化工学报 ›› 2024, Vol. 75 ›› Issue (8): 2852-2864.DOI: 10.11949/0438-1157.20240095
收稿日期:
2024-01-22
修回日期:
2024-03-10
出版日期:
2024-08-25
发布日期:
2024-08-21
通讯作者:
蔡伟华
作者简介:
李倩(1990—),男,博士,副教授,liqian@neepu.edu.cn
基金资助:
Qian LI(), Rongmin ZHANG, Zijie LIN, Qi ZHAN, Weihua CAI()
Received:
2024-01-22
Revised:
2024-03-10
Online:
2024-08-25
Published:
2024-08-21
Contact:
Weihua CAI
摘要:
基于Zigzag形通道印刷电路板式换热器内跨临界甲烷的流动换热数值模拟结果开展通道内局部对流传热系数与压降机器学习与预测。采用微元分段法提取各通道内局部多物理场参数构建数据库,通过互信息法筛选输入参数,并根据验证集预测效果确定最佳网络结构和超参数。预测结果表明,人工神经网络模型表现最佳,预测对流传热系数的平均绝对百分比误差为2.228%,预测压降则为5.009%。利用机器学习对流动换热参数的预测开发了一种Zigzag形通道印刷电路板式换热器一维仿真方法,实现了通道内流体温度、壁温、对流传热系数和压降的快速准确预测,为换热器设计提供了新的方法。
中图分类号:
李倩, 张蓉民, 林子杰, 战琪, 蔡伟华. 基于机器学习的印刷电路板式换热器流动换热预测与仿真[J]. 化工学报, 2024, 75(8): 2852-2864.
Qian LI, Rongmin ZHANG, Zijie LIN, Qi ZHAN, Weihua CAI. Prediction and simulation of flow and heat transfer for printed circuit plate heat exchanger based on machine learning[J]. CIESC Journal, 2024, 75(8): 2852-2864.
参数 | 范围 |
---|---|
进口温度Tin/K | 100.0~349.01 |
进口压力Pin/Pa | 5007448.4~10049058.0 |
进口速度Vin/(m/s) | 0.26~23.83 |
进口密度 ρin/(kg/m3) | 35.02~445.58 |
进口黏度 μin/(kg/(m·s)) | 1.02×10-5~1.73×10-4 |
进口热导率 λin/(W/(m·K)) | 0.03~0.21 |
质量流率G/(kg/(m2·s)) | 107.0~954.0 |
Reynolds数Re | 1340.0~78300.0 |
Prandtl数Pr | 0.403~7.92 |
段长L/m | 24.6~28.6 |
对流传热系数h/(W/(m2·K)) | 826.94~12097.01 |
压降 Δp/Pa | 79.0~9227.7 |
表1 数据库参数及范围
Table 1 Parameters and the range in database
参数 | 范围 |
---|---|
进口温度Tin/K | 100.0~349.01 |
进口压力Pin/Pa | 5007448.4~10049058.0 |
进口速度Vin/(m/s) | 0.26~23.83 |
进口密度 ρin/(kg/m3) | 35.02~445.58 |
进口黏度 μin/(kg/(m·s)) | 1.02×10-5~1.73×10-4 |
进口热导率 λin/(W/(m·K)) | 0.03~0.21 |
质量流率G/(kg/(m2·s)) | 107.0~954.0 |
Reynolds数Re | 1340.0~78300.0 |
Prandtl数Pr | 0.403~7.92 |
段长L/m | 24.6~28.6 |
对流传热系数h/(W/(m2·K)) | 826.94~12097.01 |
压降 Δp/Pa | 79.0~9227.7 |
序号 | 网络结构 | R2 | RMSE | MAPE/% |
---|---|---|---|---|
1 | 9-18-1 | 0.9714 | 456.287 | 7.864 |
2 | 9-18-9-1 | 0.9913 | 251.180 | 3.210 |
3 | 9-27-9-1 | 0.9906 | 261.555 | 3.293 |
4 | 9-18-36-9-1 | 0.9938 | 213.042 | 2.580 |
5 | 9-27-36-9-1 | 0.9948 | 194.579 | 2.456 |
6 | 9-18-36-18-1 | 0.9958 | 173.984 | 2.430 |
7 | 9-27-36-18-1 | 0.9972 | 142.832 | 1.900 |
8 | 9-27-36-18-9-1 | 0.9967 | 156.229 | 2.095 |
表2 预测h的神经网络结构选择
Table 2 Neural network structure selection for predicting h
序号 | 网络结构 | R2 | RMSE | MAPE/% |
---|---|---|---|---|
1 | 9-18-1 | 0.9714 | 456.287 | 7.864 |
2 | 9-18-9-1 | 0.9913 | 251.180 | 3.210 |
3 | 9-27-9-1 | 0.9906 | 261.555 | 3.293 |
4 | 9-18-36-9-1 | 0.9938 | 213.042 | 2.580 |
5 | 9-27-36-9-1 | 0.9948 | 194.579 | 2.456 |
6 | 9-18-36-18-1 | 0.9958 | 173.984 | 2.430 |
7 | 9-27-36-18-1 | 0.9972 | 142.832 | 1.900 |
8 | 9-27-36-18-9-1 | 0.9967 | 156.229 | 2.095 |
序号 | 网络结构 | R2 | RMSE | MAPE/% |
---|---|---|---|---|
1 | 9-18-1 | 0.9955 | 126.071 | 6.455 |
2 | 9-18-9-1 | 0.9962 | 113.682 | 4.722 |
3 | 9-27-9-1 | 0.9973 | 96.325 | 4.563 |
4 | 9-18-36-9-1 | 0.9972 | 96.929 | 4.495 |
5 | 9-27-36-9-1 | 0.9985 | 71.435 | 3.978 |
6 | 9-18-36-18-1 | 0.9976 | 90.092 | 4.476 |
7 | 9-27-36-18-1 | 0.9982 | 77.919 | 4.054 |
8 | 9-27-36-18-9-1 | 0.9981 | 80.123 | 3.546 |
表3 预测Δp的神经网络结构选择
Table 3 Neural network structure selection for predicting Δp
序号 | 网络结构 | R2 | RMSE | MAPE/% |
---|---|---|---|---|
1 | 9-18-1 | 0.9955 | 126.071 | 6.455 |
2 | 9-18-9-1 | 0.9962 | 113.682 | 4.722 |
3 | 9-27-9-1 | 0.9973 | 96.325 | 4.563 |
4 | 9-18-36-9-1 | 0.9972 | 96.929 | 4.495 |
5 | 9-27-36-9-1 | 0.9985 | 71.435 | 3.978 |
6 | 9-18-36-18-1 | 0.9976 | 90.092 | 4.476 |
7 | 9-27-36-18-1 | 0.9982 | 77.919 | 4.054 |
8 | 9-27-36-18-9-1 | 0.9981 | 80.123 | 3.546 |
模型 | 超参数 | 数值 |
---|---|---|
ANN | activation function | leaky-relu |
learning rate | 0.005 | |
solver | adam | |
weight decay | 0.01 | |
DTR | max depth | 40 |
min samples split | 14 | |
min samples leaf | 6 | |
AdaBoost | N estimators | 56 |
max depth | 8 | |
learning rate | 0.254 | |
base estimator | DTR | |
CatBoost | depth | 7 |
learning rate | 0.126 | |
L2 leaf reg | 1 | |
iterations | 577 |
表4 不同模型预测h选取的超参数
Table 4 Different models predict hyperparameters selected by h
模型 | 超参数 | 数值 |
---|---|---|
ANN | activation function | leaky-relu |
learning rate | 0.005 | |
solver | adam | |
weight decay | 0.01 | |
DTR | max depth | 40 |
min samples split | 14 | |
min samples leaf | 6 | |
AdaBoost | N estimators | 56 |
max depth | 8 | |
learning rate | 0.254 | |
base estimator | DTR | |
CatBoost | depth | 7 |
learning rate | 0.126 | |
L2 leaf reg | 1 | |
iterations | 577 |
模型 | 超参数 | 数值 |
---|---|---|
ANN | activation function | leaky-relu |
learning rate | 0.002 | |
solver | adam | |
weight decay | 0.01 | |
DTR | max depth | 46 |
min samples split | 9 | |
min samples leaf | 1 | |
AdaBoost | N estimators | 89 |
max depth | 9 | |
learning rate | 0.372 | |
base estimator | DTR | |
CatBoost | depth | 8 |
learning rate | 0.033 | |
L2 leaf reg | 5 | |
iterations | 303 |
表5 不同模型预测Δp选取的超参数
Table 5 Different models predict the hyperparameters selected by Δp
模型 | 超参数 | 数值 |
---|---|---|
ANN | activation function | leaky-relu |
learning rate | 0.002 | |
solver | adam | |
weight decay | 0.01 | |
DTR | max depth | 46 |
min samples split | 9 | |
min samples leaf | 1 | |
AdaBoost | N estimators | 89 |
max depth | 9 | |
learning rate | 0.372 | |
base estimator | DTR | |
CatBoost | depth | 8 |
learning rate | 0.033 | |
L2 leaf reg | 5 | |
iterations | 303 |
引用 | 通道形状 | 关联式 |
---|---|---|
Chen等[ | Zigzag | |
Kim等[ | Zigzag |
表6 对比所用的关联式
Table 6 Correlation used for comparison
引用 | 通道形状 | 关联式 |
---|---|---|
Chen等[ | Zigzag | |
Kim等[ | Zigzag |
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