论文标题

拉格朗日模型用于湍流中的被动标量梯度

Lagrangian model for passive scalar gradients in turbulence

论文作者

Zhang, Xiaolong, Carbone, Maurizio, Bragg, Andrew D.

论文摘要

沿拉格朗日轨迹的流体速度梯度的方程紧随Navier-Stokes方程。但是,这样的方程式涉及两个术语,这些术语无法从所选拉格朗日路径的速度梯度确定:压力黑森和粘性拉普拉斯式。最近的一个模型使用最近的高斯田地变形(RDGF)闭合的多层次版本处理这些未封闭的术语(Johnson \&Meneveau,Phys。〜Rev。〜Fluids,2017年)。该模型与DNS数据非常共识,并适用于任意的Taylor Reynolds编号$ \rey_λ$。受此启发,我们开发了一种拉格朗日模型,用于各向同性湍流中的被动标量梯度。被动标量梯度的方程式还涉及拉格朗日框架中未锁定的项,即标量梯度扩散项,我们使用RDGF方法对其进行建模。但是,从该模型获得的统计数据与直接数值仿真(DNS)数据的比较发现,由于模型产生的错误波动而导致的实质性错误。我们通过将沿粒子局部轨迹历史记录的标量梯度产生的封闭近似信息纳入闭合近似信息来解决此缺陷。这种修改的模型可以预测标量梯度,其生产率以及与DNS数据非常吻合的应变率特征向量的比对。但是,尽管该模型产生的有效预测约为$ \rey_λ\约500 $,但除此之外,该模型会崩溃。

The equation for the fluid velocity gradient along a Lagrangian trajectory immediately follows from the Navier-Stokes equation. However, such an equation involves two terms that cannot be determined from the velocity gradient along the chosen Lagrangian path: the pressure Hessian and the viscous Laplacian. A recent model handles these unclosed terms using a multi-level version of the recent deformation of Gaussian fields (RDGF) closure (Johnson \& Meneveau, Phys.~Rev.~Fluids, 2017). This model is in remarkable agreement with DNS data and works for arbitrary Taylor Reynolds numbers $\Rey_λ$. Inspired by this, we develop a Lagrangian model for passive scalar gradients in isotropic turbulence. The equation for passive scalar gradients also involves an unclosed term in the Lagrangian frame, namely the scalar gradient diffusion term, which we model using the RDGF approach. However, comparisons of the statistics obtained from this model with direct numerical simulation (DNS) data reveal substantial errors due to erroneously large fluctuations generated by the model. We address this defect by incorporating into the closure approximation information regarding the scalar gradient production along the local trajectory history of the particle. This modified model makes predictions for the scalar gradients, their production rates, and alignments with the strain-rate eigenvectors that are in very good agreement with DNS data. However, while the model yields valid predictions up to around $\Rey_λ\approx 500$, beyond this, the model breaks down.

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