首页> 外文期刊>Ocean Engineering >Evaluation of turbulence models for estimating the wake region of artificial reefs using particle image velocimetry and computational fluid dynamics
【24h】

Evaluation of turbulence models for estimating the wake region of artificial reefs using particle image velocimetry and computational fluid dynamics

机译:使用粒子图像速度和计算流体动力学评估用于估计人工珊瑚礁唤醒区域的湍流模型

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Many studies have evaluated the performance of turbulence models used for computational fluid dynamics (CFD) analysis of artificial reefs (ARs), but an optimal model remains elusive, particularly in terms of wake length and areal estimation. Only a few models were used in previous studies and no report has yet investigated wake shape. We compared five turbulence models and verified the CFD results using particle image velocimetry (PIV). A standard k-epsilon model, renormalization group k-epsilon model, k-omega model, shear stress transportation k-omega model, and Reynolds stress model (RSM) were used. A down-scaled half-ball-type AR was devised and CFD and PIV analyses were performed. Three different inlet velocities (three Reynolds numbers, Re) were considered in each model, and the CFD and PIV results were compared. Wake lengths obtained in the PIV experiments were 1.05 L (Re = 2632), 0.90 L (Re = 5655) and 0.85 L (Re = 8782), respectively. The RSM well-reflected this, especially when Re = 2632 (difference +1%) and Re = 5655 (difference -2%). PIV revealed that all wake regions had unique shapes, reflecting flow divergence (local upwelling) from the end of the wake. Considering such divergent flow, the RSM optimally predicted the overall characteristics of the wake region.
机译:许多研究已经评估了用于计算流体动力学(CFD)的湍流模型的性能(CFD)分析人工珊瑚礁(ARS),但最佳模型仍然难以捉摸,特别是在唤醒长度和面积估计方面。在以前的研究中只使用了一些模型,并且没有报告尚未调查唤醒形状。我们比较了五种湍流模型,并使用粒子图像VELOCIMETRY(PIV)验证了CFD结果。使用标准K-EPSILON模型,重整化组K-EPSILON模型,K-OMEGA模型,剪切应力运输K-OMEGA模型和雷诺应力模型(RSM)。设计了一个下缩小的半球型AR,并进行了CFD和PIV分析。在每个模型中考虑了三种不同的入口速度(三个雷诺数,RE),并比较了CFD和PIV结果。在PIV实验中获得的唤醒长度分别为1.05L(RE = 2632),0.90L(RE = 5655)和0.85L(RE = 8782)。 RSM良好地反映了这一点,尤其是当重新= 2632(差异±1%)和重新= 5655(差异-2%)时。 PIV透露,所有唤醒区域都有独特的形状,从尾迹末端反映流动分歧(本地升值)。考虑到这种发散流量,RSM最佳地预测了唤醒区域的整体特征。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号