Study on Internal Flow Characteristics of Airless Spray Nozzles Based on an Improved Dynamic WALE Model

Cavitation flow within airless spray nozzles critically influences both atomization quality and nozzle longevity. However, its highly transient and multiphase-coupled nature poses significant challenges to the predictive accuracy of turbulence models. To improve numerical simulation fidelity, this study develops an improved dynamic Wall-Adapting Local Eddy-viscosity (WALE) subgrid-scale model for Large Eddy Simulation (LES). Building on the standard WALE formulation, the model incorporates a dynamic coefficient determined via the Germano identity and a least-squares approach, which enables it to adaptively capture the turbulence modulation effects induced by cavitation. Coupled with a Volume of Fluid (VOF) multiphase flow method, this framework is employed to systematically simulate the complex internal nozzle flow under varying spray pressures, coating viscosities, and surface tensions. Results indicate that the improved dynamic WALE model increases numerical stability by approximately 15% compared with the standard model. The internal flow can be partitioned into three regions: a potential-flow acceleration region, a cavitation-induced fluctuation region, and an outlet formation region. Within the cavitation-induced fluctuation region near the wall, cavitation generates a local double-peaked velocity profile and pronounced pressure pulsations. Cavitation intensity increases approximately linearly with spray pressure but decreases with increasing viscosity and surface tension. Both the discharge coefficient and velocity coefficient decrease linearly with increasing cavitation number, indicating that moderate cavitation can enhance instantaneous throughput by altering the flow-field structure. Finally, outflow mass-flow experiments validate the numerical model’s reliability: the improved dynamic WALE model achieves prediction errors ranging from 0.47% to 11.91%, substantially outperforming the standard WALE model, which has errors ranging from 1.27% to 21.10%.