7 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
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…
Toward ultra-efficient high fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models via LES-trained machine learning
Christian Santoni, Dichang Zhang, Zexia Zhang +3
This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of util…