| dc.description.abstract | This thesis investigates the application of traveling wave surface morphing as an advanced
active flow control method to enhance the aerodynamic performance of airfoils operating at
low Reynolds numbers of 1000 and 20,000. The motivation for this study stems from the need
to address flow separation and improve aerodynamic efficiency in low-speed applications, such
as micro air vehicles (MAVs) and unmanned aerial vehicles (UAVs). The study focuses on the
NACA 0018 airfoil, analyzing its aerodynamic behavior under various traveling wave
configurations (frequency, amplitude, and wavelength) across different angles of attack,
ranging from 7 to 15 degrees. Through a combination of computational fluid dynamics (CFD)
methods, including the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) and Large
Eddy Simulation (LES), and simulations conducted using OpenFOAM, along with machine
learning (ML) techniques, the research provides comprehensive insights into the optimization
of traveling wave parameters for enhanced lift-to-drag ratios and delayed flow separation. The
findings at a Reynolds number of 1,000 demonstrate that backward traveling waves
significantly improve the aerodynamic performance of the NACA 0018 airfoil. Numerical
simulations show that, for wavelengths between 0.1 and 0.4, the lift-to-drag ratio increases
12% from 3.21 to 3.55, compared to the baseline unactuated case where the ratio remains at
2.83. In contrast, forward traveling waves are shown to decrease the lift-to-drag ratio due to
the induction of reverse flows on the suction side, highlighting the superiority of backward
traveling wave actuation. The results show that amplitudes and frequencies play a crucial role
in achieving optimal flow control. At an amplitude of 0.003 and a frequency of 1.5, backward
traveling waves generate a lift coefficient four times higher than those at a low frequency of
0.25. At a higher Reynolds number of 20,000, traveling wave actuation continues to exhibit
remarkable effects on flow separation control and turbulence management. Results reveal that
backward traveling waves not only enhance lift but also reduce drag by controlling the
turbulent kinetic energy (TKE) and generating coherent flow structures such as quasi-
streamwise vortices and reverse horseshoe vortices. A parametric study shows that a
wavelength of 0.3, an amplitude of 0.003, and a frequency of 1.0 yield the maximum lift-to-
drag ratio of 5.47, compared to 2.91 (i.e. by approx. 88%) for the unactuated airfoil.
Furthermore, large coherent structures (LCS) analysis demonstrates the ability of traveling
waves to stabilize boundary layer dynamics and delay flow separation, especially at higher
angles of attack. For example, at an angle of attack of 11°, the suction peak reduces from −2.15
in the unactuated case to −1.27 with traveling wave actuation, significantly shrinking the
separation bubble. This study also analyzes TWM's effects on boundary layer stability, flow
separation, and vortex structures across angles of attack (7°, 11°, and 14°). The results show
that TWM organizes vortex structures, enhances boundary layer mixing, and delays flow
separation, significantly reducing drag and stall effects. Higher amplitude TWM (a=0.02)
achieves the best results, stabilizing the boundary layer and creating a quasi-laminar flow even
at high angles of attack. These findings highlight TWM as a promising technique for improving
lift and reducing drag in aerodynamic applications. These findings demonstrate the novelty of
applying advanced flow control techniques to a thicker airfoil profile, broadening the scope of
aerodynamic research.
The integration of CFD simulations with machine learning models, such as Gaussian Process
Regression (GPR), Support Vector Machines (SVM), and Decision Trees (DT), enables
efficient prediction and optimization of aerodynamic coefficients. Machine learning techniques
are applied to analyze 93 simulation cases, revealing strong correlations between wave
parameters (frequency, amplitude, and wavelength) and aerodynamic forces. For instance, the
optimal wave configuration at an angle of attack of 11° achieves a drag reduction of 40% and
a lift increase of 20%, reducing computational time significantly compared to traditional CFD
methods.
The thesis highlights the transformative potential of traveling wave surface morphing in
advancing airfoil design for low Reynolds number applications. By bridging numerical
methods and machine learning, this study introduces a practical framework for real-time
optimization of aerodynamic performance. The research establishes that backward traveling
waves are highly effective in mitigating flow separation, enhancing lift-to-drag ratios, and
controlling turbulent flow structures. | en_US |
