A deep learning framework for predicting and optimizing flow fields in reactive flows

Computational Fluid Dynamics (CFD) is widely used for solving and optimizing the flow fields of different systems and applications. However, running CFD simulations, especially for reactive flow systems, can be very time consuming and memory intensive, which limits e.g. design space exploration in t...

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Bibliographic Details
Published in:Chemical engineering journal advances Vol. 25; p. 100966
Main Authors: Gharib, Mohsen, Maleki, Farideh Hoseinian, Rößger, Philip, Gräbner, Martin, Richter, Andreas
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2026
Elsevier
Subjects:
CFD
Chemical reactors
Convolutional autoencoders
Multilayer perceptron
Neural networks
CFD
Multilayer perceptron
Convolutional autoencoders
Chemical reactors
Neural networks
ISSN:2666-8211, 2666-8211
Online Access:Get full text
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Summary:Computational Fluid Dynamics (CFD) is widely used for solving and optimizing the flow fields of different systems and applications. However, running CFD simulations, especially for reactive flow systems, can be very time consuming and memory intensive, which limits e.g. design space exploration in the optimization tasks. In this work, a data-driven modeling methodology has been developed to predict 2D flow distributions in a chemical reactor. The primary objective was establishing correlations between global boundary conditions (including process and geometrical parameters), and the resulting CFD flow field distributions. A convolutional autoencoder was used to compress and reduce the data dimensions efficiently. Simultaneously, a multilayer perceptron served as the mapping mechanism that linked the global boundary conditions to the compressed data. The methodology developed in this work provides a very successful demonstration of its ability to map both geometric and process parameters to flow fields. The results showed a prediction accuracy of approximately 94%–97% for the CFD cases, indicating a very high prediction quality. Besides this, the prediction time was less than a second, which is significantly lower compared to the computational effort required for CFD simulations. To demonstrate the practical applicability of this approach, an interactive tool was developed to enable real-time visualization of predicted flow fields. This tool represents a foundational step toward applying digital twins and integrating such models into industrial practice. [Display omitted] •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.
ISSN:2666-8211
2666-8211
DOI:10.1016/j.ceja.2025.100966