In this paper we shortly investigate the similarity between states for different fluidized bed regimes, which is an essential requirement for the application of recurrence computational fluid dynamics (rCFD) [Lichtenegger and Pirker, Chem. Eng. Sci. 153, 394 (2016)]. Therefore, bubbling and turbulent fluidization regimes are outlined in the frame of different variable-based recurrence/distance norms (rNorm/dNorm). This last is typically used to quantify the degree of flow similarity within the observation time span. The recurrence plots or the so-called recurrence/distance matrices are accordingly constructed upon the rNorm/dNorm values and show no recurrence tendency for the full-resolved turbulent fluidization regimes. It is reported thereabouts that the high fluctuated nature of turbulent fluidization (interphase small scales) absentees the periodicity, and a disclosing procedure for the superstructure (large-scale) dynamics is vitally needed. To that end, a spatial filtering with the idea of the domain decomposition (recurrence island) has been applied to reveal a proper posterior indicator of turbulent fluidization recurrence. The filtering approach handles in increasing the recurrence prominence without changing the system behavior and makes the recurrence more visible. From other perspectives, a turbulence modeling using the coarse-grained approximate deconvolution model–two-fluid model (ADM-TFM) is employed for the turbulent fluidization case. Following its recurrence investigations, the resultant prominence offered by ADM-TFM is in a very comparable aspect to the same grid spatial filtering.

中文翻译：

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揭示流化床的循环特性

在本文中，我们不久将研究不同流化床状态下状态之间的相似性，这是应用递归计算流体动力学（rCFD）的基本要求[Lichtenegger and Pirker，Chem。。科学 153，394（2016）]。因此，在不同的基于变量的递归/距离规范（rNorm / dNorm）的框架中概述了起泡和湍流状态。该最后一个通常用于量化观察时间跨度内的流相似度。递归图或所谓的递归/距离矩阵相应地基于rNorm / dNorm值构建，并且对于完全解析的湍流流态而言，没有递归趋势。据报道，湍流的高波动性（相间小尺度）缺乏周期性，因此迫切需要公开上部结构（大尺度）动力学的程序。为此，应用具有域分解（递归岛）思想的空间滤波来揭示湍流流化递归的适当后验指标。过滤方法可以在不改变系统行为的情况下增加重复出现的次数，并使重复出现更为明显。从其他角度来看，在湍流化情况下，采用了粗粒度近似反褶积模型-双流体模型（ADM-TFM）进行的湍流建模。经过对其重复性的研究，ADM-TFM所产生的突出结果与相同的网格空间滤波在非常可比的方面。从其他角度来看，在湍流化情况下，采用了粗粒度近似反褶积模型-双流体模型（ADM-TFM）进行的湍流建模。经过对其重复性的研究，ADM-TFM所产生的突出结果与相同的网格空间滤波在非常可比的方面。从其他角度来看，在湍流化情况下，采用了粗粒度近似反褶积模型-双流体模型（ADM-TFM）进行的湍流建模。经过对其重复性的研究，ADM-TFM所产生的突出结果与相同的网格空间滤波在非常可比的方面。