A new weighted fraction Monte Carlo method for particle coagulation. Issue 9 (4th February 2021)

• Record Type: Journal Article
• Authors: Jiang, Xiao; Chan, Tat Leung
• Is Part Of: International journal of numerical methods for heat & fluid flow. Volume 31:Issue 9(2021)
• Page Start: 3009
• Page End: 3029
• Publication Date: 2021-02-04
• Subjects: Fraction function -- General dynamic equation -- Multi-Monte Carlo method -- Particle coagulation -- Weighted fraction Monte Carlo method; Heat -- Transmission -- Mathematics -- Periodicals; Fluid dynamics -- Mathematics -- Periodicals; 536.2
• DOI: 10.1108/HFF-07-2020-0449
• Languages: English
• ISSNs: 0961-5539

Abstract

Abstract : Purpose: The purpose of this study is to investigate the aerosol dynamics of the particle coagulation process using a newly developed weighted fraction Monte Carlo (WFMC) method. Design/methodology/approach: The weighted numerical particles are adopted in a similar manner to the multi-Monte Carlo (MMC) method, with the addition of a new fraction function (α). Probabilistic removal is also introduced to maintain a constant number scheme. Findings: Three typical cases with constant kernel, free-molecular coagulation kernel and different initial distributions for particle coagulation are simulated and validated. The results show an excellent agreement between the Monte Carlo (MC) method and the corresponding analytical solutions or sectional method results. Further numerical results show that the critical stochastic error in the newly proposed WFMC method is significantly reduced when compared with the traditional MMC method for higher-order moments with only a slight increase in computational cost. The particle size distribution is also found to extend for the larger size regime with the WFMC method, which is traditionally insufficient in the classical direct simulation MC and MMC methods. The effects of different fraction functions on the weight function are also investigated. Originality Value: Stochastic error is inevitable in MC simulations of aerosol dynamics. To minimize this critical stochastic error, many algorithms, such as MMC method, have been proposed. However, the weight of the numerical particles is not adjustable. This newly developed algorithm with an adjustable weight of the numerical particles can provide improved stochastic error reduction.