随机纤维膜过滤器捕集浮游生物颗粒的DPM数值模拟

DPM Numerical Simulation for Capturing Plankton Particles by Random Fiber Membrane Filters

目前常用的深海浮游生物取样方法,是采用水泵抽取海水,并通过过滤技术捕捉其中的浮游生物。因此,以浮游生物过滤器为研究对象,通过随机算法建立三维纤维膜微观模型,并基于相似准则对纤维膜模型进行数值模拟研究,同时,采用拉格朗日离散相模型(DPM)追踪浮游生物颗粒在过滤器内的运动轨迹。研究结果表明:入口速度的变化对流场的压力和速度分布有显著影响,随着入口速度增加,流场内部速度差增大,流场中的压力分布出现明显的分层现象,且压力损失随入口速度增大而增大。其孔隙较小的纤维模型具有更好的过滤性能,对浮游生物颗粒的过滤效率基本稳定在80%。

Due to the fact that currently the commonly used deep-sea plankton sampling method is to use water pumps for extraction of the seawater to catch plankton through filtration,taking plankton filters as the research object,a three-dimensional fiber membrane microscopic model has thus been established through random algorithms,followed by a numerical simulation of the fiber membrane model based on similarity criteria.Meanwhile,the Lagrangian discrete phase model(DPM)is used to track the motion trajectory of plankton particles inside the filters.The research results indicate that changes in inlet velocity have a significant impact on the pressure and velocity distribution of the flow field.As the inlet velocity increases,the velocity difference inside the flow field increases,with a clear stratification in the pressure distribution in the flow field.Moreover,the pressure loss increases with the increase of inlet velocity.The fiber model with smaller pores is characterized with a better filtration performance,with the filtration efficiency of plankton particles being basically stable at 80%.