Turbulence modelling for depth-averaged velocity and boundary shear stress of a dense rigid grass bed open channel

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
S. Sahoo, J. R. Khuntia, K. Devi, B. S. Sai Prasad, Kishanjit Kumar Khatua
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引用次数: 0

Abstract

The present research focusses on a comparison of experimental and numerical approaches for flow over fixed artificial rigid grass bed channels. Various flow parameters like longitudinal velocity, depth-averaged velocity (DAV), boundary shear stress (BSS) and secondary current are analysed and compared with seven numerical models: standard, realizable and renormalization group (RNG) k–ε models and standard, shear stress transport (SST), generalized k–ω (GEKO) and BSL k–ω models. To evaluate the strength of the seven applied models, the error analysis has been performed. It is found that the RNG k–ε and SST k–ω models provided better results for both the DAV and BSS prediction, but the RNG k–ε model is found to be the most suitable for predicting the DAV and the SST k–ω model for BSS as compared to the other models. For the longitudinal velocity profiles, both the RNG k–ε and SST k–ω models are found to provide good agreement with experimental results at the centre of the channel, whereas the SST k–ω model is more accurate near the wall. Overall, the SST k–ω model has predicted the results with good accuracy for all the flow parameters considered in the present study.
稠密刚性草床明渠深度平均流速和边界剪应力的湍流模拟
本文的研究重点是比较了固定人工刚性草床通道上水流的实验方法和数值方法。对纵向速度、深度平均速度(DAV)、边界剪应力(BSS)和二次电流等流动参数与标准、可实现和重整化群(RNG) k -ε模型和标准、剪应力输运(SST)、广义k -ω (GEKO)和BSL k -ω模型进行了分析和比较。为了评估七个应用模型的强度,进行了误差分析。结果表明,RNG k -ε和SST k -ω模型对DAV和BSS的预测效果均较好,但RNG k -ε模型对DAV和SST k -ω模型的预测效果优于其他模型。对于纵向速度分布,RNG k -ε和SST k -ω模型在通道中心与实验结果吻合较好,而SST k -ω模型在通道壁面附近更为准确。总体而言,SST k -ω模型对本研究中考虑的所有流动参数的预测结果具有较好的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
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