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- W4317584211 abstract "Two-point correlation functions are used to statistically characterize the microstructure of computationally generated CNT-polymer nanocomposite RVEs. Since the electro-mechanical properties of nanocomposite are controlled by the formation and disruption of networks between CNTs and the tunnelling of electrons between CNTs, the two-point correlation functions are calculated for the conductivity contours corresponding to the generated RVEs. The two-point correlation functions are represented in a lower dimensional subspace by performing Principal Component Analysis (PCA) on them. The principal scores of the two-point correlation functions are used as predictors in linear regression models to predict the effective stiffness, electrical conductivity, and piezoresistivity of the RVEs. The regression models predict the stiffness coefficients accurately. The models are less accurate in predicting the conductivity and piezoresistivity coefficients due to the large variance of these coefficients at certain CNT volume fractions." @default.
- W4317584211 created "2023-01-21" @default.
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- W4317584211 date "2023-01-19" @default.
- W4317584211 modified "2023-09-27" @default.
- W4317584211 title "Reduced-Order Model for the Effective Electro-Mechanical Properties of CNT-Polymer Nanocomposites via Two-Point Correlation Functions" @default.
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- W4317584211 doi "https://doi.org/10.2514/6.2023-0518" @default.
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