Michael Ekwonu
Peer Reviewer at Physics of Fluids
Nam District, Ulsan, South Korea
Summary
👤
Senior🎓
Top SchoolMichael Ekwonu is an applied mathematician and python-focused numerical analyst with nine years of experience translating complex multiphase fluid dynamics problems into measurable, data-driven insights. Currently a postdoctoral researcher at University of Ulsan and peer reviewer for Physics of Fluids, he develops 4D Lagrangian particle-tracking and simultaneous velocity–temperature measurement systems, combining OpenFOAM/OpenLB simulation, uncertainty quantification, and machine learning to make "invisible" flows visible. His background spans academia and industry—from process simulation at Shell to teaching and supervision—giving him practical process-engineering intuition alongside advanced experimental diagnostics. Known for bridging hands-on experimental design (spray and bubble-curtain visualization) with data assimilation and deep-learning approaches, he routinely mentors graduate researchers and publishes peer-reviewed work in fluid dynamics.
9 years of coding experience
4 years of employment as a software developer
MSc, Advanced Process Integration & Design (Refinery Design and Operations Major), MSc, Advanced Process Integration & Design (Refinery Design and Operations Major) at The University of ManchesterDoctor of Philosophy - PhD, Mechanical Engineering, Doctor of Philosophy - PhD, Mechanical Engineering at Pusan National UniversityBachelor of Engineering (BEng), Chemical Engineering, Bachelor of Engineering (BEng), Chemical Engineering at University of Benin
English
Github Skills (9)
latex2
python1
conda1
matlab1
toolbox1
jupyter1
orc1
modeling1
power-systems1
Programming languages (2)
Github contributions (5)
mcekwonu/ExtensysPlots
Mar 2021 - Feb 2024
Contributions:2 releases, 124 pushes, 2 tags in 3 years
mcekwonu/Hidden-Fluid-Mechanics-Pytorch
May 2022 - Nov 2022
Contributions:1 release, 75 commits, 73 pushes in 5 months
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