Ali Shirinzad is a PhD student in the Department of Mechanical Engineering at the University of Toronto, with a master’s in mechanical engineering from the University of Manitoba. His research primarily focuses on flow control over pitching airfoil and its applications to wind turbines. He is developing an experiment to investigate this study area within the broader context of turbulent flows. His work has been published in peer-reviewed journals in the field.

My primary research focuses on aerodynamic design optimization of high-speed trains, conducting in-depth investigations into diverse operating conditions, including open-air scenarios, crosswind environments, tunnel transits, and tunnel crossover events. The research objectives encompass three key aspects: aerodynamic drag mitigation, operational safety enhancement, and passenger comfort improvement.

My research focuses on the application of active flow control devices, specifically synthetic jet actuators, to low Reynolds number airfoil flow. This has diverse engineering applications, including UAVs and air taxis. The project is primarily numerical, utilizing URANS and LES simulations to gain insight into the underlying physics and enhance control efficiency.

I am developing a GPU-native implementation of the Lattice Boltzmann Method with adaptive mesh refinement for weakly compressible flow. The goal is to perform efficient and accurate Large Eddy Simulation of airfoils at low Reynolds number. This solver will facilitate the analysis of active flow control devices such as the synthetic jet actuator and enable quick generation of numerical data for designing flow control strategies.

I am an incoming MASc student. My research will focus on developing a Reynolds-Averaged Navier-Stokes (RANS) model of fluid flow inside aircraft gearboxes in collaboration with an industry partner. This model will be implemented in commercial computational fluid dynamics (CFD) software and will allow the industry partner to obtain an improved understanding of gear windage losses.

I am a first-year MASc student currently working on developing a novel Eulerian-Lagrangian model for predicting the collection efficiency of several precipitation gauges widely used in weather forecasting. This model focuses on Large Eddy Simulation (LES) of the air flow field coupled with OpenFOAM’s Lagrangian library for determining particle trajectories. This approach is useful for predicting the trajectories of snow particles and other particles not previously studied using a CFD approach.

My research focuses on multiphysics simulation of low-temperature urea decomposition processes. The simulation covers the fluid dynamics of multiphase flow, including spray and atomization, as well as the chain reactions of urea-water solution at the droplet level. The research aims to optimize urea-based selective catalytic reduction (SCR) systems, which are widely used in emission control to reduce nitrogen oxide (NOₓ) pollutants.

Dr. Xuan Shi is a computational fluid dynamics researcher who earned his PhD in Mechanical Engineering from the University of Toronto in March 2025, following his MEng in Mechanical Engineering and BASc in Computer Engineering from the same institution. His research focuses on flow control technologies using plasma actuators and synthetic jets, with publications in prestigious journals including AIAA Journal and Physics of Fluids. Dr. Shi specializes in spectral proper orthogonal decomposition (SPOD) and global resolvent analysis methods, applying high-performance computing techniques to solve complex fluid dynamics problems in aerospace and transportation engineering.

My research focused on aerodynamics, fluid mechanics, and turbulence, with particular emphasis on active flow control strategies. I developed synthetic jet arrays for separation control on NACA 0025 airfoil wings, demonstrating their effectiveness through particle image velocimetry (PIV) and comprehensive surface pressure measurements. My work contributes to the advancement of flow control technologies with potential applications in aerospace engineering and energy-efficient transportation systems.

My research involves an experimental investigation into synthetic jet active flow control to recover airfoils from stall, with significant applications for low-speed, high-altitude aircraft and enhancing wind turbine efficiency. By using novel flow visualization techniques alongside traditional quantitative methods, I aim to understand the three-dimensional flow behavior under various control strategies. With this understanding of the flow field, I perform multi-parameter optimization to enhance airfoil performance while minimizing power consumption.

My research includes two areas of acoustics: aeroacoustics and flow-generated noise, and the design and optimization of industrial silencers. The goal is to computationally predict noise generated by turbulence via the Ffowcs Williams–Hawkings (FWH) aeroacoustic analogy, using numerical simulations (RANS, LES, and DES). Additionally, I developed efficient methodologies for optimizing muffler design for large-scale applications, combining global optimization with FEM-based acoustic calculations.

My research looked into the flow control potential of piezoelectric micro-blowers for vertical tails in commercial aircraft. This project involved the characterization of the piezoelectric micro-blowers and the development of an experimental setup to test the micro-blowers with a sub-scale vertical tail model. Flow visualization, hot-wire measurements, and PIV measurements were conducted to evaluate the effectiveness of the micro-blowers.

Developed a novel method of hot water delivery in condominiums through field and computational studies.

Focused on computational simulation of synthetic jets to control low Reynolds number flow.

My work centered around the use of MEMS actuators for active flow control on airfoils. I studied the interaction between the actuators and the crossflow that leads to the desired control results. I designed a new PIV system for precise boundary layer measurements and developed high-performance algorithms for PIV data analysis using GPUs.