{"title":"基于k- ε和k-ω模型的原油减阻剂输运模拟","authors":"A. Khalaf, Asaad A. Abdullah","doi":"10.13052/EJCM1779-7179.29467","DOIUrl":null,"url":null,"abstract":"This work explores the possibility of using Newtonian turbulence k−ϵ and k−ω models for modelling crude oil flow in pipelines with drag reduction agents. These models have been applied to predict the friction factor, pressure drop and the drag reduction percentage. The simulation results of both models were compared with six published experimental data for crude oil flow in pipes with different types of drag reduction agents. The velocity near the wall was determined using the log law line of Newtonian fluid equation and by changing the parameter ΔB to achieve an excellent agreement with experimental data. Simulated data for k−ϵ model shows better agreement with most experimental data than the k−ω turbulence model.","PeriodicalId":45463,"journal":{"name":"European Journal of Computational Mechanics","volume":"1 1","pages":"459–490-459–490"},"PeriodicalIF":1.5000,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Simulation of Crude Oil Transportation with Drag Reduction Agents Using k-ϵ and k-ω Models\",\"authors\":\"A. Khalaf, Asaad A. Abdullah\",\"doi\":\"10.13052/EJCM1779-7179.29467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work explores the possibility of using Newtonian turbulence k−ϵ and k−ω models for modelling crude oil flow in pipelines with drag reduction agents. These models have been applied to predict the friction factor, pressure drop and the drag reduction percentage. The simulation results of both models were compared with six published experimental data for crude oil flow in pipes with different types of drag reduction agents. The velocity near the wall was determined using the log law line of Newtonian fluid equation and by changing the parameter ΔB to achieve an excellent agreement with experimental data. Simulated data for k−ϵ model shows better agreement with most experimental data than the k−ω turbulence model.\",\"PeriodicalId\":45463,\"journal\":{\"name\":\"European Journal of Computational Mechanics\",\"volume\":\"1 1\",\"pages\":\"459–490-459–490\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Computational Mechanics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.13052/EJCM1779-7179.29467\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Computational Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/EJCM1779-7179.29467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MECHANICS","Score":null,"Total":0}
Simulation of Crude Oil Transportation with Drag Reduction Agents Using k-ϵ and k-ω Models
This work explores the possibility of using Newtonian turbulence k−ϵ and k−ω models for modelling crude oil flow in pipelines with drag reduction agents. These models have been applied to predict the friction factor, pressure drop and the drag reduction percentage. The simulation results of both models were compared with six published experimental data for crude oil flow in pipes with different types of drag reduction agents. The velocity near the wall was determined using the log law line of Newtonian fluid equation and by changing the parameter ΔB to achieve an excellent agreement with experimental data. Simulated data for k−ϵ model shows better agreement with most experimental data than the k−ω turbulence model.