Numerical Simulations to Determine the Most Appropriate Welding and Ventilation Conditions in Small Enclosed Workspace.

Shibata, Nobuyuki; Tanaka, Masaki; Ojima, Jun; Iwasaki, Takeshi

doi:10.2486/indhealth.38.356

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…However, a good CFD model, which typically contains some empirical features, captures the relevant physics (2) of contaminant transport, generating plausible concentration fields that provide meaningful comparisons to simple deterministic models. As other researchers continue to demonstrate good agreement between measured concentration fields and fields simulated using CFD, (3,4) the potential of CFD as a means of simple model evaluation becomes evident. The effectiveness of this approach stems from the relative ease with which variables such as room shape and supply air inlet velocity can be changed and detailed spatial concentration information obtained.…”

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confidence: 91%

Comparison of Emission Models With Computational Fluid Dynamic Simulation and a Proposed Improved Model

et al. 2003

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Understanding source behavior is important in controlling exposure to airborne contaminants. Industrial hygienists are often asked to infer emission information from room concentration data. This is not easily done, but models that make simplifying assumptions regarding contaminant transport are frequently used. The errors resulting from these assumptions are not yet well understood. This study compares emission estimates from the single-zone completely mixed (CM-1), two-zone completely mixed (CM-2), and uniform diffusivity (UD) models with the emissions set as boundary conditions in computational fluid dynamic (CFD) simulations of a workplace. The room airflow and concentration fields were computed using Fluent 4. These numerical experiments were factorial combinations of three source locations, five receptor locations, three dilution airflow rates, and two generation rate profiles, constant and time-varying. The aim was to compute plausible concentration fields, not to simulate exactly the processes in a real workroom. Thus, error is defined here as the difference between model and CFD predictions. For the steady-state case the UD model had the lowest error. When the source near-field contained the breathing zone receptor, the CM-2 model was applied. Then, in decreasing agreement with CFD were UD, CM-2, and CM-1. Averaging over all source and receptor locations (CM-2 applied for only one), in decreasing order of agreement with CFD were UD, CM-1, and CM-2. Source and receptor location had large effects on emission estimates using the CM-1 model and some effect using the UD model. A location-specific mixing factor (location factor) derived from steady-state concentration gradients was used to build a more accurate time-dependent emission model, CM-L. Total mass emitted from a time-varying source was modeled most accurately by CM-L, followed by CM-1 and CM-2.

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confidence: 91%

Comparison of Emission Models With Computational Fluid Dynamic Simulation and a Proposed Improved Model

et al. 2003

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“…Many studies have concluded that particles tend to follow the airflow when moving because of small diameters. [37][38][39][40][41] Therefore, the turbulence effect of fluid can significantly influence particles' motion trajectory through momentum transfer. For the motion of particles in turbulence, Salmanzadeh et al 42 have studied the turbulent thermal plume on particle concentrations in the breathing zone and entrainment of particles emitted from different sources near the floor.…”

Section: Introductionmentioning

confidence: 99%

Diffusion characteristics of the industrial submicron particle under Brownian motion and turbulent diffusion

Wang

Yu³

et al. 2021

Indoor and Built Environment

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The industrial release of submicron aerosol particles at workplace could cause undue health effect on workers. To effectively capture and remove airborne particles, we need to study the characteristics of various interactive particle motion forces (drag force, Brownian force, Saffman lift force, etc.) and the dispersion of these aerosol particles in indoor air. In this study, the dominant force of submicron particles was determined by calculating the acting forces with different particle sizes. Then, a Discrete Particle Model (DPM) was used to calculate the trajectory of particle movement in turbulent thermal plume flow. Horizontal dispersity ( DH) was defined to evaluate the horizontal diffusion of the particulate matter. The impact of different particle diameters, heat source temperatures and initial relative velocities on DH was investigated. This study showed that the main acting forces for submicron aerosol particles were drag force, Brownian force, Saffman lift force and thermophoresis force. Brownian force cannot be ignored when the particle diameter was below 0.3 µm, which would promote the irregular movement of particles in space and enhance their diffusion ability. The smaller the particle size, the higher the heat source temperature and the lower the particles' initial velocity would lead to the increase of DH.

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“…This kind of gas shielded welding is frequently performed in enclosed compartments and rooms such as in shipbuilding industries where the welder has exposure to a high concentration of fumes [7]. Gomes et al did assessments of airborne ultrafine particles generated from tungsten inert gas, metal active gas, and friction stir welding, and found that all these processes emitted significant concentrations of ultrafine particles which were deposited in the lungs of exposed workers [8,9].…”

Section: Introductionmentioning

confidence: 99%

Indirect Prediction of Welding Fume Diffusion inside a Room Using Computational Fluid Dynamics

2016

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Abstract:Welding is an important and widely used process in the manufacturing and maintenance of various works involving metals and alloys. While welding has broad applications, the welding fume generated during the process has impacts on workers' health, which needs to be addressed. One of the major steps that can be undertaken to take care of this issue is the use of ventilation, which requires knowledge of characteristics and dispersion of the welding fume in the workers' breathing zone. It is difficult to assess welding fume dispersion from manual measurement due to numerous welding processes and sufficient data requirement. Numerical prediction of welding fume is dubious due to several errors. This paper considers the use of numerically predicted CO 2 concentrations to indirectly predict welding fume distribution in workshops. This is based on the assumption that if the particles are sufficiently small size, they follow the diffusion pattern of gases. Experiments are carried out in a room with an opening and a welding fume generation system for measurement of CO 2 and fume diffusion. The results show high possibility of predicting welding fume concentration based on Computational Fluid Dynamics (CFD) simulated CO 2 concentration with a correlation coefficient of 0.74.

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Numerical Simulations to Determine the Most Appropriate Welding and Ventilation Conditions in Small Enclosed Workspace.

Cited by 8 publications

References 14 publications

Comparison of Emission Models With Computational Fluid Dynamic Simulation and a Proposed Improved Model

Comparison of Emission Models With Computational Fluid Dynamic Simulation and a Proposed Improved Model

Diffusion characteristics of the industrial submicron particle under Brownian motion and turbulent diffusion

Indirect Prediction of Welding Fume Diffusion inside a Room Using Computational Fluid Dynamics

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