activity
20242026
most citedToward ultra-efficient high-fidelity prediction of bed morphodynamics of large-scale meandering rivers using a novel LES-trained machine learning approach

1 citations · 1 across the 3 of their papers we have counts for

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physics.flu-dyn2025

Computational study of vertical-axis MHK turbines using a coupled flow-sediment-turbine modeling approach

Mehrshad Gholami Anjiraki, Mustafa Meriç Aksen, Samin Shapourmiandouab +2

We present a coupled large-eddy simulation (LES) and bed morphodynamics study to investigate the influence of sediment dynamics on the performance of a utility-scale marine hydroki…

physics.flu-dyn2025

On the interaction of fish and marine hydrokinetic turbines: Insights gained through experimental and computational observations

Hossein Seyedzadeh, Mehrshad Gholami Anjiraki, Guglielmo Sonnino Sorisio +3

Tidal and riverine hydrokinetic turbines offer promising solutions for renewable energy generation in aquatic environments. However, their ecological impact, especially on fish beh…