In this study, the energy minimization multi-scale (EMMS)/Bubbling model is coupled with the computational fluid dynamics/discrete element method (CFD-DEM) model via a structure-dependent drag coefficient to simulate the National Energy Technology Laboratory (NETL) small-scale challenge problem using the open-source multiphase flow code MFIX. The numerical predictions are compared against particle velocity measurements obtained from high-speed particle image velocimetry (HSPIV) and differential pressure measurements. The drag-reduction effect of the EMMS bubble-based drag coefficient is observed to significantly improve predictions of the horizontal particle velocity and granular temperature when compared to several other drag coefficients tested; however, the vertical particle velocity and pressure fluctuation characteristic predictions are degraded. The drag-reduction effect is characterized by a reduction in the sizes of slugs or voids, as identified through spectral decomposition of the pressure fluctuations. Overall, this study shows great promise in employing drag coefficients, developed via multi-scale approaches (such as the EMMS paradigm), in CFD-DEM models.

在这项研究中，通过与结构有关的阻力系数，将能量最小化多尺度（EMMS）/冒泡模型与计算流体力学/离散元方法（CFD-DEM）模型耦合，以模拟国家能源技术实验室（NETL）使用开源多相流代码MFIX的小规模挑战问题。将数值预测与从高速粒子图像测速（HSPIV）和压差测量获得的粒子速度测量进行比较。与其他测试的阻力系数相比，基于EMMS气泡的阻力系数的减阻效果可显着改善水平粒子速度和颗粒温度的预测。然而，垂直粒子速度和压力波动特征的预测值下降。减阻作用的特征在于，通过压力波动的频谱分解可以确定，块或空隙的尺寸减小。总的来说，这项研究显示了在CFD-DEM模型中采用通过多尺度方法（例如EMMS范例）开发的阻力系数的巨大前景。