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Carbon nanotubes (CNTs), as they possess outstanding mechanical properties and low density, are considered as one of the most promising reinforcements in composite structures. Due to their capability of transferring loads, CNTs in long continuous forms such as yarns and tapes can withstand 20 times as much load as steel can do at the same weight. In this research, carbon nanotube yarns were wound onto an aluminum plate using a custom-built fixture to fabricate a unidirectional strip. Then, by brushing epoxy resin on the strip and laminating four layers, the unidirectional CNT reinforced epoxy resin composite beam specimens were produced. The mechanical properties of the unidirectional CNT-reinforced composite (CNTRC) were determined using standard tensile tests. This study presents a method for manufacturing CNTRC out of CNT yarns, determining the CNTRC’s Young’s modulus as well as the tensile strength, and obtaining its strain field via digital image correlation (DIC) method. It is observed that the pressure due to sandwiching of the aluminum plates during the manufacturing process leads to nonuniformity of the specimen in the width along midspan of the longitudinal direction which results in the specimen’s not being perfectly unidirectional. This phenomenon can cause the matrix cracking in tensile test and reduce the ultimate tensile strength up to 78% in comparison with perfectly unidirectional specimens. However, the Young’s modulus of such composites is comparable with those obtained from previously existing research. Also, Results from DIC showed the possible failure prone areas in the specimens, as it presents a up to 64% difference between the highest and lowest strain in the tensile loading direction through the specimens. This study will serve as a foundation for future research involving CNT composites, particularly the use of their high anisotropy to produce auxetic composites with large negative Poisson’s ratios.
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Uncertainty quantification and prediction for mechanical properties of graphene aerogels via Gaussian process metamodels
Abstract Graphene aerogels (GAs), a special class of 3D graphene assemblies, are well known for their exceptional combination of high strength, lightweightness, and high porosity. However, due to microstructural randomness, the mechanical properties of GAs are also highly stochastic, an issue that has been observed but insufficiently addressed. In this work, we develop Gaussian process metamodels to not only predict important mechanical properties of GAs but also quantify their uncertainties. Using the molecular dynamics simulation technique, GAs are assembled from randomly distributed graphene flakes and spherical inclusions, and are subsequently subject to a quasi-static uniaxial tensile load to deduce mechanical properties. Results show that given the same density, mechanical properties such as the Young’s modulus and the ultimate tensile strength can vary substantially. Treating density, Young’s modulus, and ultimate tensile strength as functions of the inclusion size, and using the simulated GA results as training data, we build Gaussian process metamodels that can efficiently predict the properties of unseen GAs. In addition, statistically valid confidence intervals centered around the predictions are established. This metamodel approach is particularly beneficial when the data acquisition requires expensive experiments or computation, which is the case for GA simulations. The present research quantifies the uncertain mechanical properties of GAs, which may shed light on the statistical analysis of novel nanomaterials of a broad variety.
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- Award ID(s):
- 2119276
- PAR ID:
- 10353094
- Date Published:
- Journal Name:
- Nano Futures
- Volume:
- 5
- Issue:
- 4
- ISSN:
- 2399-1984
- Page Range / eLocation ID:
- 045004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/2399-1984/ac3c8f
