论文标题
一种用于开发湍流模型的小涡流机制
A Small-Eddy-Dissipation Mechanism for Developing Turbulence Models
论文作者
论文摘要
Jin(Phys。Fluids,第31卷,2019年,第125102页)提出了一种新的湍流模拟方法,该方法比其他经典的湍流模型显示出更好的性能。它由用于计算参考解决方案的小涡流混合长度(SED-ML)模型组成,以及用于校正解决方案的参数扩展方法。在这项研究中,对该方法的机制进行了更深入的分析,以了解如何以高精度和低计算成本开发湍流模型。用RE_TAU = 821和2003的湍流流量以及腐烂的同质和各向同性湍流模拟,以证明新的湍流模拟方法如何工作。通过我们的分析,已经提出了用于开发湍流模型的小涡流(SED)机制。根据该机制,模型解决方案是Navier-Stokes方程的精确解决方案的渐近近似。该建模术语引入了人工耗散,可消散小涡流。湍流建模的目的是消散更多的小涡流,而无需定性地改变统计解决方案。我们希望在湍流更强的情况下可以消散更多的小涡流。这种机制不同于近似Reynolds的应力和LE的RANS,后者近似于子网格尺度(SGS)运动,而它则更精确地解释了湍流建模的物理学。开发了具有两个阻尼功能的修饰混合长度,以识别湍流的特征长度,从而导致SED-ML模型。使用线性扩展可以进一步提高模拟精度。我们的数值结果表明,SED-ML模型符合SED机制。这可能解释了为什么新方法比RANS更准确,而其计算成本比LES较低。
Jin (Phys. Fluids, vol. 31, 2019, p. 125102) proposed a new turbulence simulation method which shows better performance than other classic turbulence models. It is composed of a small-eddy-dissipation mixing length (SED-ML) model for calculating the reference solution and a parameter extension method for correcting the solution. The mechanism of this method is more deeply analyzed in this study to find out how to develop a turbulence model with a high accuracy and a low computational cost. The turbulent channel flows with Re_tau=821 and 2003 and decaying homogenous and isotropic turbulence are simulated to demonstrate how the new turbulence simulation method works. The small-eddy-dissipation (SED) mechanism for developing turbulence models has been proposed through our analysis. According to this mechanism, the model solution is an asymptotic approximation of the exact solution of the Navier-Stokes equations. The modeling term introduces an artificial dissipation which dissipates small eddies. The purpose of turbulence modeling is to dissipate more small eddies without changing the statistical solution qualitatively. We expect more small eddies can be dissipated where the turbulence is stronger. This mechanism is different from RANS which approximates the Reynolds stresses and LES which approximates the sub-grid-scale (SGS) motions, while it interprets the physics of turbulence modeling more precisely. A modified mixing length with two damping functions is developed to identify the characteristic length of turbulence, leading to the SED-ML model. The simulation accuracy can be further improved using a linear extension. Our numerical results show that the SED-ML model is in accordance with the SED mechanism. This might explain why the new method is more accurate than RANS while it requires a lower computational cost than LES.