Aerodynamic performance and efficiency enhancement of a Savonius vertical axis wind turbine with Semi-Directional Curved Guide Vane, using CFD and optimization method

Ghafoorian, Farzad; Mirmotahari, Seyed Reza; Mehrpooya, Mehdi; Akhlaghi, Mohammad

doi:10.1007/s40430-024-05030-6

J Braz. Soc. Mech. Sci. Eng.

2024

DOI: 10.1007/s40430-024-05030-6

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Aerodynamic performance and efficiency enhancement of a Savonius vertical axis wind turbine with Semi-Directional Curved Guide Vane, using CFD and optimization method

Farzad Ghafoorian,

Seyed Reza Mirmotahari,

Mehdi Mehrpooya

et al.

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Cited by 10 publications

References 40 publications

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Experimental and numerical study on the impact of inlet temperature inhomogeneity on the aerodynamic performance of a three-stage turbine

Huang,

He,

Chi

et al. 2024

Energy

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Experimental and numerical study on the impact of inlet temperature inhomogeneity on the aerodynamic performance of a three-stage turbine

Huang,

He,

Chi

et al. 2024

Energy

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Optimization design on blade with bionic protuberances of horizontal axis wind turbine

Wei,

Li,

Chang

et al. 2024

Physics of Fluids

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The operating conditions of wind turbines are changeable, and the flow separation limits the power generation capacity. In order to improve the output power of wind turbines, based on the principle of bionics, the leading-edge protuberance (LEP) of the humpback whale flipper is introduced to the blade. The optimization of LEP parameters and distribution on the blade performance is studied. The shape and distribution of nonlinear, non-uniform LEPs are controlled by three design variables: amplitude, attenuation, and number. The optimization system consists of modules such as model generation, computational fluid dynamics, and genetic algorithm. The results show that the optimized bionic wind turbine can increase the output power by 3.95% under the rated condition. The optimized LEP structure can improve the performance of the trough sections without sacrificing part of the performance of peak sections, with a maximum increase of 20%. The theory of entropy production is used to analyze the location of energy loss and its impact on the wake. The distribution of the local entropy production rate of the wind turbine wake after optimization is improved. The power spectral density analysis shows that the LEPs cause different variation laws in the wake from the original wind turbine.

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A novel deep learning approach for flow field prediction around airfoils leveraging computational space representation

Caraccio,

Marseglia,

Lauria

et al. 2024

Physics of Fluids

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The optimization workflow for airfoil shapes, crucial for maximizing the lift/drag ratio, involves numerous computational fluid dynamics (CFD) simulations. Convolutional neural networks (CNNs) expedite this process by creating fast reduced order models. However, using uniformly spaced grids for CNN training is inadequate for complex scenarios, like those with wall-bounded turbulence, due to their inability to represent spatial variability effectively. A novel method addresses this limitation by preliminarily transforming flow fields into a new computational space, enabling concise representation of crucial information. The developed neural network architectures, comprising fully connected and transposed convolution layers, accurately infer transformed field maps for incompressible flow around a NACA0012 airfoil based on Reynolds (Re) number and angle of attack. In particular, the performance of a traditional transposed convolutional neural network (TCNN) architecture is compared with that of a conditional generative adversarial network (cGAN) with a TCNN generator. The most important aspect of the proposed spatial transformation lies in the ability to transfer the learned weights onto new geometries, allowing for training with fewer CFD data than would be required for training from scratch. By applying transfer learning to cGAN models trained with 15 cases for the prediction of velocity fields around the NACA4412 airfoil, the average error is up to 70% lower than training without weight transfer. This approach streamlines the optimization process by facilitating rapid model training and precise inference of flow fields, overcoming challenges posed by complex aerodynamic scenarios.

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Aerodynamic performance and efficiency enhancement of a Savonius vertical axis wind turbine with Semi-Directional Curved Guide Vane, using CFD and optimization method

Cited by 10 publications

References 40 publications

Experimental and numerical study on the impact of inlet temperature inhomogeneity on the aerodynamic performance of a three-stage turbine

Experimental and numerical study on the impact of inlet temperature inhomogeneity on the aerodynamic performance of a three-stage turbine

Optimization design on blade with bionic protuberances of horizontal axis wind turbine

A novel deep learning approach for flow field prediction around airfoils leveraging computational space representation

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