Marr, Julian ; Zartmann, Lukas ; Reinel-Bitzer, Doris ; Andrä, Heiko ; Müller, Ralf (2023)
Data‐based prediction of the viscoelastic behavior of short fiber reinforced composites.
In: PAMM - Proceedings in Applied Mathematics & Mechanics, 2023, 22 (1)
doi: 10.26083/tuprints-00023733
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Data‐based prediction of the viscoelastic behavior of short fiber reinforced composites |
Language: | English |
Date: | 28 April 2023 |
Place of Publication: | Darmstadt |
Year of primary publication: | 2023 |
Publisher: | Wiley-VCH |
Journal or Publication Title: | PAMM - Proceedings in Applied Mathematics & Mechanics |
Volume of the journal: | 22 |
Issue Number: | 1 |
Collation: | 6 Seiten |
DOI: | 10.26083/tuprints-00023733 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
Abstract: | The viscoelastic behavior of short fiber reinforced polymers (SFRPs) partly depends on different microstructural parameters such as the local fiber orientation distribution. To account for this by simulation on component level, two‐scale methods couple simulations on the micro‐ and macroscale, which involve considerable computational costs. To circumvent this problem, the generation of a viscoelastic surrogate model is presented here. For that purpose, an adaptive sampling technique is investigated and data are obtained by creep simulations of representative volume elements (RVEs) using a fast Fourier transform (FFT) based homogenization method. Numerical tests confirm the high accuracy of the surrogate model. The possibility of using that model for efficient material optimization is shown. |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-237332 |
Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
Divisions: | 13 Department of Civil and Environmental Engineering Sciences > Mechanics > Continuum Mechanics |
Date Deposited: | 28 Apr 2023 12:51 |
Last Modified: | 14 Nov 2023 19:05 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/23733 |
PPN: | 509861458 |
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