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Using Unsupervised Machine Learning to Reduce the Energy Requirements of Active Flow Control

Kerestes, Jared N.
http://orcid.org/0000-0003-0991-6800
http://rave.ohiolink.edu/etdc/view?acc_num=wright1715121949615308

Abstract Details

2024, Doctor of Philosophy (PhD), Wright State University, Engineering PhD.
It is generally accepted that there exist two types of laminar separation bubbles (LSBs): short and long. The process by which a short LSB transitions to a long LSB is known as bursting. In this research, large eddy simulations (LES) are used to study the evolution of an LSB that develops along the suction surface of the L3FHW-LS at low Reynolds numbers. The L3FHW-LS is a new high-lift, high-work low-pressure turbine (LPT) blade designed at the Air Force Research Laboratory. The LSB is shown to burst over a critical range of Reynolds numbers. Bursting is discussed at length and its effect on transition, vortex shedding, and profile loss development are analyzed in depth. The results of these analyses make one point very clear: the effects of bursting are non-trivial. That is, long LSBs are not just longer versions of short LSBs. They are phenomena unto themselves, distinct from short LSBs in terms of their vortex dynamics, profile loss footprint, time-averaged topology, etc. This work culminates in a demonstration of how, with the aid of unsupervised machine learning, these differences can be leveraged to reduce the energy requirements of steady vortex generator jets (VGJs). Relative to pulsed VGJs, steady VGJs require significantly more energy to be effective but are more realistic to implement in actual application. By tailoring VGJ actuation to LSB type (i.e., actuating differently in response to a long LSB than to a short LSB), it is shown that significant energy savings can be realized.
Mitch Wolff, Ph.D. (Advisor)
George Huang, Ph.D. (Committee Member)
John Clark, Ph.D. (Committee Member)
Christopher Marks, Ph.D. (Committee Member)
114 p.
Fluid Dynamics
Large Eddy Simulation; Machine Learning; Fluid Dynamics; Laminar Separation Bubbles

Recommended Citations

Citations

  • Kerestes, J. N. (2024). Using Unsupervised Machine Learning to Reduce the Energy Requirements of Active Flow Control [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1715121949615308

    APA Style (7th edition)

  • Kerestes, Jared. Using Unsupervised Machine Learning to Reduce the Energy Requirements of Active Flow Control. 2024. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1715121949615308.

    MLA Style (8th edition)

  • Kerestes, Jared. "Using Unsupervised Machine Learning to Reduce the Energy Requirements of Active Flow Control." Doctoral dissertation, Wright State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=wright1715121949615308

    Chicago Manual of Style (17th edition)

wright1715121949615308
131
© 2024, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.