An experimental investigation into modeling solids friction for fluidized dense-phase pneumatic transport of powders
An experimental investigation into modeling solids friction for fluidized dense-phase pneumatic transport of powders作者机构：Department of Mechanical Engineering Thapar University Patiala Punjab 147004 India Fujian Longking Co. Ltd. 81 Lingyuan Road Longyan City Fujian 361000 China Faculty of Engineering University ofWollongong WoUongong NSW2522 Australia
出 版 物：《Particuology》 (颗粒学报（英文版）)
年 卷 期：2017年第15卷第1期
页 面：83-91页
核心收录：
学科分类：07[理学]
基 金：Department of Science and Technology Science and Engineering Research Board Ministry of Science and Technology (Government oflndia) for financial assistance provided under the Young Scientist Scheme
主 题：Fluidized dense phase Pneumatic transport Solids friction factor Scale up Volumetric loading ratio Dimensionless velocity
摘 要：Results are presented of an ongoing investigation into modeling friction in fiuidized dense-phase pneumatic transport of bulk solids. Many popular modeling methods of the solids friction use the dimen- sionless solids loading ratio and Froude number. When evaluated under proper scale-up conditions of pipe diameter and length, many of these models have resulted in significant inaccuracy. A technique for modeling solids friction has been developed using a new combination of dimensionless numbers, volu- metric loading ratio and the ratio of particle free settling velocity to superficial conveying air velocity, to replace the solids loading ratio and Froude number. The models developed using the new formalism were evaluated for accuracy and stability under significant scale-up conditions for four different prod- ucts conveyed through four different test rigs （subject to diameter and length scale-up conditions）. The new model considerably improves predictions compared with those obtained using the existing model, especially in the dense-phase region. Whereas the latter yields absolute average relative errors varying between 10% and 86%, the former yielded results with errors from 4% to 20% for a wide range of scale-up conditions. This represents a more reliable and narrower range of prediction that is suitable for industrial scale-up requirements.
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