A particle-resolved computational fluid dynamics model is built and validated to investigate the coupled transfer processes and reactions in radially layered packed beds (RLPBs), with dry reforming of methane as a model reaction. RLPBs are 53.3-69.8% lower in pressure drop and 5.4-21.6 K higher in cold spot temperature than randomly packed beds (RPBs). The methane conversions for RLPBs are slightly lower than these for RPBs, but using smaller catalyst pellets in a RLPB can balance methane conversion and pressure drop. Besides, the diameter of separating wall in RLPBs can be adjusted to meet different requirements. The existence of separating wall in RLPBs makes catalyst pellets orderly distributed and then makes flow channels less tortuous, which leads to low flow resistances and good axial convective heat transfer. This work shows that RLPBs are advantageous in enhancing transfer processes and the results should serve to guide the optimal design of RLPBs.

建立并验证了粒子分辨的计算流体动力学模型，以研究径向分层填充床 (RLPB) 中的耦合转移过程和反应，其中甲烷的干重整作为模型反应。与随机填充床 (RPB) 相比，RLPB 的压降低 53.3-69.8%，冷点温度高 5.4-21.6 K。RLPB 的甲烷转化率略低于 RPB，但在 RLPB 中使用较小的催化剂颗粒可以平衡甲烷转化率和压降。此外，RLPB中的分隔壁直径可以调整以满足不同的要求。RLPB中分隔壁的存在使催化剂颗粒有序分布，从而使流动通道不那么曲折，从而导致低流动阻力和良好的轴向对流传热。