Effect of Velocity Alignment on the Packing of Active Particles

The dynamics of active particles are increasingly being leveraged to design and control micro-robotic swarms. Local interactions play a crucial role in the phase transitions of active particles; how the combined effects of alignment, short-range repulsion, and boundary interactions regulate their packing structure and collective order with different confinement scales remains less systematically explored. In this study, we investigate the packing of active particles within a confined region, focusing on the role of local interaction rules in shaping both the packing structure and the polar order parameter. The effects of key controlling variables related to local interaction rules, including interaction radius, repulsion radius, confined boundary radius, and noise strength, are numerically studied. Specifically, by comparing systems with and without velocity-alignment interactions, we reveal the role of alignment in dictating both structural and dynamical properties of the ensemble. To quantify the packing structure, we employ Voronoi tessellation to evaluate both local and global packing densities. The results show that strong confinement induces a jammed state in which alignment effects are suppressed, resulting in high global packing density and low polar order, regardless of the noise amplitude. Upon increasing the boundary radius beyond a critical threshold, the system unjams, enabling alignment interactions to significantly enhance both the polar order parameter and packing density. Interestingly, the relationship between global packing density and micro-structural parameters, such as coordination number and Voronoi tessellation metrics, is similar in the systems with and without alignment. Our results demonstrate that collective packing and phase behaviour of active matter are governed by the nontrivial interplay between alignment, confinement, and noise, with alignment interactions driving the transition from disordered to ordered states as geometric constraints are relaxed, offering critical insights for the design of targeted micro-robotic swarms and active microfluidic sorting systems.