CFD-DEM公司
离散元法
计算流体力学
机械
粒子(生态学)
压力降
材料科学
刚度
机械工程
工程类
物理
地质学
复合材料
海洋学
作者
Darius Markauskas,Stefan Platzk,Harald Kruggel‐Emden
标识
DOI:10.1016/j.powtec.2022.117170
摘要
Pneumatic conveying is widely used for the transportation of granular materials, where the flow of a gas is used to drive particles along pipes. While many granular materials comprise of compact and regularly shaped particles, this study aims for the numerical analysis of transport of biomass particles, which are predominantly non-spherical, frequently fiber-like and flexible and yet not broadly addressed. The Discrete Element Method coupled with Computational Fluid Dynamics (DEM-CFD) using a local volume-averaging technique is used as a tool to analyze pneumatic conveying of flexible biomass particles focusing on a pipe bend as relevant domain. The required particle flexibility is incorporated into the DEM by using a bonded particle model. Two different damping coefficients instead of just one are suggested to be used to ensure the same damping ratio at different fiber deformations. A simple technique relying on a smoothing coefficient for the damping forces and moments is proposed to minimize fluctuations and to allow the increase of the used time step in the DEM-CFD simulations. Laboratory tests with wheat straw were performed to determine its mechanical and physical properties. Thereon effects of the particle stiffness, the bond damping, the particle mass flow rate, the particle length and the gas inflow velocity on pneumatic particle transport are numerically analyzed. Obtained results demonstrate that particle trajectories in the vertical pipe section and the pressure drop depend on the particle stiffness. However, the performed comparison with the results obtained using rigid, non-flexible particles suggests that pneumatic conveying of more stiff particles can be achieved by relying on rigid particles without flexible bonds. Additionally, the performed tests using different bond damping values demonstrate the importance of the considered damping ratio for the outcome of the simulations.
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