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Civil-Comp Conferences
ISSN 2753-3239
CCC: 9
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
Edited by: P. Iványi, J. Kruis and B.H.V. Topping
Paper 10.8

Fractional Calculus in Describing the Viscoelastic Response of PVB foil

M. Ĺ ejnoha

Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czechia

Full Bibliographic Reference for this paper
M. Šejnoha, "Fractional Calculus in Describing the Viscoelastic Response of PVB foil", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 9, Paper 10.8, 2024, doi:10.4203/ccc.9.10.8
Keywords: viscoelasticity, fractional viscoelasticity, Maxwell chain model, laminated glass, polymer interlayer, PVB foil, rheometer experiment.

Abstract
This paper is concerned with a theoretical description of a polyvinyl butyral (PVB) foil, which is commonly used as an interlayer of laminated-glass composites. This polymer exhibits a significant time and temperature-dependent behaviour and can be effectively described employing the theory of viscoelasticity. The present study concentrated on the ability of both standard and fractional Maxwell chain models to represent the polymer behavior observed experimentally. Both models were calibrated adopting the same set of laboratory data derived with the help of a dynamic shear rheometer for the selected range of frequencies and temperatures. The time temperatures superposition principal was exploited to extend the data beyond the allowable frequency range. The optimal model parameters were then obtained by matching the measured and theoretically predicted response of the polymer in the framework of least squares method. We have seen that the model based on fractional calculus not only requires less number of model parameters but it also provides predictions which are closer to a real behavior of the examined polymer. This promotes its application in smoothing the measured data and consequently allows us to extend the measured domain with a relatively low number of model parameters.

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