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An adaptive interface sharpening methodology for compressible multiphase flows

机译:可压缩多相流的自适应界面锐化方法

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A numerical methodology is developed to combine the advantages of adaptive mesh refinement (AMR) and interface sharpening technique. A five-equation compressible multiphase model with capillary and viscous effects is considered. The solver employs a wave propagation method along with the Tangent of Hyperbola for INterface Capturing (THINC) scheme. To calculate interface normal and curvature, an implicit filtering method is introduced which transforms the sharpened volume fraction variable to a variant with smoothed distribution. The accuracy and performance of our method is assessed through its application to multiple compressible interface problems ranging from high Mach number shock-interface interaction to gravity driven flows with viscosity and surface tension effects. The results obtained for one-dimensional shock-tube and tin-air interaction problems are shown to compare well with analytical data. The flow patterns predicted for shock-bubble interaction and under-water explosion match those from the landmark experimental and numerical studies. Furthermore, the trends and values predicted for spike position in the Rayleigh-Taylor instability and bubble's center location in bubble rising are consistent with those found in literature. Particularly, it is shown that the coupled AMR-THINC method remarkably prevents excessive interface smearing and captures delicate interfacial features such as shear-induced instabilities encountered in shock-bubble interaction. (C) 2016 Elsevier Ltd. All rights reserved.
机译:开发了一种数值方法,以结合自适应网格细化(AMR)和界面锐化技术的优点。考虑具有毛细作用和粘性作用的五方程可压缩多相模型。该求解器采用了一种波传播方法以及用于曲面捕捉的双曲线正切(THINC)方案。为了计算界面法线和曲率,引入了一种隐式滤波方法,该方法将锐化的体积分数变量转换为具有平滑分布的变量。通过将其应用于多种可压缩界面问题,从高马赫数激波-界面相互作用到重力驱动的流动(具有粘度和表面张力效应),评估了我们方法的准确性和性能。一维激波管和锡-空气相互作用问题获得的结果显示与分析数据进行了很好的比较。预测的冲击气泡相互作用和水下爆炸的流型与具有里程碑意义的实验和数值研究相匹配。此外,在瑞利-泰勒不稳定性中的峰值位置和气泡上升中气泡中心位置的趋势和预测值与文献中的一致。特别是,已表明,耦合的AMR-THINC方法可显着防止过度的界面涂抹,并捕获微妙的界面特征,例如在冲击气泡相互作用中遇到的剪切诱导的不稳定性。 (C)2016 Elsevier Ltd.保留所有权利。

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