1 citations · 1 across the 3 of their papers we have counts for
5 papers
A novel machine learning framework based on proper orthogonal decomposition for turbine wake flow predictions in natural waterways
Mustafa Meriç Aksen, Hossein Seyedzadeh, Mehrshad Gholami Anjiraki +1
We developed a machine learning (ML) surrogate trained on instantaneous large eddy simulation (LES) data filtered using proper orthogonal decomposition (POD) to predict mean flow f…
Computational and experimental study of multi-grain-size sediment transport and sorting process
Mehrshad Gholami Anjiraki, Gabriel Narh, Jonathan Craig +5
Successful prediction of the interaction between turbulent flows and river ecosystems is essential for flood management, development of mitigation strategies, and infrastructure pr…
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…
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…
Toward ultra-efficient high-fidelity prediction of bed morphodynamics of large-scale meandering rivers using a novel LES-trained machine learning approach
Zexia Zhang, Mehrshad Gholami Anjiraki, Hossein Seyedzadeh +2
Flood-induced deformation of the bed topography of fluvial meandering rivers could lead to river bank displacement, structural failure of the infrastructures, and the propagation o…