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Three cutting methods, i.e. electrical discharge machining (EDM), band saw (BS) and water jet (WJ), were used to prepare cuboid samples of closed-cell aluminium Alporas foam. On the end surfaces of the prepared samples, local roughness of mesoscopic structural components (i.e. cell wall, node and facet) and global roughness for the entire cut surface were measured using confocal microscopy. Furthermore, quasi-static uniaxial compression tests were performed with intermittent unloading-reloading. In the initial stage of compression, the measured loading stiffness and unloading elastic modulus are highest, intermediate and lowest for the EDM-cut, BS-cut and WJ-cut samples, respectively. This difference in measured compressive properties has been correlated with the difference in end-surface roughness associated with different cutting methods. However, the peak and plateau of compressive stress and the unloading elastic modulus in the plateau stage are insensitive to cutting method. An analytical model has been developed to elucidate the observed compressive behaviour and shed light on the responsible mechanisms.

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An experimental apparatus was designed to study the non-shock initiation reaction evolution process of a HMX (cyclotetramethylenete-tranitramine)-based pressed PBX (Plastic Bonded Explosive)-A column under the main constraint of the inertial mass of the explosive bulk, with strong bottom and circumferential confinements and with the strength of a PMMA plate cover as the threshold to control the internal reaction pressure. The HMX-based pressed PBX-A column was ignited by black powder. The experimental results show that the reaction violence was quite mild, and the estimated amount of explosive consumed at the time of the PMMA plate cracking was only 1.8% of the thickness. The velocity curve of the constrained surface shows that the internal reaction pressure at the initial stage of the structural deformation shows the “quasi-isobaric” characteristic, and the estimated reaction pressure at this stage was about 157.41 MPa.

ReaxFF-nn stands for Reactive Force Field (ReaxFF) with neural networks and is currently added to the General Utility Lattice Program (GULP) by modern FORTRAN programming. With GULP and ReaxFF-nn with parameters that are trained by our I-ReaxFF package, the thermal properties, crystal properties, energy minimization, etc., can be done with precision at the quasi-density functional theory (DFT) level. Compared to other Machine Learning Potentials (MLPs), we do not construct a thoroughly new machine learning potential, but just simply used a small neural network for the bond order and bond energy calculations, and the uncorrected bond orders are chosen as the input atomic feature vector. The advantage of To validate the model which we have coded in the GULP, we compared the forces of a random structure between the auto-differentiate package and our FORTRAN code, and the difference between them is about 10-6. Furthermore, we provided a systematical study of the thermal conductivity (κ) of graphene and carbon nanotubes (CNTs) through the phonon Boltzmann transport equation. The value of κ thermal conductivity of graphene is very close to the DFT calculations. Therefore, we declare that the potential we have trained for sp2 carbon can reach the quasi-DFT level. In this work, we report the thermal conductivity of CNTs calculated by ReaxFF-nn at quasi-DFT level are range from 107.032 to 310.019 W.m-1.K-1.

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