Numerical simulation on gas atomization process of nano-particle-reinforced copper-based composite melts

Copper-based composites reinforced with ceramic nanoparticles exhibit superior mechanical and thermophysical properties, making them critical materials for demanding applications such as ultra-high voltage transmission and semiconductor packaging. However, the controllable production of high-quality powder feedstock via gas atomization—essential for additive manufacturing—remains challenging for these composites, primarily due to the significant scale disparity between the particle reinforcement and the molten matrix. This study employs a computational fluid dynamics framework to elucidate the influence of TiB2 particle volume fraction (0–8%) on the gas atomization dynamics and powder characteristics of TiB2p/Cu composite melts. Adopting a liquid metal nanofluid model to describe the composite melt, the simulation integrates the shear stress transport k–ω turbulence model with a hybrid multiphase approach: the volume of fluid model for primary breakup and the discrete phase model with the Kelvin Helmholtz–Rayleigh Taylor submodel for secondary breakup. Results demonstrate that increasing particle volume fraction elevates melt viscosity and specific heat while reducing density, thermal conductivity, and surface tension. These property changes detrimentally affect atomization efficiency. At low particle fractions (≤2%), primary breakup forms an umbrella-like melt structure conducive to fragmentation. Higher fractions (≥4%) promote viscous “inverse fountain” structures near the delivery tube outlet, hindering primary droplet formation. Concurrently, the recirculation zone shortens (from 26.50 at 0% to 24.74 mm at 8%), suction pressure weakens (|ΔP| from 5.05 to 3.46 kPa), and primary droplet size increases (0.22–0.30 mm). Consequently, secondary breakup yields coarser powders, with median particle size (d50) rising from 30.54 (0%) to 51.37 μm (8%) and fine powder yield (δ53) declining from 79.91% to 52.42%. Experimental validation confirms the model's accuracy (d50 error: −13.74% at 3.9% particles). This work establishes a quantitative link between particle loading, melt properties, and atomization outcomes, providing essential guidance for optimizing powder production of particle-reinforced metal composites.