In a solid breeder blanket as a functional material, the lithium ceramics are kept in the form of a pebble bed. Helium is used as a purge gas, which flows through the pebble bed. The flow characteristics are essential in designing and running the breeding blanket efficiently, depending on the pebble bed arrangement. In the present study, a computational model of unitary pebble bed was engineered using DDPM-DEM (Dense Dispersed Phase Model-Discrete Element Method) to study the purge gas flow characteristics of the pebble bed. A similar packed bed was engineered using a porous media approach. The parameters which affect the flow characteristics are porosity, pressure distribution, pressure drop, and wall effect. Nitrogen and helium were used as purge gas to study the flow characteristics of a pebble bed. The simulations with the laminar and k-ɛ model were performed at different inlet velocities, and flow characteristics of a packed bed were observed. The comparison shows that the pressure drop simulation results agree with the Ergun equation's predicted pressure drop. Also, a previously developed experimental facility has been used to validate the simulation results. Artificial Neural Network (ANN) is used to predict the pressure drop as a complementary approach. DDPM-DEM analysis helps to predict the pressure drop which enables us to finalize the design parameters of canister, pebble size, finally the size of the pebble bed, packing fraction etc. Simulation results were also compared with the experimental results and ANN model, which were in good agreement.

中文翻译：

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球床吹扫气体流动特性模拟与实验分析

在作为功能材料的固体增殖毯中，锂陶瓷以卵石床的形式保持。氦气用作吹扫气体，流经卵石床。根据卵石床的布置，流动特性对于有效设计和运行繁殖毯至关重要。在本研究中，利用DDPM-DEM（致密分散相模型-离散元法）建立了单一卵石床的计算模型，以研究卵石床的吹扫气体流动特性。使用多孔介质方法设计了类似的填充床。影响流动特性的参数有孔隙率、压力分布、压降和壁面效应。使用氮气和氦气作为吹扫气体来研究卵石床的流动特性。在不同入口速度下使用层流模型和k-ɛ模型进行模拟，观察填充床的流动特性。对比表明，压降模拟结果与Ergun方程预测的压降相符。此外，之前开发的实验设施已用于验证模拟结果。人工神经网络 (ANN) 作为一种补充方法用于预测压降。DDPM-DEM 分析有助于预测压降，使我们能够最终确定罐的设计参数、卵石尺寸，最终确定卵石床的尺寸、填充率等。模拟结果还与实验结果和 ANN 模型进行了比较，意见一致。