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Monte Carlo simulation for simultaneous particle coagulation and deposition

机译:蒙特卡洛模拟同时进行粒子凝结和沉积

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摘要

The process of dynamic evolution in dispersed systems due to simultaneous particle coagulation and deposition is described mathematically by general dynamic equation (GDE). Monte Carlo (MC) method is an important approach of numerical solutions of GDE. However, constant-volume MC method exhibits the contradictory of low computation cost and high computation precision owing to the fluctuation of the number of simulation particles; constant-number MC method can hardly be applied to engineering application and general scientific quantitative analysis due to the continual contraction or expansion of computation domain. In addition, the two MC methods depend closely on the "subsystem" hypothesis, which constraints their expansibility and the scope of application, A new multi-Monte Carlo (MMC) method is promoted to take account of GDE for simultaneous particle coagulation and deposition. MMC method introduces the concept of "weighted fictitious particle" and is based on the "time-driven" technique. Furthermore MMC method maintains synchronously the computational domain and the total number of fictitious particles, which results in the latent expansibility of simulation for boundary condition, the space evolution of particle size distribution and even particle dynamics. The simulation results of MMC method for two special cases in which analytical solutions exist agree with analytical solutions well, which proves that MMC method has high and stable computational precision and low computation cost because of the constant and limited number of fictitious particles. Lastly the source of numerical error and the relative error of MMC method are analyzed, respectively.
机译:通过通用动力学方程(GDE)在数学上描述了由于同时发生的粒子凝结和沉积而导致的分散系统中动态演化的过程。蒙特卡洛(MC)方法是GDE数值解的重要方法。但是,由于模拟粒子数量的波动,定容MC法具有计算成本低,计算精度高的矛盾。由于计算域的不断缩小或扩展,定数MC方法几乎不能应用于工程应用和一般的科学定量分析。另外,这两种MC方法都紧密依赖于“子系统”假说,这限制了它们的可扩展性和适用范围,因此提出了一种新的Multi-Monte Carlo(MMC)方法,该方法考虑了GDE同时进行粒子凝结和沉积。 MMC方法引入了“加权虚拟粒子”的概念,并且基于“时间驱动”技术。此外,MMC方法可同时保持计算域和虚拟粒子的总数,从而为边界条件,粒度分布的空间演化甚至粒子动力学提供了潜在的模拟扩展性。在两种存在解析解的特殊情况下,MMC方法的仿真结果与解析解吻合得很好,证明了由于虚拟粒子数量恒定且数量有限,MMC方法具有较高的计算精度和较低的计算精度。最后分别分析了数值误差的来源和MMC方法的相对误差。

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