Turbulent flows are dominated by large-scale motions with temporal and spatial coherence. In particular, LSMs in boundary layers, which can span several times the boundary layer thickness in the streamwise direction, contain a large fraction of the turbulent kinetic energy of the streamwise velocity component, contribute significantly to the average Reynolds shear stresses, and transport momentum within the turbulent boundary layer. Given their importance, we pose the question of whether we can manipulate the movement of these LSMs and bring them closer to the wall in order to energize a boundary layer. To answer that question, we introduce a set of synthetic LSMs in a direct numerical simulation of a laminar boundary layer by generating a series of aligned hairpin vortices via force fields. We then target these structures via an actuator modeled as a body force field with a pitch angle towards the wall that generates a region of downwash that traps and pushes the synthetic LSMs toward the wall. The body force required to move the oncoming LSMs is computed via a model predictive control framework (Tsolovikos et al., AIAA J. 2021). The effect of moving these coherent structures toward the wall on the vorticity and momentum transport in the boundary layer is studied.
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Publication: Tsolovikos, A., Suryanarayanan, S., Bakolas, E. and Goldstein, D., 2021. Model Predictive Control of Material Volumes with Application to Vortical Structures. AIAA Journal, pp.1-14.<br>Tsolovikos, A., Suryanarayanan, S., Bakolas, E. and Goldstein, D.B., 2020. Toward Model-Based Control of Near-Wall Turbulent Coherent Structures. In AIAA Scitech 2020 Forum (p. 1319).
Presenters
Alexandros Tsolovikos
The University of Texas at Austin
Authors
Alexandros Tsolovikos
The University of Texas at Austin
Akshit Jariwala
The University of Texas at Austin
Pranav Sridhar
The University of Texas at Austin
Saikishan Suryanarayanan
University of Texas at Austin, UT Austin, The University of Texas at Austin
Efstathios Bakolas
The University of Texas at Austin
David Goldstein
University of Texas at Austin, The University of Texas at Austin