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A physically based strength prediction model for glass
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DOI：10.1016/j.ijsolstr.2023.112548.png)
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Abstract
En 中文
The strength of glass has been a subject of great interest for more than one hundred years. Due to the stochastic nature of glass, governed by microscopical surface flaws, glass plates exhibit large variations in fracture strength. The aim of this work is to propose a new strength prediction model for glass, named the Glass Strength Prediction Model (GSPM) that captures the nature of fracture initiation in glass, spanning from rate dependence to size effects. We aim for the presented model to be applicable in modern design processes and provide a procedure to facilitate input parameter calibration for glass plates from different suppliers. GSPM is a Monte Carlo based model that combines the theories of linear elastic fracture mechanics (LEFM) and sub-critical crack growth (SCG) to generate virtual tests on a representative sample of glass plates. The stress evolution in the glass plates is obtained from finite element (FE) simulations. The model results in representative fracture strength distributions that span the probable fracture initiation instances with respect to time, location and stress level. We also demonstrate how the GSPM can be used to trigger fracture in constitutive models applied in FE simulations. This feature provides the option to investigate scenarios including multiple glass plates with interdependent fracture initiation behavior. The GSPM displays great promise in terms of usability and prediction capacity. It is able to capture the fracture initiation behavior of glass plates of varying geometries exposed to load cases spanning from, e.g., quasi-static four-point bending to blast pressure. The model has the potential to reduce the number of physical experiments and numerical FE simulations in modern development processes of glass structures.
Keywords:
Glass
Strength prediction
Fracture initiation
Stochastic fracture
Monte Carlo simulations
Sub-critical crack growth
Brittle fracture
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