A ray-tracing-enhanced Lagrangian particle tracking framework for ice accretion applications

This paper presents the development of a Lagrangian particle-tracking solver for ice accretion simulations on distributed-memory systems for aircraft applications. The solver is implemented within CHAMPS, an in-house multiphysics framework. The primary aim of this work is to enhance the robustness, accuracy, and computational efficiency of Lagrangian droplet tracking. To this end, a face-to-face tracking scheme is employed for robust boundary-intersection detection, combined with a ray-tracing–enhanced bilinear patch intersection technique to improve performance and scalability. An impact-neighbor identification strategy enables smooth water-catch distributions with a reduced number of droplets, while a hybrid Eulerian– Lagrangian adaptive seeding method automates droplet initialization. Supercooled large droplet (SLD) effects are incorporated to better align predictions with experimental data. A scalable parallel implementation is employed to enable efficient execution on distributed-memory architectures. Results demonstrate favorable verification against Eulerian formulations and improved accuracy relative to experimental data. Compared to existing Lagrangian tracking approaches, the proposed framework provides improved robustness in boundary-intersection detection, increased accuracy in collection efficiency predictions, and reduced computational cost, while maintaining scalability on distributed-memory systems.