{"title":"为自由演变的颗粒悬浮液开发基于 XGBoost 的阻力模型","authors":"Ze Cao , Danesh K. Tafti","doi":"10.1016/j.powtec.2024.120408","DOIUrl":null,"url":null,"abstract":"<div><div>An XGBoost-based drag model is developed using data from Particle Resolved Simulations (PRS) of freely evolving spherical particle suspensions, encompassing Reynolds numbers from 10 to 300, solid volume fraction between 0.1 and 0.4, and particle-to-fluid density ratio of 2, 10 and 100. Drag force data from 150 continuous time instances in PRS are divided into two sets: the first set that includes data from the initial 120 instances is used for training the model and interpolation testing, while the second set comprises drag forces from the final 30 instances is used exclusively for extrapolation testing. Both interpolation and extrapolation tests demonstrate significantly improved accuracy compared to traditional drag correlations. Notably, the model achieves its highest prediction accuracy for particles with density ratios of 100, which is attributed to the increased influence of unsteady drag forces at lower density ratios that cannot be fully captured by instantaneous particle distributions alone.</div></div>","PeriodicalId":407,"journal":{"name":"Powder Technology","volume":"449 ","pages":"Article 120408"},"PeriodicalIF":4.5000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a XGBoost-based drag force model for freely evolving particle suspensions\",\"authors\":\"Ze Cao , Danesh K. Tafti\",\"doi\":\"10.1016/j.powtec.2024.120408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>An XGBoost-based drag model is developed using data from Particle Resolved Simulations (PRS) of freely evolving spherical particle suspensions, encompassing Reynolds numbers from 10 to 300, solid volume fraction between 0.1 and 0.4, and particle-to-fluid density ratio of 2, 10 and 100. Drag force data from 150 continuous time instances in PRS are divided into two sets: the first set that includes data from the initial 120 instances is used for training the model and interpolation testing, while the second set comprises drag forces from the final 30 instances is used exclusively for extrapolation testing. Both interpolation and extrapolation tests demonstrate significantly improved accuracy compared to traditional drag correlations. Notably, the model achieves its highest prediction accuracy for particles with density ratios of 100, which is attributed to the increased influence of unsteady drag forces at lower density ratios that cannot be fully captured by instantaneous particle distributions alone.</div></div>\",\"PeriodicalId\":407,\"journal\":{\"name\":\"Powder Technology\",\"volume\":\"449 \",\"pages\":\"Article 120408\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Powder Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0032591024010520\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Powder Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0032591024010520","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Development of a XGBoost-based drag force model for freely evolving particle suspensions
An XGBoost-based drag model is developed using data from Particle Resolved Simulations (PRS) of freely evolving spherical particle suspensions, encompassing Reynolds numbers from 10 to 300, solid volume fraction between 0.1 and 0.4, and particle-to-fluid density ratio of 2, 10 and 100. Drag force data from 150 continuous time instances in PRS are divided into two sets: the first set that includes data from the initial 120 instances is used for training the model and interpolation testing, while the second set comprises drag forces from the final 30 instances is used exclusively for extrapolation testing. Both interpolation and extrapolation tests demonstrate significantly improved accuracy compared to traditional drag correlations. Notably, the model achieves its highest prediction accuracy for particles with density ratios of 100, which is attributed to the increased influence of unsteady drag forces at lower density ratios that cannot be fully captured by instantaneous particle distributions alone.
期刊介绍:
Powder Technology is an International Journal on the Science and Technology of Wet and Dry Particulate Systems. Powder Technology publishes papers on all aspects of the formation of particles and their characterisation and on the study of systems containing particulate solids. No limitation is imposed on the size of the particles, which may range from nanometre scale, as in pigments or aerosols, to that of mined or quarried materials. The following list of topics is not intended to be comprehensive, but rather to indicate typical subjects which fall within the scope of the journal's interests:
Formation and synthesis of particles by precipitation and other methods.
Modification of particles by agglomeration, coating, comminution and attrition.
Characterisation of the size, shape, surface area, pore structure and strength of particles and agglomerates (including the origins and effects of inter particle forces).
Packing, failure, flow and permeability of assemblies of particles.
Particle-particle interactions and suspension rheology.
Handling and processing operations such as slurry flow, fluidization, pneumatic conveying.
Interactions between particles and their environment, including delivery of particulate products to the body.
Applications of particle technology in production of pharmaceuticals, chemicals, foods, pigments, structural, and functional materials and in environmental and energy related matters.
For materials-oriented contributions we are looking for articles revealing the effect of particle/powder characteristics (size, morphology and composition, in that order) on material performance or functionality and, ideally, comparison to any industrial standard.