首页|连续进出料鼓泡流化床停留时间分布的相似准则研究

连续进出料鼓泡流化床停留时间分布的相似准则研究

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以连续进出料鼓泡流化床为研究对象,在Glicksman相似准则的基础上引入了扩散准则数,获得了具备颗粒停留时间分布(RTD)相似性的流化床相似放大准则,并明确了颗粒RTD的相似转换关系.对缩尺流化床模型与原型的流动行为及颗粒RTD进行了数值模拟分析,发现在给定的几何相似常数范围内(1<k<200),缩尺模型计算时长可由22.8天降至1.4天,且颗粒分布规律与原型相似,最大误差不超过9.24%.缩尺模型的颗粒RTD经相似转换后能够较好地预测原型颗粒RTD规律且关键特征值最大误差为17.8%.此外,改变颗粒流量、流化气速及静床高度后,缩尺模型仍能准确预测原型的颗粒RTD,其关键特征值最大误差不超过10.32%,证实了该相似准则在变工况下具有适用性,可用于快速、准确预测大型流化床颗粒RTD.
Study on adaptability of scaling law to residence time distribution in bubbling fluidized beds with continuous operation
Particles residence time distribution(RTD)in bubbling fluidized beds is important for its performances,and the evaluation of particles RTD in large-scale bubbling fluidized beds is a critical issue.In this study,a diffusion criteria number was introduced based on the scaling law proposed by Glicksman.Then the fluidized bed similarity scaling law that exhibits RTD similarity was obtained,and the transformation relations for the similarity of particles RTD was clarified.The flow behaviors and particles RTD of the scaled-down and original fluidized beds were numerically simulated.The results show that within the specific range of geometric similarity constants(1<k<200),the computational time for the scaled-down model could be reduced from 22.8 days to 1.4 days.Moreover,the particles RTD exhibits similarity to that of the original bed,with a maximum error not exceeding 9.24%.Following the similarity transformation,the particles RTD of the scaled-down model demonstrates a favorable ability to predict the particles RTD of the original bed,with a maximum error for key characteristic values of 17.8%.Furthermore,variations in particle flow rate,fluidization velocity,and static bed height do not influence the accuracy of scaled-down model in predicting the particles RTD of the original bed.The maximum error of its key characteristic values does not exceed 10.32%.This confirms the applicability of the similarity scaling law under varying operating conditions.All the results demonstrate the effective of presented similarity scaling law for the rapid and accurate prediction of particles RTD in large-scale fluidized beds.

bubbling fluidized bedresidence time distributionscaling lawtransformation relationsscaled-down modelnumerical simulation

屠楠、刘晓群、王驰宇、方嘉宾

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西安工程大学机电工程学院,陕西 西安 710600

西安交通大学化学工程与技术学院,陕西 西安 712000

鼓泡流化床 停留时间分布 相似准则 相似转换关系 缩尺模型 数值模拟

陕西省重点研究计划国家重点研发计划陕西省秦创原"科学家+工程师"队伍建设项目中国电力建设股份有限公司研究中心定向资助计划

2022GXLH-01-082018YFB15010032022KXJ-179DJ-PTZX-2021-03

2024

10.11949/0438-1157.20231195

化工学报
中国化工学会 化学工业出版社

化工学报

CSTPCD北大核心
影响因子：1.26
ISSN：0438-1157
年,卷(期)：2024.75(2)
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