A cost-effective CNN-BEM coupling framework for design optimization of horizontal axis tidal turbine blades
The paper proposes a novel cost-effective framework that combines deep learning
convolutional neural network (CNN) and blade element momentum (BEM) models for …
convolutional neural network (CNN) and blade element momentum (BEM) models for …
Flow reconstruction from sparse sensors based on reduced-order autoencoder state estimation
The reconstruction of accurate and robust unsteady flow fields from sparse and noisy data in
real-life engineering tasks is challenging, particularly when sensors are randomly placed. To …
real-life engineering tasks is challenging, particularly when sensors are randomly placed. To …
Multi-objective optimization and optimal airfoil blade selection for a small horizontal-axis wind turbine (HAWT) for application in regions with various wind potential
The type of airfoil with small wind turbine blades should be selected based on the wind
potential of the area in which the turbine is used. In this study, 10 low Reynolds number …
potential of the area in which the turbine is used. In this study, 10 low Reynolds number …
A novel cost-efficient deep learning framework for static fluid–structure interaction analysis of hydrofoil in tidal turbine morphing blade
A tidal turbine can benefit from exquisitely designed morphing blades with a flexible trailing
edge by mitigating up to 90% of the load fluctuation in harsh ocean environments, which …
edge by mitigating up to 90% of the load fluctuation in harsh ocean environments, which …
Optimization of horizontal axis wind turbine performance with the dimpled blades by using CNN and MLP models
A Abbaskhah, H Sedighi, P Akbarzadeh… - Ocean …, 2023 - Elsevier
In this study, four NNs models are designed by three types of data: CFD data of original (no-
dimpled), dimpled blades, and experimental data. These data are used to estimate torque …
dimpled), dimpled blades, and experimental data. These data are used to estimate torque …
A deep learning framework for reconstructing experimental missing flow field of hydrofoil
Hydrofoils play a crucial role in enhancing the efficiency of fluid machinery designed for
ocean environments, reducing lift-induced drag and contributing to improved overall …
ocean environments, reducing lift-induced drag and contributing to improved overall …
Fast flow field prediction of hydrofoils based on deep learning
C Li, P Yuan, Y Liu, J Tan, X Si, S Wang, Y Cao - Ocean Engineering, 2023 - Elsevier
Conventionally, the flow field over the hydrofoil is solved by computational fluid dynamics
(CFD), which is a computationally expensive task. As an alternative, deep learning (DL) …
(CFD), which is a computationally expensive task. As an alternative, deep learning (DL) …
Super-resolution reconstruction framework of wind turbine wake: Design and application
Complete and clear global wind turbine wake data is very important for the study of wind
turbine wake characteristics in increasingly large offshore wind farms. Existing wake …
turbine wake characteristics in increasingly large offshore wind farms. Existing wake …
TurbineNet/FEM: Revolutionizing fluid-structure interaction analysis for efficient harvesting of tidal energy
Horizontal axis tidal turbines (HATT) are promising candidates for hydroelectric power
extraction in coastal urban applications. Currently, concerns around the fluid–structure …
extraction in coastal urban applications. Currently, concerns around the fluid–structure …
DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade
Horizontal axis tidal turbines (HATT) conversion of ocean tidal waves into electricity
represents a promising source of clean and sustainable energy. However, the widespread …
represents a promising source of clean and sustainable energy. However, the widespread …