WeChat Mini Program
Old Version Features

    • Unsteady Effects of a Winglet on the Performance of Horizontal-Axis Tidal Turbine

    Dahai Zhang, Di Liu,Xiaodong Liu, Haiyang Xu,Yuankui Wang,Ran Bi,Peng Qian

    Renewable Energy（2024）

    Zhejiang Univ

    Cited 7|Views42
    Abstract
    A tip winglet can reduce the induced drag and the load fluctuation of a blade, considerably improving the hydrodynamic performance of a tidal turbine. Up to now, there are fewer studies on the unsteady effects of winglets, which limits the application of this design scheme. In this paper, a tidal turbine model is established based on computational fluid dynamics (CFD) and verified by a flume experiment. Then, unsteady effects on the turbine blade are analyzed considering different winglet lengths and cant angles. According to the study, the blade performance is optimal when the winglet cant angle is 45°, with about 11% efficiency improvement and about 8% increase in axial force; as the winglet length increases, the performance tends to further improve. In addition, this study demonstrates that the reason why a winglet can reduce blade load fluctuations is that the winglet takes the tip vortex generation position away from the blade rotation plane, instead of reducing the tip vortex intensity.
    More
    Translated text
    Key words
    Winglet,Tidal turbine,Unsteady effects,Tidal current energy,Computational fluid dynamics,Flume experiment
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined