Stochastic weighted particle methods for population balance equations
Stochastic weighted particle methods for population balance equations
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DOI：
10.1016/j.jcp.2011.06.011
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发表时间：
2011-08
期刊：
影响因子：
--
通讯作者：
R. Patterson;W. Wagner;M. Kraft
中科院分区：
文献类型：
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作者：
R. Patterson;W. Wagner;M. Kraft
构造了一类凝聚权传递函数，每个函数都导出了一个随机粒子算法，用于种群平衡方程的数值处理。这些算法基于加权计算粒子系统，并且构造权重传递函数，使得计算粒子的数量在凝结事件期间不改变。这些算法还有助于模拟改变单个粒子的物理过程，例如生长或其他表面反应。四个成员的算法家庭已经通过比较简单的问题的解析解进行了数值验证。数值实验已进行复杂的层流预混火焰系统中的随机加权粒子方法的类成员进行了比较，彼此和直接模拟算法。两个加权算法已被证明提供的直接模拟算法的性能优势，在感兴趣的是集中在系统中的较大的颗粒的情况下。这种优势的程度取决于特定的系统和感兴趣的量。
A class of coagulation weight transfer functions is constructed, each member of which leads to a stochastic particle algorithm for the numerical treatment of population balance equations. These algorithms are based on systems of weighted computational particles and the weight transfer functions are constructed such that the number of computational particles does not change during coagulation events. The algorithms also facilitate the simulation of physical processes that change single particles, such as growth, or other surface reactions. Four members of the algorithm family have been numerically validated by comparison to analytic solutions to simple problems. Numerical experiments have been performed for complex laminar premixed flame systems in which members of the class of stochastic weighted particle methods were compared to each other and to a direct simulation algorithm. Two of the weighted algorithms have been shown to offer performance advantages over the direct simulation algorithm in situations where interest is focused on the larger particles in a system. The extent of this advantage depends on the particular system and on the quantities of interest.