Research Areas in Aerodynamics and Fluid Mechanics

Aerodynamic Control

Aerodynamic control of low Reynolds number flow using synthetic jet actuation has significant applications across multiple fields:

Design and operation of unmanned aerial vehicles (UAVs) at low speeds
Separation control at jet engine inlets
Optimized design of compressor blades

Over several decades, researchers have tested various control methods:

Method	Advantages	Limitations
Continuous suction	Reduces drag by removing slow-moving boundary layer fluid	Complex plumbing, additional weight
Continuous blowing	Adds momentum to stagnant fluid near surfaces	Power requirements offset aerodynamic gains
Vortex generators	Simple implementation	Fixed performance envelope
Heat strips	No moving parts	Energy intensive
External audio excitation	Non-intrusive	Limited effectiveness range
Synthetic jet actuation	No net mass flow, efficient	Requires precise timing control

Table 1: Comparison of Aerodynamic Control Methods

Our research has employed both acoustic and mechanical control to understand flow mechanisms over a NACA 0024 airfoil. This work has characterized both external (acoustic) and internal (Synthetic Jet Actuator) control methods. We’ve integrated these findings with our wind turbine research to optimize power generation in the 1-5 kW range for low wind speed regions, particularly throughout Ontario.

Aeroacoustics

Aircraft noise reduction is critical for urban airport integration, with airframe noise being a significant contributor—especially during landing operations. Landing gear systems represent the major source of airframe noise. Our research aims to accurately calculate and predict sound pressure levels (SPL) across frequency ranges.

We’ve also conducted targeted studies addressing specific industry challenges and requirements.

Advanced Turbulence Statistics from Big Data

Particle Image Velocimetry (PIV) data processing presents significant computational challenges:

Traditional processing is too time-intensive for active control systems
Dataset size constraints limit analysis scope
Uncertainty quantification remains an outstanding challenge

Our approach leverages GPU acceleration to dramatically improve processing capabilities. We’ve achieved acceleration factors up to 120x for certain algorithms, enabling real-time or near-real-time analysis. This represents a transformative capability for experimental fluid mechanics.

In addition to GPU-based advancements, recent work by Shirinzad et al. (2023) has improved CPU-based PIV processing through an enhanced Python-based open-source software, offering greater flexibility and stability for large datasets.

Processing Time Comparison

Image Size	ASUS CPU	Jetson TX1	Speed Improvement
128 × 256	0.461 s	0.018 s	25.6×
368 × 512	2.386 s	0.024 s	99.4×
1024 × 1280	16.023 s	0.071 s	225.7×
2160 × 2560	68.607 s	0.274 s	250.4×

Table 2: GPU vs CPU Processing Time Comparison for PIV Analysis

Selected Publications

2025

Jaber et al.
Jaber, K., Essel, E. E., & Sullivan, P. E. (2025). GPU-Native Adaptive Mesh Refinement with Application to Lattice Boltzmann Simulations.
Computer Physics Communications, 109543.
https://doi.org/10.1016/j.cpc.2025.109543 [JIF: 7.200]

2024

Ho et al.
Ho, H.H., Shirinzad, A., Essel, E.E., and Sullivan, P.E. (2024). Synthetic Jet Actuators for Active Flow Control: A Review.
Fluids, 9, 290.
https://doi.org/10.3390/fluids9120290 [JIF: 3.094]
Ho et al.
Ho, H., Essel, E. E., and Sullivan, P. E. (2024). Improving Three-Dimensional Synthetic Jet Modeling in a Crossflow.
ASME. J. Fluids Eng., 146(3), 031302.
doi.org/10.1115/1.4064185 [JIF: 2.412]
Machado et al.
Machado, A., Xu, K., & Sullivan, P. E. (2024). Spanwise control authority of synthetic jets on a stalled airfoil.
Physics of Fluids, 36(6), 064113.
https://doi.org/10.1063/5.0212135 [JIF: 4.532]
Machado et al.
Machado, A., Xu, K., & Sullivan, P. E. (2024). Visualizing three-dimensional effects of synthetic jet flow control.
Journal of Visualization.
https://doi.org/10.1007/s12650-024-00992-0 [JIF: 2.024]
Shi et al.
Shi, X & Sullivan, P. E. (2024). Plasma Actuator Separation Control Investigated with Spectral Proper Orthogonal Decomposition.
AIAA Journal, 62(5), 1755–1774.
https://doi.org/10.2514/1.J063327 [JIF: 2.991]
B. Xu et al.
Bin Xu, Liu, T., Shi, X., Sullivan, P. E., Chen, Z., & Chen, X. (2024). Mitigation of crosswind effects on high-speed trains using vortex generators.
Physics of Fluids, 36(7), 075199.
https://doi.org/10.1063/5.0218270 [JIF: 4.532]

2023

Arafa et al.
Arafa, N, Sullivan, P.E. and Ekmekci, A. (2023). Noise and Jet Momentum of Synthetic Jet Actuators with Different Orifice Configurations.
AIAA Journal.
https://doi.org/10.2514/1.J062920 [JIF: 2.991]
Jiang et al.
Jiang, S., Yu, J., Yin, S., Yang, Y., Chen, F., and Sullivan, P. E. (2023). Uncertainty Quantification of Separation Control with Synthetic Jet Actuator over a NACA0025 Airfoil.
Aerospace Science and Technology, Vol. 133, 108106.
https://doi.org/10.1016/J.AST.2023.108106 [JIF: 5.343]
Shirinzad et al.
Shirinzad, A., Jaber, K., Xu, K., Sullivan, P.E. (2023). An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units.
Fluids, 8, 285.
https://doi.org/10.3390/fluids8110285 [JIF: 3.094]
K. Xu et al.
Xu, K., Lavoie, P., & Sullivan, P. (2023). Flow Reattachment on a NACA 0025 Airfoil Using an Array of Microblowers.
AIAA Journal, 61(6), 2476–2485.
https://doi.org/10.2514/1.J062512 [JIF: 2.991]

2022

Arafa et al.
Arafa, N., Sullivan, P. E., & Ekmekci, A. (2022). Jet Velocity and Acoustic Excitation Characteristics of a Synthetic Jet Actuator.
Fluids, 7(12), 387.
DOI: 10.3390/FLUIDS7120387 [JIF: 3.094]
Ho et al.
Ho, H. H., Essel, E. E., & Sullivan, P. E. (2022). The interactions of a circular synthetic jet with a turbulent crossflow.
Physics of Fluids, 34(7), 075108.
https://doi.org/10.1063/5.0099533 [JIF: 4.532]