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End date: 2024 × Start date: 2020 × DDC: 530 × Publisher: Basel : MDPI × WGL Institute: IPF ×

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Now showing 1 - 3 of 3
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    Statistical Analysis of Mechanical Stressing in Short Fiber Reinforced Composites by Means of Statistical and Representative Volume Elements
    (Basel : MDPI, 2021) Breuer, Kevin; Spickenheuer, Axel; Stommel, Markus
    Analyzing representative volume elements with the finite element method is one method to calculate the local stress at the microscale of short fiber reinforced plastics. It can be shown with Monte-Carlo simulations that the stress distribution depends on the local arrangement of the fibers and is therefore unique for each fiber constellation. In this contribution the stress distribution and the effective composite properties are examined as a function of the considered volume of the representative volume elements. Moreover, the influence of locally varying fiber volume fraction is examined, using statistical volume elements. The results show that the average stress probability distribution is independent of the number of fibers and independent of local fluctuation of the fiber volume fraction. Furthermore, it is derived from the stress distributions that the statistical deviation of the effective composite properties should not be neglected in the case of injection molded components. A finite element analysis indicates that the macroscopic stresses and strains on component level are significantly influenced by local, statistical fluctuation of the composite properties.
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    Lightweight polymer-carbon composite current collector for lithium-ion batteries
    (Basel : MDPI, 2020) Fritsch, Marco; Coeler, Matthias; Kunz, Karina; Krause, Beate; Marcinkowski, Peter; Pötschke, Petra; Wolter, Mareike; Michaelis, Alexander
    A hermetic dense polymer-carbon composite-based current collector foil (PCCF) for lithium-ion battery applications was developed and evaluated in comparison to state-of-the-art aluminum (Al) foil collector. Water-processed LiNi0.5Mn1.5O4 (LMNO) cathode and Li4Ti5O12 (LTO) anode coatings with the integration of a thin carbon primer at the interface to the collector were prepared. Despite the fact that the laboratory manufactured PCCF shows a much higher film thickness of 55 µm compared to Al foil of 19 µm, the electrode resistance was measured to be by a factor of 5 lower compared to the Al collector, which was attributed to the low contact resistance between PCCF, carbon primer and electrode microstructure. The PCCF-C-primer collector shows a sufficient voltage stability up to 5 V vs. Li/Li+ and a negligible Li-intercalation loss into the carbon primer. Electrochemical cell tests demonstrate the applicability of the developed PCCF for LMNO and LTO electrodes, with no disadvantage compared to state-of-the-art Al collector. Due to a 50% lower material density, the lightweight and hermetic dense PCCF polymer collector offers the possibility to significantly decrease the mass loading of the collector in battery cells, which can be of special interest for bipolar battery architectures. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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    Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database
    (Basel : MDPI, 2021) Breuer, Kevin; Stommel, Markus
    In this study, an artificial neural network is designed and trained to predict the elastic properties of short fiber reinforced plastics. The results of finite element simulations of three-dimensional representative volume elements are used as a data basis for the neural network. The fiber volume fraction, fiber length, matrix-phase properties, and fiber orientation are varied so that the neural network can be used within a very wide range of parameters. A comparison of the predictions of the neural network with additional finite element simulations shows that the stiffnesses of short fiber reinforced plastics can be predicted very well by the neural network. The average prediction accuracy is equal or better than by a two-step homogenization using the classical method of Mori and Tanaka. Moreover, it is shown that the training of the neural network on an extended data set works well and that particularly calculation-intensive data points can be avoided without loss of prediction quality.