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Main Authors: Mitterlindner, Michael, Graber, Maximilian, Kratzer, Regina, Reichhartinger, Markus, Radl, Stefan
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.26695
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author Mitterlindner, Michael
Graber, Maximilian
Kratzer, Regina
Reichhartinger, Markus
Radl, Stefan
author_facet Mitterlindner, Michael
Graber, Maximilian
Kratzer, Regina
Reichhartinger, Markus
Radl, Stefan
contents Detailed Computational Fluid Dynamics (CFD) simulations are too computationally expensive for the real-time control and design optimization of multiphase flow reactors. To address these limitations, we introduce CLARA, a software toolbox that automates the generation of Compartment Models (CM) via the unsupervised clustering of CFD data. Unlike previous studies, our toolbox enables the modelling of multiphase phenomena and interphase mass transfer within each compartment. CLARA employs unsupervised clustering algorithms, graph reassignment, and optimization routines to ensure mass conservation and spatial connectivity across all compartments. Verification studies utilizing analytical benchmarks and reactive multiphase CFD simulations demonstrate that the CMs produced by CLARA accurately reproduce reactor performance and spatial species distributions. The significantly reduced computational demand of CMs compared to full CFD models enables the optimal control of multiphase reactors and facilitates their rational design and optimization.
format Preprint
id arxiv_https___arxiv_org_abs_2604_26695
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Compartment Modelling of Multiphase Reactors using Unsupervised Clustering
Mitterlindner, Michael
Graber, Maximilian
Kratzer, Regina
Reichhartinger, Markus
Radl, Stefan
Fluid Dynamics
Detailed Computational Fluid Dynamics (CFD) simulations are too computationally expensive for the real-time control and design optimization of multiphase flow reactors. To address these limitations, we introduce CLARA, a software toolbox that automates the generation of Compartment Models (CM) via the unsupervised clustering of CFD data. Unlike previous studies, our toolbox enables the modelling of multiphase phenomena and interphase mass transfer within each compartment. CLARA employs unsupervised clustering algorithms, graph reassignment, and optimization routines to ensure mass conservation and spatial connectivity across all compartments. Verification studies utilizing analytical benchmarks and reactive multiphase CFD simulations demonstrate that the CMs produced by CLARA accurately reproduce reactor performance and spatial species distributions. The significantly reduced computational demand of CMs compared to full CFD models enables the optimal control of multiphase reactors and facilitates their rational design and optimization.
title Compartment Modelling of Multiphase Reactors using Unsupervised Clustering
topic Fluid Dynamics
url https://arxiv.org/abs/2604.26695