%T Predicting Stochastic Lightning Mechanical Damage Effects on Carbon Fiber Reinforced Polymer Matrix Composites
%A J. Lee
%A S.Z.H. Shah
%I DEStech Publications Inc.
%D 2022
%O cited By 1; Conference of 37th Technical Conference of the American Society for Composites, ASC 2022 ; Conference Date: 19 September 2022 Through 21 September 2022; Conference Code:182780
%L scholars17498
%J Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022
%K Carbon fiber reinforced plastics; Forecasting; Hydrodynamics; Lightning; Metadata; Nanocomposites; Stochastic systems, Air blast; Blast models; Carbon fibre reinforced polymer; Damage effects; Damage initiation; Damage prediction; Deterministics; Mechanical damages; Stochastic-modeling; Stochastics, Stochastic models
%X Three stochastic air blast models are developed with spatially varying elastic properties and failure strengths for predicting lightning mechanical damage to AS4/3506 carbon/epoxy composites subjected to < 100 kA peak currents: (1) the conventional weapon effects program (CWP) model, (2) the coupled eulerianlagrangian (CEL) model, and (3) the smoothed-particle hydrodynamics (SPH) model. This work is an extension of our previous studies 1-4 that used deterministic air blast models for lightning mechanical damage prediction. Stochastic variations in composite material properties were generated using the Box-Muller transformation algorithm with the mean (i.e., room temperature experimental data) and their standard deviations (i.e., 10% of the mean herein as reference). The predicted dynamic responses and corresponding damage initiation prediction for composites under equivalent air blast loading were comparable for the deterministic and stochastic models. Overall, the domains with displacement, von-Mises stress, and damage initiation contours predicted in the stochastic models were somewhat sporadic and asymmetric along the fiber's local orientation and varied intermittently. This suggests the significance of local property variations in lightning mechanical damage prediction. Thus, stochastic air blast models may provide a more accurate lightning mechanical damage approximation than traditional (deterministic) air blast models. All stochastic models proposed in this work demonstrated satisfactory accuracy compared to the baseline models, but required substantial computational time due to the random material model generation/assignment process, which needs to be optimized in future work. © Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022. All rights reserved.