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Reynolds Stress Model for Viscoelastic Drag-Reducing Flow Induced by Polymer Solution

机译:聚合物溶液诱导的粘弹性减阻流的雷诺应力模型

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摘要

Viscoelasticity drag-reducing flow by polymer solution can reduce pumping energy of pipe flow significantly. One of the simulation manners is direct numerical simulation (DNS). However, the computational time is too long to accept in engineering. Turbulent model is a powerful tool to solve engineering problems because of its fast computational ability. However, its precision is usually low. To solve this problem, we introduce DNS to provide accurate data to construct a high-precision turbulent model. A Reynolds stress model for viscoelastic polymer drag-reducing flow is established. The rheological behavior of the drag-reducing flow is described by the Giesekus constitutive Equation. Compared with the DNS data, mean velocity, mean conformation tensor, drag reduction, and stresses are predicted accurately in low Reynolds numbers and Weissenberg numbers but worsen as the two numbers increase. The computational time of the Reynolds stress model (RSM) is only 1/120,960 of DNS, showing the advantage of computational speed.
机译:聚合物溶液的粘弹性减阻流可以显着降低管道流量的泵送能量。模拟方式之一是直接数值模拟(DNS)。但是,计算时间太长,无法在工程中接受。湍流模型具有快速的计算能力,是解决工程问题的有力工具。但是,其精度通常较低。为了解决这个问题,我们引入了DNS以提供准确的数据来构建高精度的湍流模型。建立了粘弹性聚合物减阻流的雷诺应力模型。减阻流的流变行为由Giesekus本构方程描述。与DNS数据相比,在低雷诺数和Weissenberg数下可以准确预测平均速度,平均构象张量,减阻和应力,但随着两个数的增加,平均速度,平均构象张量,减阻和应力将得到准确预测。雷诺应力模型(RSM)的计算时间仅为DNS的1 / 120,960,显示了计算速度的优势。

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