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首页> 外文期刊>SIAM Journal on Scientific Computing >AN ASYMPTOTIC FITTING FINITE ELEMENT METHOD WITH EXPONENTIAL MESH REFINEMENT FOR ACCURATE COMPUTATION OF CORNER EDDIES IN VISCOUS FLOWS
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AN ASYMPTOTIC FITTING FINITE ELEMENT METHOD WITH EXPONENTIAL MESH REFINEMENT FOR ACCURATE COMPUTATION OF CORNER EDDIES IN VISCOUS FLOWS

机译:指数网格细化的渐近拟合有限元法用于粘性流中角涡的精确计算。

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It is well known that any viscous fluid flow near a corner consists of infinite series of eddies with decreasing size and intensity, unless the angle is larger than a certain critical angle [H. K. Moffat, J. Fluid Mech., 18 (1964), pp. 1-18]. The objective of the current work is to simulate such infinite series of eddies occurring in steady flows in domains with corners. The problem is approached by high-order finite element method with exponential mesh refinement near the corners, coupled with analytical asymptotics of the flow near the corners. Such approach allows one to compute position and intensity of the eddies near the corners in addition to the other main features of the flow. The method was tested on the problem of the lid-driven cavity flow as well as on the problem of the backward-facing step flow. The results of computations of the lid-driven cavity problem show that the proposed method computes the central eddy with accuracy comparable to the best of existing methods and is more accurate for computing the corner eddies than the existing methods. The results also indicate that the relative error of finding the eddies' intensity and position decreases uniformly for all the eddies as the mesh is refined (i.e., the relative error in computation of different eddies does not depend on their size).
机译:众所周知,除非角大于某个临界角[H],否则在拐角附近的任何粘性流体流动都将由无穷系列的涡流组成,涡流的大小和强度都会减小。 K.Moffat,J.Fluid Mech。,18(1964),第1-18页。当前工作的目的是模拟在具有角的区域中稳定流动中出现的这种无限系列的涡流。该问题是通过高阶有限元方法(在拐角处采用指数网格细化方法)以及拐角附近流的解析渐近性来解决的。除了流的其他主要特征之外,这种方法还允许计算拐角附近涡流的位置和强度。在盖驱动腔流动以及后向阶梯流动问题上对该方法进行了测试。盖驱动腔问题的计算结果表明,所提出的方法计算中心涡流的精度与现有方法的最佳相当,并且比现有方法更准确地计算角涡。结果还表明,随着网格的细化,发现所有涡流的涡流强度和位置的相对误差会均匀减小(即,计算不同涡流时的相对误差不取决于它们的大小)。

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