{
  "abstract": "This work presents an experimentally grounded identification and validation framework for a fractional-order three-node thermal network (F3N) describing the dynamic behavior of a thermoelectric cooling system. The framework combines experimental-data-driven parameter-bound estimation, Latin hypercube sampling, and multi-objective evolutionary optimization (NSGA-II) across ten independent seeds, simultaneously minimizing cold- and hot-side temperature prediction errors and their dynamic mismatch. The identified network reproduces the cold- and hot-side transient dynamics on the identification dataset with low RMSE and 95%-calibrated bootstrap prediction bands. Repeatability of the underlying measurements was confirmed by comparing three independent open-loop trials under the identical excitation protocol: cross-trial correlations exceed 0.998 for both channels, with a mean cross-trial standard deviation of 0.20 °C (Tc) and 0.22 °C (Th), and the identification dataset used throughout is the pointwise average of these three trials. A Fisher-information-based identifiability analysis shows that this predictive accuracy coexists with limited practical identifiability at the level of individual physical parameters: 28 of 55 parameter pairs are correlated above |ρ|=0.8, only the transport delay and the Peltier gain are individually well-determined, and a grid-based refit at fixed fractional order indicates that the identified value β^=0.6905 is not distinguishable, in terms of fit quality, from a commensurate integer-order formulation. External validation on three independent closed-loop trials under different operating conditions shows limited transferability of the identified parameters: the cold-side channel retains predictive value, while the hot-side channel does not, a pattern consistent with a non-independent ambient-temperature reference in the validation dataset. The linearized network is shown to be stable under Matignon’s criterion, and a graph-theoretic reformulation distinguishes the physical thermal network from a statistical parameter-compensation network derived from the identifiability analysis. The results characterize both the predictive capability and the parameter-level limitations of a fractional-order network identified from thermal measurements and provide an experimentally grounded basis for analyzing memory-driven thermal dynamics in thermoelectric systems.",
  "abstract_shown": true,
  "affiliations": [
    "University Institute of Intelligent Systems and Numerical Applications in Engineering, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain",
    "Department of Process Engineering, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Miguel F. Ferrer Pareja",
      "orcid": "0009-0000-7518-3583"
    },
    {
      "affs": [
        1
      ],
      "name": "Carlos Sánchez Morales",
      "orcid": "0000-0001-5520-7154"
    },
    {
      "affs": [
        1
      ],
      "name": "Federico León Zerpa",
      "orcid": "0000-0003-2284-8400"
    },
    {
      "affs": [
        0
      ],
      "name": "Alejandro Ramos Martín",
      "orcid": "0000-0001-5759-4469"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8zw70m5nqp67m.bib",
  "cited_by": 0,
  "doi": "10.3390/fractalfract10100695",
  "fulltext_url": null,
  "id": "8zw70m5nqp67m",
  "issns": [
    "2504-3110"
  ],
  "landing_url": "https://www.mdpi.com/2504-3110/10/10/695",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2207|Advanced Control Systems Design"
    ]
  ],
  "openalex": "W7220410473",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/8zw70m5nqp67m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:32Z",
      "native_id": "10.3390/fractalfract10100695",
      "source": "crossref",
      "url": "https://www.mdpi.com/2504-3110/10/10/695"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2504-3110",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "A Fractional-Order Dynamical Network Model for Thermoelectric Cooling: Identification and Parameter-Space Structure",
  "title_text": "A Fractional-Order Dynamical Network Model for Thermoelectric Cooling: Identification and Parameter-Space Structure",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:32Z",
  "url": "https://research.draconitic.com/paper/8zw70m5nqp67m",
  "venue": "Fractal and Fractional",
  "versions": []
}