From fluid flow to coupled processes in fractured rock: Recent advances and new frontiers
HS Viswanathan, J Ajo‐Franklin… - Reviews of …, 2022 - Wiley Online Library
Quantitative predictions of natural and induced phenomena in fractured rock is one of the
great challenges in the Earth and Energy Sciences with far‐reaching economic and …
great challenges in the Earth and Energy Sciences with far‐reaching economic and …
Modeling of hierarchical solidification microstructures in metal additive manufacturing: Challenges and opportunities
Metal-based additive manufacturing (AM) processes often produce parts with improved
properties compared to conventional manufacturing and metal working routes. However …
properties compared to conventional manufacturing and metal working routes. However …
A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning
An image-based deep learning framework is developed to predict nonlinear stress
distribution and failure pattern in microstructural representations of composite materials in …
distribution and failure pattern in microstructural representations of composite materials in …
[HTML][HTML] Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network …
Fibre-reinforced polymer (FRP) composites have been widely used in different engineering
sectors due to their excellent physical and mechanical properties. Therefore, fast …
sectors due to their excellent physical and mechanical properties. Therefore, fast …
StressNet-Deep learning to predict stress with fracture propagation in brittle materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of
cracks aided by high internal stresses. Hence, accurate prediction of maximum internal …
cracks aided by high internal stresses. Hence, accurate prediction of maximum internal …
Graph neural networks for simulating crack coalescence and propagation in brittle materials
High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-
based models can become computationally demanding as the number of microcracks …
based models can become computationally demanding as the number of microcracks …
Crack path predictions in heterogeneous media by machine learning
M Worthington, HB Chew - Journal of the Mechanics and Physics of Solids, 2023 - Elsevier
The interaction between stress fields at the crack-tip and nearby microstructural
heterogeneities influences the crack path, and in turn, the effective fracture toughness of a …
heterogeneities influences the crack path, and in turn, the effective fracture toughness of a …
[HTML][HTML] Effects of defects on the transverse mechanical response of unidirectional fibre-reinforced polymers: DEM simulation and deep learning prediction
The presence of defects in composite materials is hardly avoidable during the process of
materials manufacturing, which may affect the mechanical behaviour of the material. This …
materials manufacturing, which may affect the mechanical behaviour of the material. This …
Data-driven enhanced FDEM for simulating the rock mechanical behavior
Z Wu, R Zhao, X Xu, Q Liu, M Liu - International Journal of Mechanical …, 2024 - Elsevier
In this paper, a data-driven enhanced combined finite-discrete element method (DDFDEM)
is proposed to simulate the rock mechanical behavior by directly assigning the rock …
is proposed to simulate the rock mechanical behavior by directly assigning the rock …
Probabilistic learning and updating of a digital twin for composite material systems
This article presents an approach for characterizing and estimating statistical dependence
between a large number of observables in a composite material system. Conditional …
between a large number of observables in a composite material system. Conditional …