Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 148136

148136 - Reduced-Complexity Models for Transitional and Turbulent Flows 

This poster will provide brief overviews of my three recent young investigator awards. 

I will focus on my 2024 ONR YIP.  This project aims to enable drastically improved predictions of laminar-to-turbulent transition for hypersonic boundary layers by developing a new class of probabilistic models that predict the likelihood that a flow will transition at each downstream position.  Predicting boundary layer transition and the associated impact on heat transfer and aerodynamic performance is a fundamental challenge in hypersonic vehicle design, and the inability of current models to quantify the sensitivity of the transition process to freestream and surface conditions creates large uncertainties that necessitate conservative designs that increase weight and degrade vehicle performance.  This project will fill the urgent need for reliable hypersonic transition models by approaching the problem from a novel probabilistic perspective.  Rather than seeking a definite transition point, which is inconsistent with the sensitivity and intermittent nature of transitional flow, the proposed model will take a statistical description of environmental disturbances as input and predict the expected (mean) transition point and the probability of observing transition at each downstream position along the boundary layer.  The models will be used to study the sensitivity of transition probabilities to leading-edge bluntness, freestream disturbances, and surface roughness for a series of blunt and finned cones.

Second, I briefly present work from my 2022 NSF CAREER award, which seeks to develop a new class of space-time reduced-order models for turbulent flows.  Unlike standard methods, the new models will respect the intimate physical relationship between spatial and temporal scales, potentially drastically increasing their efficiency and accuracy.  Our progress so far will be demonstrated using the problem of transport in a lid-driven cavity flow. 

Third, I will summarize my recently concluded 2020 AFOSR YIP.  This project introduced a new flow estimation and control framework based on a popular model called resolvent analysis.  The new tools have numerous advantages over standard methods when applied to fluids problems, including improved accuracy and reduced cost.  Their effectiveness will be demonstrated by estimating and controlling velocity fluctuations in the laminar and turbulent wakes of an airfoil using sparse sensors and actuators on the surface of the airfoil.

Presenting Author: Aaron Towne University of Michigan

Presenting Author Biography: Aaron Towne is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan. His research develops reduced-complexity models that can be used to understand, predict, and control turbulent flows, using both physics-based and data-driven methods. Applications include aeroacoustics, aerodynamics, and wall-bounded flows, among others. Before joining the faculty at Michigan, he was a Postdoctoral Fellow in the Center for Turbulence Research at Stanford University. He received his PhD and MS degrees from the California Institute of Technology and his BS from the University of Wisconsin-Madison. He is a recipient of the 2020 Young Investigator Program Award (YIP) from the Air Force Office of Scientific Research (AFOSR), a 2022 National Science Foundation (NSF) CAREER Award, the 2024 Young Investigator Program Award (YIP) from the Office of Naval Research (ONR), and multiple best paper awards from the American Institute of Aeronautics and Astronautics (AIAA) and American Society of Mechanical Engineers (ASME).

Authors:

Aaron Towne University of Michigan