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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 …
A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements
A comprehensive understanding of wind turbine wake characteristics is vital, particularly in
the context of expanding large offshore wind farms. Existing wake measurement techniques …
the context of expanding large offshore wind farms. Existing wake measurement techniques …
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 …
Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades
A precise and cost-effective prediction tool for fluid-structure interaction (FSI) analysis is
crucial for optimizing the structural design of tidal turbine blades. However, the high …
crucial for optimizing the structural design of tidal turbine blades. However, the high …
Enhancing high-resolution reconstruction of flow fields using physics-informed diffusion model with probability flow sampling
Y Guo, X Cao, M Zhou, H Leng, J Song - Physics of Fluids, 2024 - pubs.aip.org
The application of artificial intelligence (AI) technology in fluid dynamics is becoming
increasingly prevalent, particularly in accelerating the solution of partial differential …
increasingly prevalent, particularly in accelerating the solution of partial differential …
Three-dimensional autoencoder for the flow field reconstruction of an inclined circular disk
At present, research on integrating deep learning with fluid dynamics has mostly focused on
two-dimensional (2D) flow fields. Shifting from the study of 2D flow fields to the study of three …
two-dimensional (2D) flow fields. Shifting from the study of 2D flow fields to the study of three …
Reconstructing multiphase flow fields with limited pressure observations based on an improved transformer model
Y Xu, Y Sha, C Wang, H Cui, Y Wei - Ocean Engineering, 2024 - Elsevier
In practical applications, the implementation of active cavitation control can significantly
enhance the hydrodynamic performance of underwater vehicles. However, the sparsity of …
enhance the hydrodynamic performance of underwater vehicles. However, the sparsity of …
TurbineNet: Advancing tidal turbine blade hydrodynamic performance prediction with neural networks
The efficient prediction of system performance is a critical aspect of engineering equipment
design, with the traditional methods facing limitations such as high computational demands …
design, with the traditional methods facing limitations such as high computational demands …
Reconstruction of the solid–liquid two-phase flow field in the pipeline based on limited pipeline wall information
S **ao, C Wan, H Zhu, D Zhou, Y Bao, S Huang… - Physics of …, 2025 - pubs.aip.org
Pipeline hydraulic transportation is the primary method for transporting deep-sea mineral
resources and fossil fuels. Pipeline blockage often causes excessive pressure in the …
resources and fossil fuels. Pipeline blockage often causes excessive pressure in the …