Investigating the effect of external force on the collision of an iron bullet with shear-thickening fluid nanocomposites using molecular dynamics simulation
Source abstract
Abstract
The main uses of bullets are as projectiles in firearms for military, law enforcement, and civilian purposes. However, bullets have significant industrial applications beyond their role in weaponry, such as precision drilling, cutting, and shaping of hard materials. Steel bullets have a very resistant structure and will not be damaged if exposed to corrosion. Other applications that are resistant to strike and projectiles, such as the automotive and aerospace industries, are based on fibers or fabrics with high toughness and tensile strength, such as Kevlar and p -aramid fibers, which are impregnated with some thermoplastic or thermoset polymers. Therefore, in the upcoming research, the impact of external force (EF) with various values (0.1, 0.2, 0.3, 0.4, and 0.5 V/Å) on the strength of Kevlar nanofibers reinforced with silicon dioxide (SiO2) nanoparticles and ethylene glycol (EG) has been investigated using LAMMPS software. EG and SiO2 nanoparticles with 10 and 2 vol.% were added to Kevlar fabrics, creating a shear thickening fluid (STF) nanocomposite. The energy parameters of the interaction between the particles, collision velocity, and the center of mass have been calculated. The findings of the research indicate an increase in the interaction energy from 111271.85 to 154351.15 kcal/mol, an increase in the collision velocity from 0.7046 to 1.5812 Å/fs and an increase in the center of mass from 0.62671 to 1.1670 Å due to the EF applied from 0.2 to 0.4 (kcal/mol)/Å. These results show EF should be optimized in actual cases for the collision process to occur effectively.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: High-Velocity Impact and Material Behavior · Energetic Materials and Combustion · Ion-surface interactions and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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