Coarse grained particle methods significantly reduce the computation cost of large‐scale fluidized bed simulation by lumping many real particles into a computation parcel. This research provides a method to estimate the errors associated with parcel size in large‐scale fluidized bed simulations. This uncertainty is first quantified in small scale domains by comparing results of discrete particle method with that employing coarse parcels of different sizes. Then, this uncertainty is correlated with parcel size and simulation domains consisting of a simple homogeneous cooling system and more complex bubbling and circulating fluidized beds. These correlations allow us to accurately estimate the uncertainty in large‐scale fluidized beds based solely on data obtained in smaller systems. The ability to estimate model‐related uncertainty in larger systems makes this method relevant for industrial applications. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2340–2350, 2018

中文翻译：

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粗离散粒子模拟中估计与包裹大小相关的不确定性的方法

通过将许多真实粒子集中到一个计算包中，粗粒度粒子方法可以大大降低大规模流化床模拟的计算成本。这项研究提供了一种在大型流化床模拟中估算与包裹尺寸相关的误差的方法。首先，通过将离散粒子方法的结果与采用不同大小的粗糙包裹的结果进行比较，在小规模域中对不确定性进行量化。然后，将这种不确定性与包裹大小和模拟域相关联，该区域由简单的均质冷却系统以及更复杂的起泡和循环流化床组成。这些相关性使我们能够仅基于较小系统中获得的数据来准确估计大型流化床中的不确定性。估计大型系统中与模型相关的不确定性的能力使该方法与工业应用相关。©2018美国化学工程师学会AIChE J，64：2340–2350，2018年