Fernando Zigunov

Dr. Fernando Zigunov is an Assistant Professor at Syracuse University and a member of the Skytop Turbulence Laboratory. His research integrates machine learning with experimental fluid dynamics to solve complex flow problems in aerospace engineering and energy systems. He specializes in advanced diagnostic techniques such as tomographic PIV, time-resolved pressure-sensitive paint (PSP), and modal analysis to study flow dynamics and turbulence.

Education:

• Ph.D., Mechanical Engineering, Florida State University (2020)
• B.S., Mechanical Engineering, Universidade do Vale do Rio dos Sinos, Brazil (2017)

Research Interests: Dr. Zigunov’s work focuses on automated experimental setups for flow control, data assimilation in fluid dynamics, and the application of machine learning to optimize engineering systems. He develops novel methods for analyzing volumetric flow data and reducing noise in supersonic jets using genetic algorithms.

Publications: His recent work includes advancements in stereoscopic PIV techniques, afterbody flow dynamics, and active flow control strategies. These studies highlight his expertise in combining computational models with experimental data to address real-world fluid dynamics challenges.

Awards:

• Don Fuqua Fellowship Award (2018–2020)
• Brazilian Scientific Mobility Program Exchange Scholarship (2015–2016)

Grants & Advising: While specific grants are not listed, his research is supported by Syracuse University’s resources. He currently advises no listed students but collaborates on projects involving advanced fluid diagnostics and flow control.

Labs/Teams: Dr. Zigunov leads research within the Skytop Turbulence Laboratory, focusing on experimental and computational fluid dynamics innovations.