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OpenLPT (Open-source Lagrangian Particle Tracking) is a specific computational framework for three-dimensional particle tracking introduced by Tan et al. in 2020.[1] The method is designed to address challenges in high-concentration particle tracking and to reduce ghost particles in volumetric reconstruction.
Lagrangian particle tracking (LPT) is a class of experimental techniques used in fluid mechanics to reconstruct particle trajectories in three-dimensional space.[2] Established approaches such as Shake-the-Box (STB) have demonstrated high accuracy in resolving particle trajectories, although challenges remain in handling high seeding densities and suppressing ghost particles arising from reconstruction ambiguities.[2]
OpenLPT represents a specific implementation within the broader class of Lagrangian particle tracking methods, rather than the technique itself.[2]
OpenLPT builds upon existing LPT principles and introduces procedures for particle reconstruction and trajectory linking under high particle concentrations.[1] The framework incorporates strategies intended to reduce ghost particles and improve robustness in dense particle fields.[1]
OpenLPT has been referenced and discussed in subsequent studies on particle tracking methodologies. In particular, it has been used as a reference framework in the development and evaluation of alternative LPT approaches, indicating its role as a distinct method within the field.[3][4]
The OpenLPT framework has been made available as open-source software through public code repositories.[1]
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