3 papers
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…
physics.flu-dyn2024
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…
physics.flu-dyn2024
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…