Stick-slip

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A Model for Elastic Hysteresis of Unidirectional Fibrous Nano Composites Incorporating Stick-Slip

Journal Title, Volume, Page: 
Journal of Material Science and Engineering A: Structural Materials: Properties, Microstructure and Processing, 530 (15), pp. 349–356.
Year of Publication: 
2011
Authors: 
M.M.S. Dwaikat
Faculty of Industrial Design Engineering, Section Product Engineering, Technical University of Delft, Delft, The Netherlands
Current Affiliation: 
Department of Civil Engineering, An-Najah National University, Palestine
C. Spitas
Faculty of Industrial Design Engineering, Section Product Engineering, Technical University of Delft, Delft, The Netherlands
V. Spitas
School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
Preferred Abstract (Original): 
In fibrous nano-composites, slip of fillers within the matrix comprises a major mechanism through which energy is dissipated. In the current study, a simplified model for predicting the elastic hysteresis of perfectly aligned unidirectional nano-composites loaded in the direction of the fibers is developed. The model, based on shear lag analysis and derived from basic principles of continuum micromechanics, incorporates a shear stick-slip constitutive law at the matrix–fiber interface. Once calibrated by comparison to cyclic stress–strain curves on nano-composites, the model is used to conduct a set of parametric studies on the influence of various parameters on the energy dissipation. Simulation results reveal that the interfacial shear stick-slip constitutive law, the volume fraction andthe aspect ratio of the fibers, and the fiber-to-matrix stiffness ratio have a direct influence on the hysteresis of nano-composites. Also, it is demonstrated that it is possible to achieve an optimal set of parameters for which energy dissipation due to hysteresis is maximized. The proposed model provides a numerically efficient yet reasonably accurate alternative for use in design and analysis of fibrous composites when compared to existing complex models.
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