A numerical study for predicting the maximum horizontal distance of particles' dispersion during transient welding processes. (15th July 2023)

• Record Type: Journal Article
• Authors: Zhuang, Jiawei; Liu, Jianlin; Chen, Gengyang; Han, Kun; Jiang, Jie; Tian, Dongdong; Diao, Yongfa; Shen, Henggen
• Is Part Of: Journal of building engineering. Volume 71(2023)
• Publication Date: 2023-07-15
• Subjects: Transient welding processes -- Particles' dispersion distance -- Predictive model -- Multiple regression analysis -- Computational fluid dynamics; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2023.106449
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract : Particles from industrial processes are extremely harmful to workers' health and indoor environments. Acquiring the dispersion range of particles can help assess indoor air quality and optimize ventilation strategies. This study applies Computational Fluid Dynamics techniques to investigate the maximum horizontal distance (Δ R max ) of particles' dispersion during welding processes. The effects of releasing temperature ( T 0 ), releasing velocity ( v 0 ), operation time ( t 0 ), and particle diameter ( d p ) on particles' dispersion distance is analyzed. Multiple nonlinear regression analysis combining with the Box-Behnken design (BBD) method is adopted to develop the predictive model of Δ R max . The results show that the evolution of particles' dispersion distance can be divided into two and four stages in the horizontal and vertical directions. The influencing factors can shorten the duration of particles' dispersion but do not change their variation trend. Δ R max is positively correlated with T 0, v 0, and t 0, but negatively correlated with d p, and the combined effect of T 0 and t 0 can be converted into the influence of Q '. Moreover, the predictive model of Δ R max with respect to Q ′, v 0 and d p is developed based on the simulation results, and the greater Δ T, v 0 or t 0 is, the better its applicability is. These findings can help assess the exposure risk of particles and refine ventilation system for welding processes. Highlights: Maximum horizontal diffusion distance (Δ R max ) in welding processes is investigated. Δ R max increases with the increasing effective heat exchange amount ( Q ′). Multiple regression analysis is applied to develop and optimize the model for Δ R max . A predictive model between Δ R max and Q ′, v 0 and d p is obtained for future applications.