School of Physics Faculty Search Colloquium Series- Dr. Alec Linot

Speaker: Dr. Alec Linot

Host: Dr. Roman Grigoriev

Title: Low-dimensional models and stability analysis for controlling unsteady fluid flows

Abstract: Predicting and controlling unsteady fluid flows is of broad interest for tasks such as minimizing drag in a pipe, maintaining lift during a gust encounter, and increasing turbulence for mixing or delaying flow separation. For perspective, turbulent drag accounts for 5% of manmade CO2, thus, there are major benefits to even small improvements in drag reduction. Computational fluid dynamics (CFD) allows us to simulate these flows, but these simulations can be computationally expensive and difficult to interpret. This problem is further exasperated because repeated simulations are often required for control, computing invariant solutions, or determining stability to perturbations.

Here, I discuss two approaches for overcoming these computational challenges, allowing us to perform analysis that we could not do with CFD alone. In the first section, I describe a low-dimensional modeling method to find a coordinate system of the manifold on which turbulent trajectories lie and the time evolution in this coordinate system. This method is completely data-driven, is computationally less expensive to evaluate than the full simulation and maintains all the physics of the true system. Then, I show how this model can be used for computing invariant solutions and for control with reinforcement learning. In the second section, I discuss how to avoid the transition from a laminar flow to a turbulent flow during deceleration (decelerating flows are often the most sensitive to perturbations). Specifically, I present an optimization procedure for computing the deceleration profile that leads to the smallest transient growth computed using nonmodal stability analysis. This approach results in deceleration profiles that reduce the energy of perturbations by multiple orders of magnitude.

Bio: Alec Linot works as a postdoctoral researcher with Professor Kunihiko (Sam) Taira in the Mechanical and Aerospace Engineering Department at UCLA. Prior to working with Prof. Taira, he received a BS in Chemical Engineering from Kansas State University and a Ph.D. in Chemical and Biological Engineering from the University of Wisconsin–Madison with Michael D. Graham. His research focuses on the low-order modeling and control of complex fluid flows through the development and use of methods in machine learning and stability analysis.