一种用于造粒离散元建模的基于网络的颗粒尺寸分布测量方法
A network-based approach to measure granule size distribution for discrete element modeling of granulation
AI总结：
针对离散元建模中转鼓造粒稠密流的颗粒识别难题，提出基于网络科学社区检测的改进方法，相比现有方法能更准确识别颗粒并复现工艺参数对颗粒尺寸分布的影响。
摘要：
转鼓造粒是一种粒径增大过程，其中颗粒物料与液体粘结剂一同被搅动，以形成更大尺寸的颗粒。离散元建模（DEM）正越来越多地被用于更好地理解和研究造粒过程。然而，与实验不同，在DEM框架内测量颗粒尺寸通常需要对颗粒给出明确的量化定义，以及相应的颗粒识别方法。在本研究中，我们发现文献中现有的定义及相关方法在识别稠密流动（如转鼓造粒过程中的流动）中的颗粒时效果不佳。我们提出了一种改进的定义和颗粒识别方法，该方法基于网络科学文献中的社区检测技术。以液体沉降法为基准进行对比，所提出的方法能更好地识别转鼓造粒机中的颗粒。我们还改变了液体含量、填充率等造粒工艺参数，研究了这些参数对转鼓造粒后得到的累积颗粒尺寸分布的影响。我们发现，现有的颗粒识别方法无法复现工艺参数对累积颗粒尺寸分布的已知影响，而基于社区检测的所提方法能以更高的精度复现这些影响。
英文摘要：
Drum granulation is a size enlargement process where granular material is agitated with a liquid binder to form larger size granules. Discrete element modeling is increasingly being used to better understand and investigate the granulation process. However, unlike experiments the measurement of granule size within a DEM framework often necessitates an explicit quantitative definition of a granule and a corresponding granule identification method. In this work, we show that the existing definitions and the associated methods in literature are ineffective at identifying granules for dense flows such as during drum granulation. We propose an improved definition and granule identification method based on community-detection used in network science literature. The proposed method better identifies granules in a drum granulator as benchmarked against liquid-settling. We also vary granulation process parameters like liquid content and fill level and study their effect on the cumulative granule size distribution attained after drum granulation. We find that the existing granule-identification methods fail to reproduce the well-known effects of process parameters on the cumulative granule size distribution. The proposed method, based on community detection, reproduces the effects with better accuracy.
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