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An under-relaxation factor control method for accelerating the iteration convergence of flow field simulation

机译:加速流场模拟迭代收敛的欠松弛因子控制方法

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Purpose - This paper aims to accelerate the iteration convergence for elliptic fluid flow problems, so that an under-relaxation factor control method is developed. Design/methodology/approach - There should be an optimal under-relaxation factor that can result in the equivalence of the global residual norms of momentum equation u and momentum equation v. The two residual norms of the momentum equations will be equivalent through controlling the velocity under-relaxation factors, and then the iteration convergence can be accelerated. Two expressions (α = (α~0)~(β~γ) and α = (α~0)~((1/β)~γ) are proposed to adjust the values of under-relaxation factors for every n iterations. Findings - From the five preliminary computations it is found that the value of y can be larger than 1 and of n can be less than 5 for an open system, and the value of γ should be less than 1 and that of n should be larger than 10 for a closed system. These two pairs of parameters are then used in another five examples. It is found that the saving in CPU times is at least 43.9 percent for the closed system and 67.5 percent for the open system. Research limitations/implications - When the Re or Ra of the two-dimensional problems are low, this control method is feasible. More research work is needed in order to apply it in three-dimensional or high Re or Ra problems. Originality/value - This method is helpful for the acceleration of iteration convergence in simple problems, and is a preparation for the advanced research in complicated problems.
机译:目的-本文旨在加速椭圆流体流动问题的迭代收敛,从而开发了一种松弛因子控制方法。设计/方法/方法-应该有一个最佳的松弛松弛因子,该因子可以导致动量方程u和动量方程v的全局残差范数相等。通过控制速度,动量方程的两个残差范数将相等松弛因子,然后可以加快迭代收敛。提出了两个表达式(α=(α〜0)〜(β〜γ)和α=(α〜0)〜((1 /β)〜γ),以每n次迭代调整一次松弛因子的值。发现-从五个初步计算中可以发现,对于开放系统,y的值可以大于1,n的值可以小于5,并且γ的值应小于1,n的值应较大对于封闭系统,此参数对要比10多;然后在另外五个示例中使用这两对参数,发现封闭系统的CPU时间节省至少43.9%,开放系统至少节省67.5%。 -当二维问题的Re或Ra低时,此控制方法是可行的。需要更多的研究工作才能将其应用于三维或较高的Re或Ra问题。创新性/价值-此方法很有帮助为加速简单问题的迭代收敛,为复杂问题的高级研究做准备。

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