or chaos. His teaching includes the undergraduate engineering math classes and graduate courses on fluid mechanics and numerical analysis.

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