{"title":"应用计算流体动力学的生物固体中不同尺寸范围颗粒物质的分散模拟","authors":"Praneeth Nimmatoori, Ashok Kumar","doi":"10.4236/ACES.2021.112012","DOIUrl":null,"url":null,"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 < 2 m/s) and medium (2 < u < 5 m/s) wind speeds. For high (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.","PeriodicalId":7332,"journal":{"name":"Advances in Chemical Engineering and Science","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Dispersion Modeling of Particulate Matter in Different Size Ranges Releasing from a Biosolids Applied Agricultural Field Using Computational Fluid Dynamics\",\"authors\":\"Praneeth Nimmatoori, Ashok Kumar\",\"doi\":\"10.4236/ACES.2021.112012\",\"DOIUrl\":null,\"url\":null,\"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 < 2 m/s) and medium (2 < u < 5 m/s) wind speeds. For high (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.\",\"PeriodicalId\":7332,\"journal\":{\"name\":\"Advances in Chemical Engineering and Science\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Chemical Engineering and Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4236/ACES.2021.112012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Chemical Engineering and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/ACES.2021.112012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dispersion Modeling of Particulate Matter in Different Size Ranges Releasing from a Biosolids Applied Agricultural Field Using Computational Fluid Dynamics
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 < 2 m/s) and medium (2 < u < 5 m/s) wind speeds. For high (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.