CIESC Journal ›› 2024, Vol. 75 ›› Issue (8): 2852-2864.DOI: 10.11949/0438-1157.20240095
• Fluid dynamics and transport phenomena • Previous Articles Next Articles
Qian LI(), Rongmin ZHANG, Zijie LIN, Qi ZHAN, Weihua CAI(
)
Received:
2024-01-22
Revised:
2024-03-10
Online:
2024-08-21
Published:
2024-08-25
Contact:
Weihua CAI
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:
CLC Number:
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.
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.
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 |
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 |
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 |
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 |
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 |
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 |
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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