稀疏拉格朗日模拟中模型改进及粒子密度研究

推进技术 ›› 2017, Vol. 38 ›› Issue (5): 1084-1092.

稀疏拉格朗日模拟中模型改进及粒子密度研究

徐栋,夏朝阳,叶桃红

中国科学技术大学热科学与能源工程系，安徽合肥 230027,中国科学技术大学热科学与能源工程系，安徽合肥 230027,中国科学技术大学热科学与能源工程系，安徽合肥 230027

发布日期:2021-08-15
作者简介:徐栋，男，硕士生，研究领域为湍流流动及燃烧数值模拟。E-mail: xud0526@mail.ustc.edu.cn 通讯作者:叶桃红，男，博士，副教授，研究领域为湍流燃烧数值模拟。
基金资助:
国家自然科学基金(91441117；51576182)。

Study on Model Modification and Particle Density in Sparse-Lagrangian Simulation

Department of Thermal Science and Energy Engineering，University of Science and Technology of China， Hefei 230027，China,Department of Thermal Science and Energy Engineering，University of Science and Technology of China， Hefei 230027，China and Department of Thermal Science and Energy Engineering，University of Science and Technology of China， Hefei 230027，China

Published:2021-08-15

摘要/Abstract

摘要： 基于多维条件映射(MMC)模型的稀疏拉格朗日粒子模拟是一种湍流混合及燃烧模拟方法。为进一步推进MMC模型的应用，针对现有MMC混合模型参数不精确、通用性差以及模拟结果受拉格朗日粒子密度影响较大的问题，以混合物分数为参考变量，改进了小尺度混合模型并修正了标量混合时间尺度；提出了与流场当地混合特性有关的拉格朗日粒子加密方法。为验证新模型及粒子加密的有效性，开展了湍流圆管射流混合的大涡模拟。计算结果表明改进的MMC模型具有较好的普适性及精确性；拉格朗日粒子经过加密后大大提高了模拟准确性。

关键词: MMC模型；稀疏拉格朗日粒子；概率密度函数；小尺度混合模型；大涡模拟

Abstract: Sparse-Lagrangian simulation，which is based on multiple mapping conditioning(MMC)model，is a method used to simulate turbulent mixing and combustion. In order to further promote the application of MMC model，several problems are studied that some parameters are of bad accuracy and poor universality in present MMC mixing model and simulation is greatly influenced by the number of Lagrangian-particles used in simulation. The small-scale mixing model and scalar mixing time-scale are modified by setting the mixture fraction as reference variable. Meanwhile，a method of adding Lagrangian-particles according to the local mixing characteristics of flow field is proposed. To evaluate the effectiveness of the modified MMC model and the particle refinement，the large eddy simulation of a turbulent round jet was investigated. The present results show that the modified MMC model is more universally applicable and accurate. Furthermore，the accuracy of sparse-Lagrangian simulation is greatly advanced after refinement.

Key words: Multiple mapping conditioning model；Sparse Lagrangian-particle；Probability density function；Small-scall mixing model；Large eddy simulation

徐栋,夏朝阳,叶桃红. 稀疏拉格朗日模拟中模型改进及粒子密度研究[J]. 推进技术, 2017, 38(5): 1084-1092.

XU Dong,XIA Zhao-yang,YE Tao-hong. Study on Model Modification and Particle Density in Sparse-Lagrangian Simulation[J]. Journal of Propulsion Technology, 2017, 38(5): 1084-1092.

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