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In computational fluid dynamics, the modeling of paint application processes by electrostatic rotary bell sprayer is mostly performed using an Euler–Lagrange approach. The initial conditions of the discrete phase—position, velocity, size, and charge—have an essential influence on the resulting film thickness distribution and the total charge transferred to the object. Typically, so-called injection models are used to specify these initial conditions, whereby the determination of the injection model coefficients is crucial. In this paper, a framework is proposed that combines experimental input data, an injection model, and a metamodel-based optimization. The painting tests for the generation of input and validation data were carried out in a technical center in the industrial standard. The simulations were performed using ANSYSFluent. Initial droplet conditions could efficiently be determined via the framework so that the painting-specific objectives were achieved with reasonable accuracy. In addition to the framework, a turbulence study of the strongly swirled shaping air of this atomizer was carried out, whereby a substantial underestimation of the axial air velocity was found in the turbulence models being investigated. The initial droplet conditions were also used in this study to draw conclusions about the accuracy of the airflow simulation. The proposed framework can be adapted to other solvers and efficiently finds injection model coefficients for other paint applicators.

Initial droplet conditions in numerical spray painting by electrostatic rotary bell sprayers