主管部门: 中国航天科技集团有限公司
主办单位: 中国航天空气动力技术研究院
中国宇航学会
中国宇航出版有限责任公司

基于POD和代理模型的气固流化床流场预测

Flow Field Prediction of Gas-Solid Fluidized Bed Based on POD and Surrogate Model

  • 摘要: 针对CFD数值模拟气固流化床中两相流动复杂、计算成本高昂的问题, 建立了一种基于本征正交分解(proper orthogonal decomposition, POD)和代理模型的非反应性气固流化床流体动力学降阶模型, 通过POD-RBF方法和POD-Kriging方法分别对流化床的气固两相流动过程进行了预测, 将不同气速下的降阶模型解与全阶模型解进行了比较, 并探究了气体速度对床层运动的影响。结果表明, 在用于生成POD的设计参数范围内, 降阶模型与全阶模型结果之间的平均相对误差(mean relative error, MRE)不超过5%, 两种方法对设计工况的预测精度均高于非设计工况。对于流化床内压力、固含率、气体速度的分布, POD-RBF方法的预测精度均高于POD-Kriging方法, 其中对压力场的预测精度最高, 对速度场的预测精度最低。降阶模型在保持较高的重建和预测精度的同时, 将计算效率提高了近400倍。

     

    Abstract: Aiming at the problems of complex two-phase flow and high computational cost in CFD numerical simulation of gas-solid fluidized beds, a reduced-order model for investigating the fluid dynamics in a non-reactive gas-solid fluidized bed was established based on the proper orthogonal decomposition (POD) and the surrogate model. The gas-solid two-phase flow process in the fluidized bed was predicted by adopting the POD-RBF method and the POD-Kriging method, respectively. Besides, the reduced-order model solutions with different gas velocities were compared with the full-order model solutions, and the effect of gas velocity on the bed motion was also studied. The results show that the mean relative error (MRE) between the reduced-order model and the full-order model does not exceed 5% in the range of design parameters used to generate the POD, and the prediction accuracy of the two methods is higher for the design conditions than for the non-design conditions. For the distributions of pressure, solids content, and gas velocity in the fluidized bed, the prediction accuracy of the POD-RBF method is higher than that of the POD-Kriging method. The prediction for the pressure field has the highest accuracy and the velocity field has the lowest one in the fluidized bed by employing these two reduced-order methods. Moreover, the reduced-order model improves the computational efficiency by nearly 400 times while maintaining high reconstruction and prediction accuracy.

     

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