Flow and atomization characteristics of a twin-fluid nozzle with internal swirling and self-priming effects

Kang, Can; Gao, Keke; Zhao, Hexiang

doi:10.1016/j.ijheatfluidflow.2020.108632

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…This means that the actual rate at which the particles become smaller will probably be lower than the predicted rate. Second, Zhang et al 49) found that the particle sizes in air increase as the distance from the nozzle increases because the aerosol particles will collide and aggregate. We only considered decreases in aerosol particle diameters and did not consider increases in the particle diameters.…”

Section: Discussionmentioning

confidence: 99%

Fugacity model incorporating computational fluid dynamics for analyzing the behavior of an insecticide sprayed indoors

Tanaka,

Matoba,

Kondo

et al. 2023

J. Pestic. Sci.

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Fugacity models are used widely to predict the time-dependent behaviors of chemicals in environments containing several media ( e.g. , air, sediment, soil, and water). However, these fugacity models work on the assumption that the concentration of a chemical in each medium is uniform, so they cannot describe the spatial distribution of the chemical. We developed a new fugacity model, termed InPestCFD, incorporating computational fluid dynamics to describe both the time-dependent distribution and the spatial distribution of a chemical in a medium. InPestCFD was used to calculate the behavior of an insecticide released from an aerosol canister in a room. Indoor airflow and aerosol particle behavior were calculated via computational fluid dynamics and using a Lagrangian dispersion model. Transport of the insecticide among media (aerosol particles, air, ceiling, floor, and walls) was calculated using the fugacity model. The time-dependent distributions and spatial distributions of the insecticide in the media agreed well with real measurements.

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Section: Discussionmentioning

confidence: 99%

Fugacity model incorporating computational fluid dynamics for analyzing the behavior of an insecticide sprayed indoors

Tanaka,

Matoba,

Kondo

et al. 2023

J. Pestic. Sci.

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“…Sauter mean diameter (e.g., SMD or D32) is one of the main atomizer characteristics and has been widely discussed as it plays an important role in internal combustion engines, gas turbines, furnaces, boilers, and agricultural sprays. Prediction of optimum SMD is not only essential in the design stage of these applications but also influences its performance [1], [2]. The focus is to keep SMD as low as possible due to higher efficiency and lower production of pollutions in combustion.…”

Section: Introductionmentioning

confidence: 99%

A Computational Fluid Dynamics-based Correlation to Predict Droplet Sauter Mean Diameter for ?? Yliq Atomization Model in Pressure Swirl Atomizer

Amedorme¹,

Apodi

2021

EJENG

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Liquid atomization is crucial to ensure efficient combustion as it is an inherent part of the injector system. The combustion of fuels relies on effective atomization to increase the surface area of the fuel and consequently achieve high rates of mixing and evaporation. Pressure swirl atomizers are inexpensive and reliable type of atomizer for fuel injection owing to its superior atomization characteristics and relatively simple geometry. The Sauter mean diameter (SMD) of atomizer contributes significantly to the combustion chamber performance. This paper presents a two-step strategy to predict droplet SMD for atomisation model in pressure swirl atomizer through the combination of experimentally validated Computation Fluid Dynamics (CFD) and Optimal Latin Hypercubes (OLHC) Design of Experiments (DoE) techniques. A three-dimensional Eulerian two-phase CFD model is developed to account for liquid and gas phases as a single continuum with high-density variation at large Reynolds and Weber numbers and validated against experimental measurements, before being employed to carry out a parametric study involving operating conditions and fluid properties of the pressure swirl atomizer. The atomizer is then represented in terms of four design variables, namely liquid viscosity, liquid velocity, surface tension and atomizer exit diameter. An 87-point OLHC DoE is constructed within the design variables space using a permutation genetic algorithm resulting in an accurate SMD prediction. Results show the newly developed SMD prediction is found to be superior compared with existing correlations and indicate significant improvement in the droplets SMD.

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Numerical study of swirl cooling enhancement by adding mist to air: Effects of droplet diameter and mist concentration

Han

Zhang

Song

et al. 2022

Applied Thermal Engineering

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Flow and atomization characteristics of a twin-fluid nozzle with internal swirling and self-priming effects

Cited by 9 publications

References 39 publications

Fugacity model incorporating computational fluid dynamics for analyzing the behavior of an insecticide sprayed indoors

Fugacity model incorporating computational fluid dynamics for analyzing the behavior of an insecticide sprayed indoors

A Computational Fluid Dynamics-based Correlation to Predict Droplet Sauter Mean Diameter for ?? Yliq Atomization Model in Pressure Swirl Atomizer

Numerical study of swirl cooling enhancement by adding mist to air: Effects of droplet diameter and mist concentration

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