Development of a numerical two-phase single-fluid RANS model using OpenFOAM applied to liquid jet at

来源 :13th International Conference on Liquid Atomization and Spra | 被引量 : 0次 | 上传用户:qiushuigediao
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  The efficiency of irrigation systems and the efficacy of pesticide spraying are key features that must be improved in order to both reduce water consumption and minimize the environmental impact of pollutants.An updated version of a RANS(Reynolds-Averaged Navier-Stokes)multiphase Eulerian model is presented to tackle a 3-D transient atomization problem applied to agricultural liquid jets.Favre-averaged equations are used to describe a fictitious single-phase variable-density fluid and a modeled liquid/gas interface surface per unit volume,all implemented in a numerical solver using the OpenFOAM code.Special attention to the phase-incompressible nature of the problem required an hybrid formulation between a full-incompressible and a compressible solver algorithms.In an analog form to the single-phase variable-density formulation,three turbulence models(κ-∈,κ-∈ Realizable and Reynolds Stress Model(RSM))are tested in a study-case scenario using a default liquid volume fraction diffusion model.Previous experimental results on a similar liquid round-jet showed that the droplets fluctuating velocity can be 10 times higher in the axial direction compared to the radial one,giving the importance to evaluate the comparative behavior of a RSM over a κ-∈ type turbulence model.The test scenario consists of a round 1 mm nozzle,placed in an up-down direction with gravity,injecting water as the main fluid into still air.The model goes from 50 mm into the nozzle and up to 500 mm of atmosphere domain.The results are compared,principally,in terms of the predicted mean-quantities.Up to this stage,only a benchmark between the different models can be performed.Results show a difference between all approaches and are yet to be further analyzed.Therefore,an experimental set-up is under construction to perform measurements to provide a reliable baseline to compare with.
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