潮汐涡轮机基准项目:第一阶段-稳定流量盲预测

R. Willden, Xiaosheng Chen, C.R. Vogel
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引用次数: 0

摘要

本文介绍了由英国EPSRC和Supergen ORE Hub资助的潮汐涡轮机基准项目的第一个盲预测阶段。在第一阶段,只考虑了稳定的流动条件,即低湍流和高湍流(3.1%)水平。在盲预测阶段之前,进行了一个大型实验室规模的实验,在明确的流动条件下,在有和没有上游湍流网格的情况下,将一个高度仪表化的1.6m直径潮汐转子拖曳通过一个大型拖曳槽。测试活动和转子设计的细节作为社区盲预测练习的一部分发布。参与者被邀请使用一系列工程建模方法来模拟涡轮机的性能和负载。来自学术界和工业界的12个小组提交了26份解决方案,其中包括叶片解析计算流体动力学,执行器线,边界积分元方法,涡旋方法和工程叶片单元动量方法。实验和盲目预测之间的比较非常积极,有助于为模型提供验证和不确定性估计,同时也验证了实验测试本身。实验表明,实验涡轮机数据提供了一个强大的数据集,研究人员和设计工程师可以根据该数据集测试他们的模型和实现,以确保他们的过程中的鲁棒性,有助于减少不确定性,并增加工程过程的信心。此外,数据集为建模者评估和改进方法提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions
This paper presents the first blind prediction stage of the Tidal Turbine Benchmarking Project being conducted and funded by the UK's EPSRC and Supergen ORE Hub. In this first stage, only steady flow conditions, at low and elevated turbulence (3.1%) levels, were considered. Prior to the blind prediction stage, a large laboratory scale experiment was conducted in which a highly instrumented 1.6m diameter tidal rotor was towed through a large towing tank in well-defined flow conditions with and without an upstream turbulence grid. Details of the test campaign and rotor design were released as part of this community blind prediction exercise. Participants were invited to use a range of engineering modelling approaches to simulate the performance and loads of the turbine. 26 submissions were received from 12 groups from across academia and industry using solution techniques ranging from blade resolved computational fluid dynamics through actuator line, boundary integral element methods, vortex methods to engineering Blade Element Momentum methods. The comparisons between experiments and blind predictions were extremely positive helping to provide validation and uncertainty estimates for the models, but also validating the experimental tests themselves. The exercise demonstrated that the experimental turbine data provides a robust data set against which researchers and design engineers can test their models and implementations to ensure robustness in their processes, helping to reduce uncertainty and provide increased confidence in engineering processes. Furthermore, the data set provides the basis by which modellers can evaluate and refine approaches.
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