7 citations · 8 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…
Wind farm layout optimization using a novel machine learning approach
Mehrshad Gholami Anjiraki, Christian Santoni, Samin Shapourmiandouab +4
We present a novel approach to optimize wind farm layouts for maximum annual energy production (AEP). The optimization effort requires efficient wake models to predict the wake flo…
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…
Large eddy simulation of a utility-scale vertical-axis marine hydrokinetic turbine under live-bed conditions
Mehrshad Gholami Anjiraki, Mustafa Meriç Aksen, Jonathan Craig +2
We present a coupled large-eddy simulation (LES) and bed morphodynamics study to investigate the impact of sediment dynamics on the wake flow, wake recovery and power production of…
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…