粒径对平底筒仓中心卸料的流态 及仓壁压力分布的影响Influence of particle size on the flow pattern and wall pressure distribution of central discharge in flat-bottom silo
杨世明1,刘克瑾2,姚辉江2,黄硕硕2,杜春来1,章敬波1 YANG Shiming1, LIU Kejin2, YAO Huijiang2, HUANG Shuoshuo2, DU Chunlai1, ZHANG Jingbo1 · 2025 · Zhongguo youzhi
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
旨在为筒仓的设计和优化提供参考,基于自主设计的半圆柱形有机玻璃平底筒仓模型,进行了平均粒径分别为15、3.5 mm和5.5 mm陶球颗粒的室内筒仓中心卸料试验和离散元数值模拟。采用流态观察、速度分析、颗粒位移追踪3种方法探究了3组粒径颗粒的流态演变过程,分析了仓壁压力分布及变化规律,通过PFC 2D得到孔隙率、力链等细观变量分布并联合宏观层次的物理试验探讨了粒径大小对流态及仓壁压力的影响。结果表明:粒径对颗粒流态的整体演化过程无显著影响,不同粒径颗粒的流态均由整体流经漏斗流过渡为管状流;大粒径颗粒完成卸料过程耗时更久,卸料速率更慢;粒径对颗粒的流动轨迹无显著影响;边界区并不是一成不变的,在卸料过程中随着粒径的增大而逐渐上移;不同粒径颗粒组的峰值卸料压力最大值均位于距离仓底约1/3的位置;粒径越大,仓壁的压力波动越剧烈,峰值卸料压力也越大。综上,粒径对平底筒仓中心卸料的流态无显著影响,不同粒径的颗粒流态演化过程和颗粒流动轨迹具有相似性,粒径越大产生的仓壁卸料压力也越大。在实际工程中,需考虑粒径对筒仓结构安全性的影响。 To provide a reference for the design and optimization of silos, based on a self-designed semi-cylindrical plexiglass flat-bottom silo
Abstract by 杨世明1,刘克瑾2,姚辉江2,黄硕硕2,杜春来1,章敬波1 YANG Shiming1, LIU Kejin2, YAO Huijiang2, HUANG Shuoshuo2, DU Chunlai1, ZHANG Jingbo1, Zhongguo youzhi (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data
Negative / Null Result ReportMeteorological Causes of the Secular Variations in Observed Extreme Precipitation Events for the Conterminous United States
Negative / Null Result ReportThe Next Generation of Sepsis Clinical Trial Designs
Negative / Null Result ReportAnalysis of DNA Methylation in Young People: Limited Evidence for an Association Between Victimization Stress and Epigenetic Variation in Blood
Negative / Null Result ReportStudy preregistration: an early example and analysis.
Negative / Null Result ReportInsights Into LSTM Fully Convolutional Networks for Time Series Classification
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: DOAJ · DOI 10.19902/j.cnki.zgyz.1003-7969.240387
