CIESC Journal ›› 2023, Vol. 74 ›› Issue (9): 3775-3785.DOI: 10.11949/0438-1157.20230711
• Fluid dynamics and transport phenomena • Previous Articles Next Articles
Kaijie WEN1,2(), Li GUO1,2, Zhaojie XIA1,2, Jianhua CHEN1()
Received:
2023-07-10
Revised:
2023-09-02
Online:
2023-11-20
Published:
2023-09-25
Contact:
Jianhua CHEN
温凯杰1,2(), 郭力1,2, 夏诏杰1,2, 陈建华1()
通讯作者:
陈建华
作者简介:
温凯杰(1998—),女,硕士研究生,wenkaijie21@mails.ucas.ac.cn
基金资助:
CLC Number:
Kaijie WEN, Li GUO, Zhaojie XIA, Jianhua CHEN. A rapid simulation method of gas-solid flow by coupling CFD and deep learning[J]. CIESC Journal, 2023, 74(9): 3775-3785.
温凯杰, 郭力, 夏诏杰, 陈建华. 一种耦合CFD与深度学习的气固快速模拟方法[J]. 化工学报, 2023, 74(9): 3775-3785.
变量 | 数值 |
---|---|
颗粒直径(dp)/µm | 275 |
固相密度(ρs)/(kg/m3) | 2500 |
气相密度(ρg)/(kg/m3) | 1.225 |
气相黏度(μg)/(Pa·s) | 1.84×10-5 |
反应器尺寸/m | 0.28×1.5 |
床层高度/m | 0.4 |
网格数量 | 56×300 |
模拟时间步长/s | 2×10-4 |
写入文件时间步长/s | 0.01 |
总模拟时长/s | 10 |
Table 1 Main parameters of the simulation
变量 | 数值 |
---|---|
颗粒直径(dp)/µm | 275 |
固相密度(ρs)/(kg/m3) | 2500 |
气相密度(ρg)/(kg/m3) | 1.225 |
气相黏度(μg)/(Pa·s) | 1.84×10-5 |
反应器尺寸/m | 0.28×1.5 |
床层高度/m | 0.4 |
网格数量 | 56×300 |
模拟时间步长/s | 2×10-4 |
写入文件时间步长/s | 0.01 |
总模拟时长/s | 10 |
Name | Type | Kernel | Stride | Padding | Ch I/O |
---|---|---|---|---|---|
Encoder | CNN | 3×3 | 1×1 | 1×1 | 5/16 |
ConvLSTM | 3×3 | 1×1 | 1×1 | 16/64 | |
CNN | 3×3 | 2×2 | 1×1 | 64/64 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 64/96 | |
CNN | 3×3 | 2×2 | 1×1 | 96/96 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 96/96 | |
Forecast | ConvLSTM | 3×3 | 1×1 | 1×1 | 96/96 |
CNN | 4×4 | 2×2 | 1×1 | 96/96 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 96/96 | |
CNN | 4×4 | 2×2 | 1×1 | 96/96 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 96/64 | |
CNN | 3×3 | 1×1 | 1×1 | 64/16 | |
CNN | 1×1 | 1×1 | 0×0 | 16/5 |
Table 2 Parameters of the neural network
Name | Type | Kernel | Stride | Padding | Ch I/O |
---|---|---|---|---|---|
Encoder | CNN | 3×3 | 1×1 | 1×1 | 5/16 |
ConvLSTM | 3×3 | 1×1 | 1×1 | 16/64 | |
CNN | 3×3 | 2×2 | 1×1 | 64/64 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 64/96 | |
CNN | 3×3 | 2×2 | 1×1 | 96/96 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 96/96 | |
Forecast | ConvLSTM | 3×3 | 1×1 | 1×1 | 96/96 |
CNN | 4×4 | 2×2 | 1×1 | 96/96 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 96/96 | |
CNN | 4×4 | 2×2 | 1×1 | 96/96 | |
ConvLSTM | 3×3 | 1×1 | 1×1 | 96/64 | |
CNN | 3×3 | 1×1 | 1×1 | 64/16 | |
CNN | 1×1 | 1×1 | 0×0 | 16/5 |
Physical quantity | MSE | PSNR |
---|---|---|
εs | 6.67×10-4 | 31.76 |
Ugx | 1.04×10-5 | 49.83 |
Ugy | 1.69×10-5 | 47.72 |
Usx | 1.53×10-5 | 48.15 |
Usy | 2.37×10-5 | 46.25 |
Table 3 Model performance on the test set
Physical quantity | MSE | PSNR |
---|---|---|
εs | 6.67×10-4 | 31.76 |
Ugx | 1.04×10-5 | 49.83 |
Ugy | 1.69×10-5 | 47.72 |
Usx | 1.53×10-5 | 48.15 |
Usy | 2.37×10-5 | 46.25 |
Fig.10 Comparison of typical axial distribution after correction of solid volume fraction [(a)—(c)] and typical axial distribution after further treatment of very small values [(d)—(f)] when deep learning model predicts εs, Ug and Us
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