Aditya G. Nair

Aditya G. Nair is an Assistant Professor in the Department of Mechanical Engineering at the University of Nevada Reno, specializing in aerospace engineering. He holds a PhD from Florida State University (2018), an MS from the University of Michigan (2013), and a B.E. from the University of Mumbai (2011). His research focuses on computational fluid dynamics (CFD), fluid-structure interaction, and applying network theory and data science to fluid systems. He teaches courses such as Aerodynamics, Compressible Flow, and Computational Fluid Dynamics. His research explores topics like turbulence control, phase-based flow stabilization, and machine learning applications in fluid dynamics. Nair’s work bridges fluid mechanics with network science and data-driven methodologies, with applications in unsteady aerodynamics and flow control. He maintains an active GitHub repository portfolio, including projects on modal decomposition and fluid flow control strategies.

Education:

• PhD in Mechanical Engineering, Florida State University (2018)
• MS in Mechanical Engineering, University of Michigan (2013)
• B.E. in Mechanical Engineering, University of Mumbai (2011)

Research Interests: Integrating fluid mechanics with data science, network theory, and graph theory to address challenges in turbulence, flow control, and CFD. His work emphasizes model reduction techniques, adaptive mesh refinement, and invariant-based control strategies.

Teaching: ME482/682 Aerodynamics (2020–2023), ME480/680 Compressible Flow (2021, 2023), ME793 Computational Fluid Dynamics (2022).

Research Outputs: Recent work focuses on network-theoretic modeling of fluid flows, data-driven control strategies, and turbulence modification through energy/enstrophy manipulation. Key themes include phase-based control systems and machine learning-enhanced metamodeling.

Labs/Teams: Active contributor to open-source projects via GitHub, including repositories like Modal-Decomposition-ROM and Vortical-community-detection, reflecting his interest in reduced-order modeling and fluid flow analysis.