The analysis and quantification of fluid transport and mixing in chemical reactors is of great interest in order to avoid dead zones and to control heterogeneities in concentration distributions. From a Lagrangian perspective, coherent flow structures play a central role in this context. In the past few years, different computational methods have been developed to identify such finite-time coherent sets directly from trajectories of fluid particles. Such type of trajectory data is obtained via numerical simulations or lab experiments (e.g. by time-resolved particle tracking (4D-PTV) or by Lagrangian sensors).

In this contribution, we demonstrate the application of different trajectory-based approaches for the identification of coherent flow structures in stirred tank reactors. For this purpose, several recently proposed methods, such as spectral clustering of trajectories [1,2] or single-trajectory diagnostics [3] have been implemented in Python to improve performance and facilitate embedding.