Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste

This study aims to provide an efficient alternative for predicting creep modulus of cement paste based on Deep Convolutional Neural Network (DCNN). First, a microscale lattice model for short-term creep is adopted to build a database that contains 18,920 samples. Then, 3 DCNNs with different consecutive convolutional layers are built to learn...

Predictive analytical modelling and experimental validation of processing maps in additive manufacturing of nitinol alloys

Nitinol (NiTi) shape memory alloys fabricated by Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) have attracted much attention in recent years, as compared with conventional manufacturing processes it allows to produce Nitinol parts with high design complexity. Avoidance of defects during L-PBF is crucial for the production of...

An integrated methodology for the supply reliability analysis of multi-product pipeline systems under pumps failure

As the main way for the long-distance transportation of refined products, multi-products pipelines are of vital importance to the regional energy security. The supply reliability evaluation of multi-product pipeline systems can improve the effective response to unexpected disruptions and guarantee the reliable oil supply. Based on reliability...

A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

Carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain...