The analysis of fluid transport and mixing in chemical reactors is crucial to avoid dead zones and control concentration distributions. From a Lagrangian perspective, coherent flow structures are key. Recent computational methods can identify finite-time coherent sets directly from fluid particle trajectories obtained from numerical simulations or laboratory experiments such as 4D-PTV or Lagrangian sensors.

This contribution demonstrates the application of trajectory-based approaches to identify coherent flow structures in chemical reactors and analyze their transport properties. Several recently proposed methods, such as spectral clustering of trajectories or single trajectory diagnostics, have been implemented in Python to improve performance and ease embedding.

Acknowledgements
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB1615 – 503850735 – TP B05