Numerical predictions of the anisotropic viscoelastic response of uni-directional fibre composites

Finite Element (FE) simulations are conducted to predict the viscoelastic properties of uni-directional (UD) fibre composites. The response of both periodic unit cells and random stochastic volume elements (SVEs) is analysed; the fibres are assumed to behave as linear elastic isotropic solids while the matrix is taken as a linear viscoelastic solid. Monte Carlo analyses are conducted to determine the probability distributions of all viscoelastic properties. Simulations are conducted on SVEs of increasing size in order to determine the suitable size of a representative volume element (RVE). The predictions of the FE simulations are compared to those of existing theories and it is found that the Mori-Tanaka (1973) and Lielens (1999) models are the most effective in predicting the anisotropic viscoelastic response of the RVE

High strain rate behaviour of nano-quasicrystalline Al93Fe3Cr2Ti2 alloy and composites

We demonstrate the outstanding dynamic strength of nano-quasicrystalline Al93Fe3Cr2Ti2 at% alloy and composites. Unlike most crystalline Al alloys, this alloy exhibits substantial strain rate sensitivity and retains ductility at high strain rates. This opens new pathways for use in safety-critical materials requiring impact resistance

Data-driven prediction of the probability of creep-fatigue crack initiation in 316H stainless steel

Stainless steel components in advanced gas-cooled reactors (AGRs) are susceptible to creep–fatigue cracking at high temperatures. Quantifying the probability of creep–fatigue crack initiation requires probabilistic numerical simulations; these are complex and computationally intensive. Here, we present a data-driven approach to develop fast probabilistic surrogate models of creep–fatigue crack initiation in 316H stainless steel. We perform a set of Monte Carlo simulations based on the R5V2/3 high temperature assessment procedure and determine the sensitivity of the probability of crack initiation to loads and operating conditions. The data are used to train different supervised machine learning models considering Bayesian hyperparameter optimization. We discuss the relative performance of such models and show that a gradient tree boosting algorithm results in surrogate models with the highest accuracy