Investigation of wake characteristics of the MEXICO wind turbine using lattice Boltzmann method

Li, Linmin; Xu, Chunxiao; Shi, Chen; Han, Xingxing; Shen, Wen Zhong

doi:10.1002/we.2560

Cited by 12 publications

(12 citation statements)

References 49 publications

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“…With the continuous development of DL, inspired by the local perception in the study of the cat brain visual cortex, convolutional neural network (CNN) improves the deep neural network through sparse connections and parameter sharing 24 . Of course, DL involves not only the methods mentioned above but also deep belief nets (DBN), deep Boltzmann machines (DBM), and so on 25 . SDAE, LSTM, and BLSTM neural networks are applied in this paper.…”

Section: Introductionmentioning

confidence: 99%

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A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm

et al. 2022

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Wind power prediction (WPP) is extremely important in promoting the power grid's consumption of wind power. To improve the accuracy of WPP, a three-stage multiensemble short-term WPP method based on ensemble learning and deep learning is proposed in this paper. In the first stage, variational mode decomposition and wavelet transform were applied to decompose the original data sequence into different frequency bands. In the second stage, based on the decomposition sequences, the stacked denoising autoencoder (SDAE), long short-term memory (LSTM), and bidirectional long short-term memory (BLSTM) were used to predict the sequences; and 42 submodels were obtained. In the third stage, a support vector machine (SVM) was applied to give weight to each submodel to obtain the final ensemble prediction results. Based on three-stage integration, a new multi-integration model is proposed that repeats the third-stage integration operation. A case study is presented to verify the effectiveness and superiority of the proposed three-stage multi-integration WPP method. The normalized root mean square error (NRMSE) decreased by 0.0343 compared with LSTM, decreased by 0.0336 compared with BLSTM, and decreased by 0.0323 compared with SDAE, which demonstrated the effectiveness of the proposed new multistage ensemble and deep learning WPP method.

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Section: Introductionmentioning

confidence: 99%

“…24 Of course, DL involves not only the methods mentioned above but also deep belief nets (DBN), deep Boltzmann machines (DBM), and so on. 25 SDAE, LSTM, and BLSTM neural networks are applied in this paper.…”

mentioning

confidence: 99%

A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm

et al. 2022

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“…The wake velocity results obtained from this scheme proved to be consistent with the experimental measurements. Recently, Li et al [15] used the MEXICO rotor and employed a Lattice Boltzmann/ LES method for the investigation of wake characteristics. The evolution of velocity and wake characteristics agreed well with the experiments.…”

Section: Introductionmentioning

confidence: 99%

On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III

Purohit

Kabir

2021

Energies

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This work presents a comparison study of the CFD modeling with two different turbulence modeling approaches viz. unsteady RANS and LES, on a full-scale model of the (New) MEXICO rotor wind turbine. The main emphasis of the paper is on the rotor and wake aerodynamics. Simulations are carried out for the three wind speeds considered in the MEXICO experiment (10, 15, and 24 ms−1). The results of uRANS and LES are compared against the (New) MEXICO experimental measurements of pressure distributions, axial, radial, and azimuth traverse of three velocity components. The near wake characteristics and vorticity are also analyzed. The pressure distribution results show that the LES can predict the onset of flow separation more accurately than uRANS when the turbine operates in the stall condition. The LES can compute the flow structures in wake significantly better than the uRANS for the stall condition of the blade. For the design condition, the mean absolute error in axial and radial velocity components along radial traverse is less than 10% for both the modeling approaches, whereas tangential component error is less than 2% from the LES approach. The results also reveal that wake recovers faster in the uRANS approach, requiring further research of the far wake region using both CFD modeling approaches.

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“…7 The tip speed ratio is a very important parameter to describe the characteristics of wind turbines. 8,9 The tip speed ratio is defined as…”

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confidence: 99%

Effect of the tip speed ratio on the wake characteristics of wind turbines using LBM‐LES

Cui,

Mao,

Wang

et al. 2024

Energy Science & Engineering

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In this paper, the wake characteristics of Zell 2000 wind turbine under different tip velocity ratios are studied by using the lattice Boltzmann method and large eddy simulation. The adaptive mesh refinement method is performed to capture the fine flow structure and wake characteristics development. In this paper, we mainly focus on the effect of the tip speed ratio on the flow structure and unsteady characteristics of wind turbine wake. The three‐dimensional flow vorticity structures, the section vorticity diagram, the pressure fluctuation of wake and the lift coefficient of wind turbine wake are utilized to explore the effect of the tip speed ratio on the unsteady physics mechanism of wind turbine wake. With the increase of the tip speed ratio, the distance between two adjacent vortex rings along the axial direction gradually decreases, as the position of the broken vortex circles gradually approaches the center of the blade, separated vortexes are rapidly generated, and the coherent structure appears closer to the wind turbine. A relationship is established between the tip speed ratios and the positions of the broken vortex circles. It is further found that the dominant frequency amplitude gradually increases with the increase of tip speed ratio and the pressure amplitude spectra of vortex increases with the decrease of the distance between the wake and the center of the blade axis. The above series of studies can provide significant physical insight into deep understanding the influence of the tip speed ratio on the wake characteristics of wind turbines.

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Investigation of wake characteristics of the MEXICO wind turbine using lattice Boltzmann method

Cited by 12 publications

References 49 publications

A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm

A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm

On the Accuracy of uRANS and LES-Based CFD Modeling Approaches for Rotor and Wake Aerodynamics of the (New) MEXICO Wind Turbine Rotor Phase-III

Effect of the tip speed ratio on the wake characteristics of wind turbines using LBM‐LES

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