Higher-order sensitivity analyses to understand the role of FE input parameters on the simulation of composites in progressive fracture tests

Abstract

With the rise of data-driven engineering methods, such as machine learning, a comprehensive understanding of the underlying data fed into these algorithms is required for effectively applying these methods. This study presents two highly efficient Finite Element (FE) models that operate at different length scales, based on Continuum Damage Mechanics (CDM). The combination with Random Sampling-High Dimensional Model Representation (RS-HDMR) enables the determination of influential FE input parameters and their correlations to simulate Fibre Reinforced Polymer (FRP) composites subjected to a variety of progressive fracture tests in tension and compression. The results indicate that laminate-based models utilise the FE input parameters efficiently where all parameters are found to be influential. On the other hand, only fibre-related properties are relevant in ply-based FE models, with all input parameters related to transverse properties deemed to be non-influential. The findings of this study aid in identifying suitable fracture tests for a meaningful calibration and validation of CDM-based FE models. Moreover, the study lays the foundation for understanding the role of input parameters within data-driven methods, and for developing efficient reduced-order models or surrogates for uncertainty quantification on the damage tolerance of FRP composites.