Computational Evaluation of Stiffness and Toughness in MWCNTReinforced Polymer Nanocomposites
Umang B. Jani, Bhavik A. Ardeshana, Ajay M. Patel, Jalpa B. Ardeshana, Digant H. RavalIntroduction:
The rising demand for lightweight, high-performance nanocomposites has driven the search for reliable computational methods that not only predict material behavior but also reduce experimental costs and development time. Experiments on polymer nanocomposites, apart from being costly, are also very time-consuming, which has led to the emergence of reliable simulation-based modelling.
Methods:
This paper presents representative volume element (RVE)-based micromechanical modelling as a suitable technique for determining the mechanical properties of Multi-Walled Carbon nanotube (MWCNT)-reinforced Polyphenylene Sulphide (PPS) nanocomposites. ANSYS Material Designer software is used for analyze nanocomposites containing 1 wt%, 5 wt%, and 10 wt% MWCNTs under tensile testing.
Results:
The simulation predicted a significant increase in rigidity and elastic modulus with increasing MWCNT concentration. For example, the Young's modulus was 3.54 GPa for the pure PPS and rose to 16.81 GPa for the 10 wt% MWCNT—containing composite. On the contrary, the toughness of the composites went down from 7.78 × 10⁶ Pa×strain to 1.76 × 10⁶ Pa×strain due to the decrease in ductility at higher nanotube loadings.
Discussion:
This trade-off regarding stiffness and toughness is not a new revelation; in fact, it is a characteristic of the majority of CNT-reinforced polymer nanocomposites. Increased stiffness can be explained by MWCNTs having a very high modulus and an efficient load-transfer mechanism with the matrix (PPS).
Conclusion:
The current research provides evidence that simulation-aided material modelling can be used for predict the mechanical properties of PPS-MWCNT nanocomposites. The adopted computational strategy could aid in the development and refinement of next-gen lightweight components for the aerospace, automobile, and construction sectors, thereby reducing the cost and time spent on experimental testing.