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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 computational costs associated with fluid dynamic analysis pose a significant challenge, as the current lack of efficient FSI prediction methods hinders the advancement of cutting-edge tidal turbine designs. To address this issue, this paper proposes a novel consolidated framework that integrates deep learning convolutional neural networks (CNN) with blade element momentum (BEM) theory and finite element method (FEM) to perform deformation analysis of turbine blade structures. The proposed CNN-BEM-FEM integrated framework efficiently identifies the geometric features and predicts the hydrodynamic parameters of turbine blades and thus, achieving accurate assessments of the structural behavior of tidal turbines. The study applies two-step verification procedures to validate the prediction accuracy of the CNN-BEM-FEM framework and the result demonstrates excellent agreement with experimental tests for hydrodynamic performance and blade deformation. When compared with the static one-way FSI calculated by Ansys Workbench software, the computational efficiency of CNN-BEM-FEM framework increases by more than 18 times, with discrepancies in blade deformation and equivalent stress calculations generally less than 5 %. By applying the proposed method to predict the FSI performance of tidal turbine blades with various shear web structures, the practical applicability for composite turbine blade design is successfully demonstrated. The results underscore the potential of the CNN-BEM-FEM framework as an efficient and accurate prediction tool for optimizing the structural design of tidal turbine blades. • A high-efficient framework that combines CNN, BEM and FEM for tidal turbine rotor structural analysis is proposed. • Convolutional neural network (CNN) is used for identifying hydrodynamic performance of turbine blade section. • Blade element momentum (BEM) is for turbine prediction, while finite element method (FEM) is for structural analysis. • It shows great agreement with experiments, ensuring precise prediction of turbine blade deformations and hydrodynamics. • It outperforms benchmark software by more than 18 times, demonstrating exceptional computational efficiency. [ABSTRACT FROM AUTHOR]

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 reduces the overall cost of tidal energy. However, existing fluid–structure interaction (FSI) methods of resolving flow-induced deformation of the blades is computationally expensive, which poses an important challenge to effective morphing blade design. This paper presents a novel static FSI tool based on deep learning to cost-efficiently analyze the fluid–structure coupling of a hydrofoil. Specifically, adopting a convolutional neural network (CNN) to predict the fluid force and finite element method (FEM) to solve the solid structure response, a new CNN-FEM framework with an iterative scheme for solving the FSI problem is developed to achieve equilibrium between the fluid and structural forces. The new framework is used to predict the elastic deformation of the flexible blade section of the hydrofoil to demonstrate its effectiveness in the FSI evaluation. Comparison of the results to those produced by commercially developed software (i.e., Ansys Workbench) shows that this method yields extremely close prediction results of average equivalent stress and an accuracy of more than 92%. Moreover, it is 100 times more computationally efficient than the commercial Ansys Workbench software, requiring less than 3s for one-way FSI calculation. Taking advantage of this cost-effectiveness, the CNN-FEM can be used to achieve the accurate prediction of the deformation characteristics of the flexible hydrofoil under various flow scenarios that lay a foundation for advanced morphing blade design in the future. [ABSTRACT FROM AUTHOR]

Horizontal axis tidal turbines (HATT) conversion of ocean tidal waves into electricity represents a promising source of clean and sustainable energy. However, the widespread adoption of these turbines has been hindered by persistent challenges, primarily stemming from the high costs associated with their construction and maintenance; and efficient conversion of tidal energy. Addressing these challenges is paramount for propelling tidal turbine technology and ensuring its economic viability. This study focuses on the efficient conversion of tidal energy into electricity by optimization of composite material turbine blades which is a complex problem that spans multiple physical domains, including hydrodynamics (the study of water flow) and structural mechanics (the study of material behavior under loads). To tackle these multifaceted challenges, we introduce an innovative DLFSI (Deep learning fluid-structure interaction) model which represents a groundbreaking approach to predict and optimize the hydrodynamic and structural performance of tidal turbine blades. DLFSI leverages the power of convolutional neural networks (CNN) to recognize intricate geometric features of turbine blades rapidly and accurately. By seamlessly integrating the blade element momentum (BEM) theory and finite element method (FEM), the DLFSI model facilitates comprehensive predictions of how composite blades will perform in real-world conditions. With this approach, we have achieved substantial improvements in critical performance metrics such as the power coefficient (a measure of energy conversion efficiency) and the maximum equivalent stress (a key indicator of structural integrity). The innovative DLFSI model presented in this study holds the potential for practical application within the realm of tidal turbine design and is poised to catalyze the sustainable progression of renewable energy technologies. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper proposes a novel cost-effective framework that combines deep learning convolutional neural network (CNN) and blade element momentum (BEM) models for optimizing the performance of three-dimensional (3D) horizontal axis tidal turbines (HATTs). The framework employs signed distance function (SDF) to reconstruct the three-dimensional blade geometry, utilizes CNN to identify the hydrodynamic performance of each blade section, and ultimately predicts the performance of HATT using BEM. On top of the new CNN-BEM model, the rotor blade geometrical optimization by multi-objective non-dominated sorting genetic algorithm (NSGA-II) is carried out to obtain a better trade-off solution with the maximal power coefficient of turbine and minimal hydrodynamic load exerted on the blades. The results show that the CNN-BEM model has good agreement with experimental data and reduces prediction time by 46.7% compared to the conventional Xfoil-BEM model, while reducing general optimization time by 20.1%. The new model's cost-efficiency allows for a better trade-off solution with reduced hydrodynamic load while maintaining the power coefficient. Thus, the proposed model has the capability to deliver both accurate and fast prediction and optimization of HATT performance, making it a valuable tool for guiding the design of tidal turbines. • A cost-effective framework coupling convolutional neural network (CNN) and blade element momentum (BEM). • The new coupled CNN-BEM model is applied for tidal turbine blade optimization. • Trade-off with maximal power coefficient and minimal hydrodynamic load is achieved by multi-objective optimization. • The new model reduces prediction time by 46.7% and general optimization time by 20.1%. • The new model's cost-efficiency allows for better trade-off solution than conventional Xfoil-BEM model. [ABSTRACT FROM AUTHOR]