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The influence of turbulence model and two and three-dimensional domain selection on the simulated performance characteristics of vertical axis tidal turbines

机译:湍流模型以及二维和三维域选择对垂直轴潮汐涡轮机模拟性能特性的影响

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摘要

The influence of Computational Fluid Dynamics (CFD) modeling techniques on the accuracy of fixed pitch vertical axis turbine power output predictions was investigated. Using Two-Dimensional (2D) and Three-Dimensional (3D) models, as well as the Baseline-Reynolds Stress Model (BSL-RSM) and the k-omega Shear Stress Transport (k-omega SST) model in its fully turbulent and laminar-to-turbulent formulation, differences in power output modeling accuracy were evaluated against experimental results from literature. The highest correlation was found using a 3D domain model that fully resolved the boundary layer combined with the k-omega SST laminar-to-turbulent model. The turbulent 3D fully resolved boundary layer k-omega SST model also accurately predicted power output for most rotational rates, at a significantly reduced computational cost when compared to its laminar-to-turbulent formulation. The 3D fully resolved BSL-RSM model and 3D wall function boundary layer k-omega SST model were found to poorly simulate power output. Poor output predictions were also obtained using 2D domain k-omega SST models, as they were unable to account for blade tip and strut effects. The authors suggest that 3D domain fully turbulent k-omega SST models with fully resolved boundary layer meshes are used for predicting turbine power output given their accuracy and computational efficiency. (C) 2016 Elsevier Ltd. All rights reserved.
机译:研究了计算流体动力学(CFD)建模技术对固定螺距垂直轴涡轮机功率输出预测精度的影响。使用二维(2D)和三维(3D)模型,以及基线-雷诺应力模型(BSL-RSM)和k-omega剪切应力传递(k-omega SST)模型,层流到湍流的公式,功率输出建模精度的差异与文献中的实验结果进行了评估。使用完全解析边界层的3D域模型与k-omega SST层流到湍流模型相结合,发现了最高的相关性。与层流到湍流的公式相比,湍流的3D完全解析边界层k-ωSST模型还可以准确预测大多数转速下的功率输出,而计算成本却大大降低。发现3D完全解析的BSL-RSM模型和3D墙函数边界层k-omega SST模型不能很好地模拟功率输出。使用2D域k-omega SST模型也获得了较差的输出预测,因为它们无法解释叶片的尖端和支撑效果。作者认为,鉴于其精确性和计算效率,具有完全解析的边界层网格的3D域全湍动k-omega SST模型可用于预测涡轮机功率输出。 (C)2016 Elsevier Ltd.保留所有权利。

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