Research on Prediction of Transient Parameters in Tod Bundle Subchannel Based on POD-ML Method
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摘要: 模型降阶（ROM）通过将全阶守恒方程映射至低阶子空间或构建数据驱动的代理模型，有效降低了物理模型的复杂性。相比传统的计算流体动力学（CFD）仿真，降阶模型在大规模仿真计算中计算效率更高。本文利用本征正交分解（POD）结合机器学习（ML），提出了一种适用于瞬态工况的降阶模型框架，并以此实现棒束子通道内质量流量参数瞬态预测。针对两种不同的预测方法进行对比，结果显示在长期和短期预测中，两种方法各有优劣，可为未来进行其他复杂系统的预测提供方案。Abstract: Model reduction (ROM) effectively reduces the complexity of physical models by mapping full-order conservation equations to lower-order subspaces or building data-driven proxy models. Compared with traditional computational fluid dynamics (CFD) simulation, the reduced order model is more efficient in large-scale simulation. In this paper, a reduced order model framework is proposed by combining POD with machine learning (ML) to predict mass flow parameters in beam subchannels. The comparison of two different forecasting methods shows that both methods have advantages and disadvantages in long-term and short-term forecasting, which can provide a scheme for other complex system forecasting in the future.
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Key words:
- model reduction technique /
- Bundle subchannel /
- Machine learning
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图 5 搅浑翼及搅浑翼出口流体处网格情况
Figure 5. The Mesh of the Muddy the Wing grid and the Fluid at the Outlet of the Muddy the Wing Grid
图 11 POD+LSTM方法算例1，算例2和算例3预测的相对误差
Figure 11. Relative Errors Predicted by POD+LSTM Method for Case 1, Case 2 and Case 3
图 12 算例2在搅浑翼出口处预测的第64 s情况
Figure 12. The 64th Second Situation Predicted by Case 2 at the Exit of the Muddy Wing
图 13 LSTM+POD方法算例1预测的相对误差
Figure 13. Relative Error Predicted by Case 1 of LSTM+POD Method
图 14 LSTM+POD方法算例2和算例3预测的相对误差
Figure 14. Relative Error Predicted by Case 2 and Case3 of LSTM+POD Method
图 15 算例1分别进行两种方法相对误差对比
Figure 15. Comparison of the Relative Errors of the two Methods in Case 1
图 16 算例2分别进行两种方法相对误差对比
Figure 16. Comparison of the Relative Errors of the two Methods in Case 2
图 17 算例3分别进行两种方法相对误差对比
Figure 17. Comparison of the Relative Errors of the two Methods in Case 3
表 1 边界条件参数变化范围
Table 1. Variation Range of Boundary Condition Parameters
参数 范围 入口温度/K （550 K，580 K） 壁面热流密度/(W·m−2) （6×105，8×105） 入口质量流量/(kg·s−1) （1.0，1.5） 压力/MPa 14.71 
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