.png){kind=icon}
返回
Real-time plume tracking using transfer learning approach
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compchemeng.2023.108172.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning has been used to track the real-time flammable plume of natural gas. However, a large volume of high-fidelity data is required to train the deep learning model for sufficient accuracy in congested industrial environments, which can be computationally prohibitive. This study proposes a transfer learning-based variablefidelity approach for real-time plume tracking. A Gaussian dispersion model was applied to efficiently generate a large volume of low-fidelity data, which is then used to pre-train the deep learning model. A limited number of high-fidelity simulations were conducted by solving the Navier-Stokes equation to fine-tune the pre-trained model. A case study demonstrated our proposed approach could reduce high-fidelity computations by 72% while ensuring prediction accuracy with R2=0.96 for released plume area estimation in congested chemical facilities. Optimal number of frozen layers, learning rate and the number of high-fidelity simulations required were determined to ensure adequate efficiency for this approach. This study provides an efficient alternative to improve the generalization of deep learning for real-time plume area estimation for large-scale congested chemical plants.
Keyword:
Natural gas release
Flammable area prediction
Variable -fidelity modeling
Transfer learning
Deep learning
Digital twin for emergency management
期刊
C
IF:
3.9
论文数:
8.1K
被引数:
1.7W
机构
引用论文
Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents基于深度神经网络的有毒气体泄漏事故安全疏散路径优化框架
PREAI
分享收藏A stochastic programming approach for gas detector placement using CFD-based dispersion simulations
PREAI
分享收藏Transfer learning based variable-fidelity surrogate model for shell buckling prediction.png)
COMPOSITE STRUCTURES
COMPOSITE STRUCTURESIF7.1
PREAI
分享收藏分享收藏Transfer learning for short-term wind speed prediction with deep neural networksRENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏
查看更多内容