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A hybrid modeling approach for predicting non-Newtonian flow through multilayer sintered composite-cloths

Fuhrmann, Melanie 1; Rehm, Sebastian; Müller, Martin; Nirschl, Hermann 1; Gleiß, Marco 1
1 Institut für Mechanische Verfahrenstechnik und Mechanik (MVM), Karlsruher Institut für Technologie (KIT)

Abstract:

The accurate determination of pressure loss across filter media is of central importance in the design of filtration processes, as it directly affects energy efficiency and thus operating costs. While Darcy-based approaches reliably describe the flow of Newtonian fluids, no transferable models exist for non-Newtonian fluids flowing through multilayer sintered metal wire composite-cloths, despite their widespread use in industrial applications such as polymer melt filtration or heavy fuel oil filtration in marine engines. To address this gap, this study develops and systematically compares several modeling approaches for predicting the flow velocity of shear-thinning fluids through such composite-cloths, relying on quantities available in industrial practice, such as standardized air permeability measurements. The investigated approaches comprise a physics-based white box model derived from a modified Darcy law, a purely data-driven black box model based on gradient boosting, including a target-scaled variant, and hybrid models combining both paradigms in serial and parallel configuration. All models are evaluated across ten random data splits and compared using paired statistical tests. ... mehr


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Originalveröffentlichung
DOI: 10.1016/j.dche.2026.100341
Zugehörige Institution(en) am KIT Institut für Mechanische Verfahrenstechnik und Mechanik (MVM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2026
Sprache Englisch
Identifikator ISSN: 2772-5081
KITopen-ID: 1000196986
Erschienen in Digital Chemical Engineering
Verlag Elsevier
Seiten Article no: 100341
Vorab online veröffentlicht am 10.09.2026
Schlagwörter composite-cloth, filtration, flow resistance, hybrid modeling, machine, learning
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