{
  "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%.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Computers & Chemical Engineering 216: 109911 (2026)",
  "arxiv": "2606.13099",
  "author_total": 7,
  "authors": [
    {
      "name": "Vamika Rathi"
    },
    {
      "name": "Fatima Sehar"
    },
    {
      "name": "Finn Sommer"
    },
    {
      "name": "Sebastian Goetschel"
    },
    {
      "name": "Eike Steuwe"
    },
    {
      "name": "Alexandra von Kameke"
    },
    {
      "name": "Daniel Ruprecht"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8b141ap098ewe.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.13099",
  "fulltext_url": null,
  "id": "8b141ap098ewe",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.13099",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.13099",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8b141ap098ewe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:19Z",
      "native_id": "2606.13099",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.13099"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor",
  "title_text": "Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:19Z",
  "url": "https://research.draconitic.com/paper/8b141ap098ewe",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.compchemeng.2026.109911",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}