Particle clustering is prominent in risers, reducing gas-solid contact. This work numerically examines mass transfer between gas and clusters in fully developed region of a riser. To this end, particle clustering with a first-order catalytic bio-oil upgradation reaction in a triply periodic domain is simulated using four-way coupled CFD-DEM, an Eulerian–Lagrangian approach. Two mass transfer mechanisms in particle clusters are investigated: cluster breakup and gas velocity fluctuations within clusters. This analysis is performed by accessing individual cluster-level information using our recently developed technique based on DBSCAN: Density Based Spatial Clustering of Applications with Noise, an unsupervised ML algorithm. We show that the commonly used particle response time underpredicts the mass transfer timescale, whereas cluster breakup overpredicts the mass transfer timescale and is independent of the cluster size. In contrast, gas velocity fluctuations within clusters accurately predict mass transfer in particle clusters. Moreover, the mass transfer timescale based on the gas velocity fluctuations increases linearly with the cluster size.

中文翻译：

---

使用 CFD-DEM 对冒口中的传质进行簇级分析


颗粒聚集在冒口中很突出，减少了气固接触。这项工作对立管完全发育区域中气体和团簇之间的质量传递进行了数值检查。为此，使用四向耦合 CFD-DEM（一种欧拉-拉格朗日方法）模拟了在三周期域中具有一级催化生物油升级反应的粒子聚集。研究了粒子团中的两种传质机制：团簇破裂和团簇内的气体速度波动。此分析是通过使用我们最近开发的基于 DBSCAN：Density Based Spatial Clustering of Applications with Noise（一种无监督 ML 算法）的技术访问单个集群级信息来执行的。我们表明，常用的粒子响应时间低估了传质时间尺度，而簇分解高估了传质时间尺度，并且与簇大小无关。相比之下，团簇内的气体速度波动可以准确预测粒子团中的质量传递。此外，基于气体速度波动的传质时间尺度随团簇大小线性增加。