Direct numerical simulations of aerodynamic performance of wind turbine aerofoil by considering the blades active vibrations

Nakhchi, M.E.; Naung, Shine Win; Dala, Laurent; Rahmati, Mohammad

doi:10.1016/j.renene.2022.04.052

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…Johnson et al [9] proposed an adaptable projection-based method to superimpose complex ice configurations onto a baseline structure and provided an efficient methodology to include ice accretion in the high-fidelity geometric shell analysis of a realistic wind turbine blade. Nakhchi et al [10] investigated the aerodynamic performance of the horizontal-axis wind turbine blades by considering the flap-wise oscillations with the direct numerical simulations method. They analyzed the details of flow structure by considering the realistic behavior of the wind turbine blade structure with natural vibration frequencies.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of the Blade Deformation on Wind Turbine Output Power in the Framework of a Bidirectional Fluid-Structure Interaction Model

Yuan¹,

Liu²,

Li³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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The blades of large-scale wind turbines can obviously deform during operation, and such a deformation can affect the wind turbine's output power to a certain extent. In order to shed some light on this phenomenon, for which limited information is available in the literature, a bidirectional fluid-structure interaction (FSI) numerical model is employed in this work. In particular, a 5 MW large-scale wind turbine designed by the National Renewable Energy Laboratory (NREL) of the United States is considered as a testbed. The research results show that blades' deformation can increase the wind turbine's output power by 135 kW at rated working conditions. Compared with the outcomes of the simulations conducted using the model with no blade deformation, the results obtained with the FSI model are closer to the experimental data. It is concluded that the bidirectional FSI model can replicate the working conditions of wind turbines with great fidelity, thereby providing an effective method for wind turbine design and optimization.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of the Blade Deformation on Wind Turbine Output Power in the Framework of a Bidirectional Fluid-Structure Interaction Model

Yuan¹,

Liu²,

Li³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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“…In this respect, static torsional tests have been performed in [ 23 ], which, together with finite element analyses, provide conclusive results on the torsional modes. To determine the effect of blade vibration on aerodynamic performance, high-resolution numerical simulations of an oscillating wind turbine blade were performed in [ 24 , 25 ]. It is found that pressure fluctuations are amplified by the oscillation, thus leading to a greater variation in fatigue loading.…”

Section: Introductionmentioning

confidence: 99%

Structural Testing by Torsion of Scalable Wind Turbine Blades

et al. 2022

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In life service, the wind turbine blades are subjected to compound loading: torsion, bending, and traction, all these resulting in the occurrence of normal and tangential stresses. At some points, the equivalent stresses, due to overlapping effects provided by normal and shear stresses, can have high values, close to those for which the structure can reach to the failure point. If the effects of erosion and clashes with foreign bodies are added, the structure of the blade may lose its integrity. Considering both the complex shape of the blade and internal structure used, the mechanical behavior of the blade, such as the rigidity and resistance along the length of the blade, are usually determined with some uncertainty. This paper presents the results obtained in the non-destructive tests at static torsion of a scalable wind turbine blade. The objective of the paper was to determine the variation of the equivalent stress in the most stressed points of the blade, in relation to the torques applied. To determine the points with the highest stress, a finite element analysis was performed on the scalable wind turbine blade. Electrotensiometric transducers were mounted at different points of the blade, determining the main stresses in the respective points, as well as their variation during the torsion test, by subsequent calculations. The determinations were performed by applying the torque in both senses, in relation to the blade axis, thus concluding the values of the equivalent stress in the two cases.

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“…Therefore, computer-based predictive modeling of composites manufacturing is essential for controlling the process, shortening the production cycle, and optimizing material properties to meet the requirements of various applications. Although computer models have been successfully used to predict various physical processes in many applications (e.g., aerospace [7,8], structure [9], biomechanics [10,11], etc. ), the physics-based computational modeling for composite manufacturing is a less explored area, and there are only very few studies on modeling the impregnation or pyrolysis processes in past decades [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Physics-integrated Neural Differentiable (PiNDiff) Model for Composites Manufacturing

Akhare

Luo

Wang

2022

Preprint

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Various manufacturing technologies are being developed to improve the manufacturing of composites owing to their low weight and high performance. The mechanical properties of the composites depend on various variables and parameters of the manufacturing process, which are challenging, if not impossible, to determine and optimize experimentally. Traditional first-principle modeling approaches are not accessible due to the complex physics involved. A hybrid model that combines incomplete physics knowledge with available measurement data within a differentiable programming framework opens up new avenues to tackle the challenges. In this work, a physics-integrated neural differentiable (PiNDiff) model is developed, where the partially known physics is integrated into the recurrent network architecture to enable effective learning and generalization. The merit and potential of the proposed method have been demonstrated in modeling the curing process of thick thermoset composite laminates, whose governing physics is partially given. The proposed PiNDiff model shows the capability to learn unknown physics from the limited, indirect data and, meanwhile, can be used to infer unobserved variables and parameters. The performance of the PiNDiff model has been compared with two state-of-the-art (SOTA) black-box deep learning models, and its advantages over the purely data-driven models and first-principles physics-based models have been discussed in detail. The demonstrated PiNDiff strategy may provide a general strategy to model phenomena where physics is only partially known and sparse, indirect data are available.

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Direct numerical simulations of aerodynamic performance of wind turbine aerofoil by considering the blades active vibrations

Cited by 11 publications

References 37 publications

Analysis of the Influence of the Blade Deformation on Wind Turbine Output Power in the Framework of a Bidirectional Fluid-Structure Interaction Model

Analysis of the Influence of the Blade Deformation on Wind Turbine Output Power in the Framework of a Bidirectional Fluid-Structure Interaction Model

Structural Testing by Torsion of Scalable Wind Turbine Blades

Physics-integrated Neural Differentiable (PiNDiff) Model for Composites Manufacturing

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