改进的HLLC方法及其在Baer-Nunziato模型中的应用
发布时间：2019-03-20 09:17
【摘要】：波速估计对HLLC方法的计算影响较大,如果取值过小,则不能抓住波系特征;如果取值过大,则会引入较大的粘性。如何确定波速已有大量的研究,但是每种方法都有一定的适用范围。本文提出一种可避免估计波速的HLLC方法,并应用于两相流Baer-Nunziato模型模拟。本文方法与三种经典的波速估计方法进行比较,针对几类典型的B-N模型初值问题,不同的波速估计方法模拟能力差异较大,而本文方法可以自动确定适当的波速,因此得到较好的模拟结果。
[Abstract]:The wave velocity estimation has a great influence on the calculation of the HLLC method. If the value is too small, the wave system can not be grasped; if the value is too large, a large viscosity is introduced. How to determine the wave velocity has a great deal of research, but each method has a certain range of application. This paper presents an HLLC method which can avoid the estimation of wave velocity, and it is applied to the two-phase flow Baer-Nunzito model. The method of this paper is compared with the three classical wave velocity estimation methods, and the simulation ability of different wave velocity estimation methods is relatively large for several typical B-N models, and the method can automatically determine the appropriate wave velocity, so the better simulation results can be obtained.
【作者单位】： 北京应用物理与计算数学研究所;
【基金】：国家自然科学基金(91130021;11372052;11371069)资助项目
【分类号】：O359
，
本文编号：2444071
[Abstract]:The wave velocity estimation has a great influence on the calculation of the HLLC method. If the value is too small, the wave system can not be grasped; if the value is too large, a large viscosity is introduced. How to determine the wave velocity has a great deal of research, but each method has a certain range of application. This paper presents an HLLC method which can avoid the estimation of wave velocity, and it is applied to the two-phase flow Baer-Nunzito model. The method of this paper is compared with the three classical wave velocity estimation methods, and the simulation ability of different wave velocity estimation methods is relatively large for several typical B-N models, and the method can automatically determine the appropriate wave velocity, so the better simulation results can be obtained.
【作者单位】： 北京应用物理与计算数学研究所;
【基金】：国家自然科学基金(91130021;11372052;11371069)资助项目
【分类号】：O359
，
本文编号：2444071
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