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Machine learning assisted discovery of effective viscous material laws for shear-thinning fiber suspensions

Sterr, Benedikt ORCID iD icon 1; Hrymak, Andrew; Schneider, Matti; Böhlke, Thomas ORCID iD icon 1
1 Institut für Technische Mechanik (ITM), Karlsruher Institut für Technologie (KIT)

Abstract:

In this article, we combine a Fast Fourier Transform based computational approach and a supervised machine learning strategy to discover models for the anisotropic effective viscosity of shear-thinning fiber suspensions. Using the Fast Fourier Transform based computational approach, we study the effects of the fiber orientation state and the imposed macroscopic shear rate tensor on the effective viscosity for a broad range of shear rates of engineering process interest. We visualize the effective viscosity in three dimensions and find that the anisotropy of the effective viscosity and its shear rate dependence vary strongly with the fiber orientation state. Combining the results of this work with insights from literature, we formulate four requirements a model of the effective viscosity should satisfy for shear-thinning fiber suspensions with a Cross-type matrix fluid. Furthermore, we introduce four model candidates with differing numbers of parameters and different theoretical motivations, and use supervised machine learning techniques for non-convex optimization to identify parameter sets for the model candidates. By doing so, we leverage the flexibility of automatic differentiation and the robustness of gradient based, supervised machine learning. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000171560
Veröffentlicht am 13.06.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Technische Mechanik (ITM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 0178-7675, 1432-0924
KITopen-ID: 1000171560
Erschienen in Computational Mechanics
Verlag Springer
Vorab online veröffentlicht am 04.06.2024
Schlagwörter Effective viscosity, Fiber-reinforced composites, Non-Newtonian suspension, Supervised machine learning, Cross-fluid
Nachgewiesen in Web of Science
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