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Simulation of the flow around a circular cylinder at Re = 3900 with Partially- Averaged Navier-Stokes equations

机译:使用部分平均的Navier-Stokes方程模拟Re = 3900时圆柱体周围的流动

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This study employs Partially-Averaged Navier-Stokes (PANS) equations to simulate the flow around a smooth circular cylinder at Reynolds number 3900. It intends to evaluate the importance of discretization and modelling errors on the accuracy of this mathematical model. Furthermore, the study addresses the effect of the physical resolution, or fraction of turbulence kinetic energy being modelled f(k), on the predictions accuracy. To this end, Validation exercises are carried out using five different values of f(k) which range from typical values for well resolved Scale-Resolving Simulations (f(k) = 0.25) to Reynolds-Averaged Navier-Stokes equations (f(k) = 1.00). Naturally, these exercises require the evaluation of numerical errors, i.e. Verification studies. Consequently, and taking advantage of the ability of PANS to enable the distinction between discretization and modelling errors, spatial and temporal grid refinement studies are carried out to assess the magnitude of the discretization error, as well as its dependence on f(k). The outcome confirms the ability of PANS, in combination with f(k) 0.50, to substantially decrease the modelling error when compared to f(k) = 1.00. However, the reduction of fk tends to increase the model dependence on the spatial and temporal resolution. It is demonstrated that similarly to the effect of the spatial and temporal grid resolution on the magnitude of the numerical error, the modelling error diminishes with the physical resolution (f(k) - 0). The convergence of the predictions with f(k) is also illustrated.
机译:本研究使用偏平均Navier-Stokes(PANS)方程来模拟雷诺数为3900时光滑圆柱体周围的流动。该研究旨在评估离散化和建模误差对此数学模型准确性的重要性。此外,该研究解决了物理分辨率或被建模为f(k)的湍流动能分数对预测精度的影响。为此,验证练习是使用f(k)的五个不同值进行的,这些值的范围从分辨率良好的比例解析模拟的典型值(f(k)<= 0.25)到雷诺平均Navier-Stokes方程(f( k)= 1.00)。当然,这些练习需要评估数字误差,即验证研究。因此,利用PANS能够区分离散化和建模误差的能力,进行了时空网格细化研究,以评估离散化误差的大小及其对f(k)的依赖性。结果证实,与f(k)= 1.00相比,结合f(k)<0.50,PANS能够显着降低建模误差。但是,fk的减小往往会增加模型对空间和时间分辨率的依赖性。结果表明,类似于空间和时间网格分辨率对数值误差幅度的影响,建模误差随物理分辨率的降低而减小(f(k)-> 0)。还说明了预测与f(k)的收敛性。

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