Natural fiber-reinforced polymer composites (NFRPCs) are being widely used in aerospace,
marine, automotive and healthcare applications due to their sustainability, low cost and …

This review shows the potential of using the Representative Volume Element concept for the
multi-scale simulation of 3D woven high performance thermoset composites manufactured …

A novel hybrid data-driven framework was introduced in this study for the prediction of fluid-
particle dynamics of submicron particles under hydrodynamic and Brownian forces in fibrous …

In liquid composite molding processes, variabilities in material and process conditions can
lead to distorted flow patterns during filling. These distortions appear not only within the …

Soil and rock, as typical porous media, widely exist in natural slopes and landslides and
underground reservoirs. Accurate predicting the permeability of porous media is crucial in …

Carbon fiber-reinforced polymer (CFRP) composites are extensively used in various
engineering applications due to their superior strength-to-weight ratio and excellent …

The use of data-driven methods for metal additive manufacturing (AM) is currently gaining
importance as indicated by the increasing number of scientific literature in this field …

Abstract In Resin Transfer Moulding (RTM), local variations in reinforcement properties
(porosity and permeability) and the formation of gaps along the reinforcement edges result …

This study developed a data-driven model for the prediction of fluid–particle dynamics by
coupling a flow surrogate model based on the deep convolutional neural network (CNN) …

Simulating water flow in fractured crystalline rock requires tackling its stochastic nature. We
aim to utilize the multilevel Monte Carlo method for cost-effective estimation of simulation …