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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| Vydáno v: | Chemical engineering journal advances Ročník 25; s. 100966 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.03.2026
Elsevier |
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| ISSN: | 2666-8211, 2666-8211 |
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| Abstract | 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.
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•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. |
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| AbstractList | 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. 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 towards applying digital twins and integrating such models into industrial practice. |
| ArticleNumber | 100966 |
| Author | Gharib, Mohsen Maleki, Farideh Hoseinian Gräbner, Martin Rößger, Philip Richter, Andreas |
| Author_xml | – sequence: 1 givenname: Mohsen surname: Gharib fullname: Gharib, Mohsen email: Mohsen.Gharib@ikts.fraunhofer.de organization: Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Winterbergstr. 28, 01277 Dresden, Germany – sequence: 2 givenname: Farideh Hoseinian surname: Maleki fullname: Maleki, Farideh Hoseinian organization: Institute of Energy Process Engineering and Chemical Engineering IEC, TU Bergakademie Freiberg, Fuchsmühlenweg 9, 09599 Freiberg, Germany – sequence: 3 givenname: Philip surname: Rößger fullname: Rößger, Philip organization: Institute of Energy Process Engineering and Chemical Engineering IEC, TU Bergakademie Freiberg, Fuchsmühlenweg 9, 09599 Freiberg, Germany – sequence: 4 givenname: Martin orcidid: 0000-0002-6474-4012 surname: Gräbner fullname: Gräbner, Martin organization: Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Winterbergstr. 28, 01277 Dresden, Germany – sequence: 5 givenname: Andreas surname: Richter fullname: Richter, Andreas organization: Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Winterbergstr. 28, 01277 Dresden, Germany |
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| Copyright | 2025 |
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| Keywords | CFD Multilayer perceptron Convolutional autoencoders Chemical reactors Neural networks |
| Language | English |
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