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Turbulent boundary layers are dominated by large-scale motions (LSMs) of streamwise momentum surplus and deficit that contribute significantly to the statistics of the flow. In particular, the high-momentum LSMs residing in the outer region of the boundary layer have the potential to re-energize the flow and delay separation if brought closer to the wall. This work explores the effect of selectively manipulating LSMs in a moderate Reynolds number turbulent boundary layer for separation delay via well-resolved large-eddy simulations. Toward that goal, a model predictive control scheme is developed based on a reduced-order model of the flow that directs LSMs of interest closer to the wall in an optimal way via a body force-induced downwash. The performance improvement achieved by targeting LSMs for separation delay, compared to a naive actuation scheme that does not account for the presence of LSMs, is demonstrated.
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Generation of Hairpin vortices using a hybrid physic-cyber data assimilation approach
Turbulent boundary layers are largely influenced by spatiotemporally developing coherent structures known as Large-Scale Motions (LSMs). This work examines the idea of creating synthetic hairpin trains, a model for LSMs, generated in a nominal zero pressure gradient laminar boundary layer. The study investigates the agreement between the experimentally measured flow field and the hairpin vortices and its simulated counterpart with a hybrid 2D inlet region. This approach uses time-varying unsteady spatially discrete velocity data obtained through experiments as an inflow boundary condition to the direct numerical simulation (DNS). A pre-processing divergence correction and interpolation scheme is employed to convert experimental data into a format better suited for the DNS. The matching is done by recreating a downstream flow using this hybrid physio-cyber approach. This method demonstrates the capability to produce a sequence of hairpins even with a simple 2D planar coarse dataset. A satisfactory qualitative and quantitative agreement was evident when comparing Q-criterion iso-surfaces of instantaneous DNS and phase-locked experimental data. The results of this study not only demonstrate the efficacy of the proposed approach in recreating LSMs but also suggest its applicability to future hybrid experimental-DNS flow control studies.
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- Award ID(s):
- 2129494
- PAR ID:
- 10507729
- Publisher / Repository:
- AIAA
- Date Published:
- Journal Name:
- AIAA Scitech 2024
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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