Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
Abstract
Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We explore a setup where in-silico sensors, based on a recently proposed real-world design, are tracked using data from an accelerometer and magnetometer available from a built-in inertial measurement unit. Filtering algorithms, using a bespoke dynamical model, are used to process these readings into position estimates. We compare tracking performance of an extended Kalman filter, a particle filter and the unscented Kalman filter implemented in the pykalman library. Our numerical experiments track in-silico particles moving in an analytically given three dimensional vortex as well as in the experimentally measured flow-field of a lab-scale stirred tank reactor. Using the Maxey-Riley-Gatignol equations for the movement of inertial particles as ground-truth, we demonstrate that trajectories can be reconstructed from noisy synthetic data with errors below 10%.
Versions
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BibTeX
@misc{rathi2026tracking,
title = {{Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor}},
author = {Vamika Rathi and Fatima Sehar and Finn Sommer and Sebastian Goetschel and Eike Steuwe and Alexandra von Kameke and Daniel Ruprecht},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2606.13099},
eprint = {2606.13099},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.13099},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
AU - Vamika Rathi
AU - Fatima Sehar
AU - Finn Sommer
AU - Sebastian Goetschel
AU - Eike Steuwe
AU - Alexandra von Kameke
AU - Daniel Ruprecht
PY - 2026
DA - 2026/06/11
PB - arXiv
DO - 10.48550/arxiv.2606.13099
UR - https://arxiv.org/abs/2606.13099
AB - Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We explore a setup where in-silico sensors, based on a recently proposed real-world design, are tracked using data from an accelerometer and magnetometer available from a built-in inertial measurement unit. Filtering algorithms, using a bespoke dynamical model, are used to process these readings into position estimates. We compare tracking performance of an extended Kalman filter, a particle filter and the unscented Kalman filter implemented in the pykalman library. Our numerical experiments track in-silico particles moving in an analytically given three dimensional vortex as well as in the experimentally measured flow-field of a lab-scale stirred tank reactor. Using the Maxey-Riley-Gatignol equations for the movement of inertial particles as ground-truth, we demonstrate that trajectories can be reconstructed from noisy synthetic data with errors below 10%.
ER -