Abstract

This paper proposes a methodology using computational fluid dynamics (CFD)-FLUENT to simulate the dispersion of particulate matter releasing from a biosolid applied agricultural field and predict the particulate concentrations for different ranges of particle sizes. The discrete phase model (Lagrangian-Eulerian approach) was used in combination with each of the four turbulence models: Standard kε (kε), Realizable kε (Rkε), Standard kω (kω), and Shear-stress transport k-ω (SST) to predict particulate matter size concentrations for distances downwind of the agricultural field. In this modeling approach, particulates were simulated as discrete phase and air as continuous phase. The predicted particulate matter concentrations were compared statistically with their corresponding field study observations to evaluate the performance of turbulence models. The statistical analysis concluded that among four turbulence models, the discrete phase model when used with Rkε performed the best in predicting particulate matter concentrations for low (u u u > 5 m/s) wind speeds, Rkε, kω, and SST showed similar performances. The discrete phase model using Rkε performed very well and modeled the best concentrations for the particle sizes (μm): 0.23, 0.3, 0.4, 0.5, 0.65, 0.8, 1, 1.6, 2, 3, 4, and 5. For particle sizes: 7.5 and 10, the performances of Rkε, kε, kω, and SST were similar.

Highlights

  • This paper proposes a methodology using computational fluid dynamics (CFD)-FLUENT to simulate the dispersion of particulate matter releasing from a biosolid applied agricultural field and predict the particulate concentrations for different ranges of particle sizes

  • The following are the conclusions of this study: 1) The discrete phase modeling method was used successfully with each of the four turbulence models: kε, Realizable kε (Rkε), kω, and stress transport k-ω (SST) for predicting particle trajectories and their concentrations for distances downwind of a biosolid applied agricultural field

  • For high wind speeds (u > 5 m/s), the statistical results and confidence limits showed similar performances of Rkε, kω, and SST indicating the need for more particulate matter data sampling studies for further statistical evaluation to determine the best performing turbulence model

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Summary

Introduction

Researchers have applied CFD to simulate the dispersion of contaminants in complex urban environments [1]-[9]. A few CFD studies involved modeling of the dispersion of particulate matter emissions. Alizadehdakhel et al [10] studied the dispersion of pollutants from a stack of a cement plant and used the Large Eddy Simulation (LES) method for modeling the particulate matter concentrations for different downwind distances. Pospisil and Jicha [11] used kε RNG turbulence model for simulating the dispersion of PM10 emissions from moving vehicles along roadways in an urban area

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