使用自适应网格细化提高了空化流的精度

Lucas Legagneux, Maurits van den Boogaard, Benoit Mallol
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摘要

通过增加流速,从而降低静压,可以产生将箔片容器提升到水面以上所需的高升力。当达到饱和点时,随着蒸汽的形成,开始出现空化现象。根据压降,可以观察到汽腔与表面的循环分离。这种现象被称为脱落,对快速船只上的水翼的性能至关重要。表征这些空化动力学对设计至关重要,但对这种高度湍流、动态和不稳定的两相流进行建模是一项挑战。在非空化流动的复杂性之上,需要适当地捕获低压区域和蒸汽腔。这通常需要一个非常精细的网格,其中包含大体积的小细胞,以确保离散度足够高,以便在整个循环过程中捕获蒸汽腔。此外,使用(I)(D)DES和LES等计算量大的模型来研究复杂的空化案例在文献中越来越普遍。目前的工作是研究在RANS仿真中使用Fine™Marine中的自适应网格细化(AGR),以降低计算成本,同时提高精度。自动高频网格适应确保在任何给定时间的最佳数量的细胞,在任何给定位置的正确细化。它是通过使用界面捕获准则(在水和蒸汽之间)结合压力和速度的Hessians来实现的。该方法先前成功应用于船舶阻力等稳态情况[1]和箔片通风中存在的非定常情况[2],为应用于空化铺平了道路。为了验证结果的质量,我们对著名的Delft Twist 11试验用例进行了模拟,并与Bouziad[3]和Foeth[4]进行的两次隧道试验以及文献中的计算结果进行了比较。文中所观察到的动力学结果与实验结果非常吻合
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved accuracy in cavitating flows using adaptive grid refinement
The high lift required to raise foiling vessels above water is generated by increasing the flow velocity, thus decreasing the static pressure. If the saturation point is reached, cavitation begins to appear with the formation of vapor. Depending on the pressure drop, cyclic detachments of the vapor cavities from the surface can be observed. This phenomenon is referred to as shedding and is critical to the performance of a hydrofoil on fast vessels. Characterizing these cavitation dynamics is essential for the design, but modeling this highly turbulent, dynamic, and unstable two-phase flow is a challenge. On top of the complexity of the non-cavitating flow, there is the need to properly capture low pressure regions and vapor cavities. This typically requires a very fine mesh with small cells in a large volume to make sure the discretization is high enough to capture vapor cavities during the entire cycle. Additionally, the use of computationally heavy models such as (I)(D)DES and LES is increasingly common in the literature to study complex cavitation cases. Present work studies the use of Adaptive Grid Refinement (AGR) in Fine™ Marine on RANS simulations, to reduce the computational cost while increasing the precision. The automatic high-frequency mesh adaptation ensures an optimum number of cells at any given time, with the right refinement at any given location. It is achieved by using an interface-capturing criterion (between water and vapor) combined with the Hessians of both pressure and velocity. The previously successful application of this method on steady-state cases like ship resistance [1] and on the unsteadiness present in foil ventilation [2] paved the way for the application to cavitation. To validate the quality of the results, simulations have been performed on the well-known Delft Twist 11 test case and compared with two campaigns of tunnel testing carried out by Bouziad [3] and Foeth [4], as well as computational results from literature. Excellent agreement is obtained with the dynamics observed in
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