.png){kind=icon}
.png){kind=icon}
Return
A deep learning framework for predicting and optimizing flow fields in reactive flows
.png){kind=icon}
OA
AI
DOI：10.1016/j.ceja.2025.100966.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Developed a data-driven surrogate model for predicting 2D reactive flow fields in chemical reactors. • Integrated convolutional autoencoders and multilayer perceptrons to map global boundary conditions to CFD-based flow distributions. • Achieved high computational efficiency in estimating key flow variables within reactive flow systems. • Created an interactive visualization tool enabling real-time design exploration and optimization of reactor performance.
Keywords:
Chemical reactors
CFD
Convolutional autoencoders
Neural networks
Multilayer perceptron
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.4K
Citations:
3.9K