Reduced order CFD modeling approach based on the asymptotic expansion—An application for heterogeneous catalytic systems

Recent experimental techniques allow to obtain atomic scale information of heterogeneous catalysts under operando conditions, but, typically require rather complex reactor geometries. To utilize this complementary information in e.g. kinetic model development, Computational Fluid Dynamics (CFD) is needed to address the non-trivial coupling of chemical kinetics and mass transport in such chambers. However, conventional CFD approaches for solving catalytic systems have a drawback of huge computational expense, incurred by trying to solve a stiff problem. In this study, we present a reduced order approach with a significantly lower computational footprint than conventional CFD. The idea behind the approach is to estimate the solution without having to directly couple the mass transport and surface kinetics. This is achieved by a lowest-order asymptotic expansion in the catalyst sample size or, equivalently, the lateral variation of gas phase concentrations above the catalytic surface. This reduces the overall simulation time by orders of magnitude, particularly for inverse problems. We demonstrate the approach for catalytic formation of Methanol from CO 2 and H 2 in a two dimensional channel flow and for different applied reaction conditions, sample sizes and catalyst loadings. • Reduced Order CFD to compute fast solutions for heterogeneous catalytic systems. • Addressing the issue of stiff coupling between mass transport and surface kinetics. • Using CFD to determine the surface kinetics for heterogeneous catalytic systems.

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Publication Details

Journal
Chemical Engineering Journal
Published
2024-12-24
DOI
https://doi.org/10.1016/j.cej.2024.158684
Citations
2
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.56

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article

Reduced order CFD modeling approach based on the asymptotic expansion—An application for heterogeneous catalytic systems

Georg Brösigke, Jens‐Uwe Repke, Sebastian Matera, Christian Merdon et al.
2 citations
Chemical Engineering Journal
Model Reduction and Neural Networks
0.56
article

Reduced order CFD modeling approach based on the asymptotic expansion—An application for heterogeneous catalytic systems

Georg Brösigke, Jens‐Uwe Repke, Sebastian Matera, Christian Merdon, Jürgen Fuhrmann, Daniel Runge, Muhammad Uzair Qureshi
article en
2 citations

Abstract

Recent experimental techniques allow to obtain atomic scale information of heterogeneous catalysts under operando conditions, but, typically require rather complex reactor geometries. To utilize this complementary information in e.g. kinetic model development, Computational Fluid Dynamics (CFD) is needed to address the non-trivial coupling of chemical kinetics and mass transport in such chambers. However, conventional CFD approaches for solving catalytic systems have a drawback of huge computational expense, incurred by trying to solve a stiff problem. In this study, we present a reduced order approach with a significantly lower computational footprint than conventional CFD. The idea behind the approach is to estimate the solution without having to directly couple the mass transport and surface kinetics. This is achieved by a lowest-order asymptotic expansion in the catalyst sample size or, equivalently, the lateral variation of gas phase concentrations above the catalytic surface. This reduces the overall simulation time by orders of magnitude, particularly for inverse problems. We demonstrate the approach for catalytic formation of Methanol from CO 2 and H 2 in a two dimensional channel flow and for different applied reaction conditions, sample sizes and catalyst loadings. • Reduced Order CFD to compute fast solutions for heterogeneous catalytic systems. • Addressing the issue of stiff coupling between mass transport and surface kinetics. • Using CFD to determine the surface kinetics for heterogeneous catalytic systems.

Chemical Engineering JournalVol. 504
Fritz Haber Institute of the Max Planck Society (DE), Weierstrass Institute for Applied Analysis and Stochastics (DE), Technische Universität Berlin (DE)
Deutsche Forschungsgemeinschaft, Higher Education Commission, Pakistan
Openalex Percentile: Top 33%
Model Reduction and Neural Networks
0.56
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