Reduced order modeling of spray drying

Spray drying is a common a technique in process engineering, involving analysis tasks for large-scale multiphysics mechanisms, typically addressed using Computational Fluid Dynamics (CFD) software based on Finite Element Method (FEM) simulation. To enable e.g. real-time control schemes to take into account spatio-temporal behavior, efficient Reduced Order Models (ROMs) need to be identified, which in the best case retain physical information and interpretability as much as possible. In this paper, this problem is approached for a simplified two-dimensional spray dryer CFD model. Two different versions of Dynamic Mode Decomposition with control (DMDc) are compared, namely (i) a direct application of DMDc to snapshot data for temperature and humidity profiles, including droplet populations as moving and distributed inputs, and (ii) an adaptation exploring structure preservation based on the physical insight to the underlying mechanisms. It turns out that for the presented case study the structure preserving DMDc yields improved results.

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Publication Details

Journal
IFAC-PapersOnLine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ifacol.2025.03.026
Citations
1
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.32
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article

Reduced order modeling of spray drying

A. Schaum, Arvid Lepsien
1 citations
IFAC-PapersOnLine
Model Reduction and Neural Networks
1.32
article

Reduced order modeling of spray drying

A. Schaum, Arvid Lepsien
article en
1 citations

Abstract

Spray drying is a common a technique in process engineering, involving analysis tasks for large-scale multiphysics mechanisms, typically addressed using Computational Fluid Dynamics (CFD) software based on Finite Element Method (FEM) simulation. To enable e.g. real-time control schemes to take into account spatio-temporal behavior, efficient Reduced Order Models (ROMs) need to be identified, which in the best case retain physical information and interpretability as much as possible. In this paper, this problem is approached for a simplified two-dimensional spray dryer CFD model. Two different versions of Dynamic Mode Decomposition with control (DMDc) are compared, namely (i) a direct application of DMDc to snapshot data for temperature and humidity profiles, including droplet populations as moving and distributed inputs, and (ii) an adaptation exploring structure preservation based on the physical insight to the underlying mechanisms. It turns out that for the presented case study the structure preserving DMDc yields improved results.

IFAC-PapersOnLineVol. 59(1)
University of Hohenheim (DE)
Openalex Percentile: Top 21%
Model Reduction and Neural Networks
1.32
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Reduced order modeling of spray drying — A. Schaum, Arvid Lepsien · IFAC-PapersOnLine (2025) | TGRS Research Map | TGRS