Articles producció científica> Enginyeria Química

Bayesian Machine Scientist to Compare Data Collapses for the Nikuradse Dataset

  • Identification data

    Identifier: imarina:6389816
    Authors:
    Reichardt, IgnasiPallares, JordiSales-Pardo, MartaGuimera, Roger
    Abstract:
    © 2020 American Physical Society. Ever since Nikuradse's experiments on turbulent friction in 1933, there have been theoretical attempts to describe his measurements by collapsing the data into single-variable functions. However, this approach, which is common in other areas of physics and in other fields, is limited by the lack of rigorous quantitative methods to compare alternative data collapses. Here, we address this limitation by using an unsupervised method to find analytic functions that optimally describe each of the data collapses for the Nikuradse dataset. By descaling these analytic functions, we show that a low dispersion of the scaled data does not guarantee that a data collapse is a good description of the original data. In fact, we find that, out of all the proposed data collapses, the original one proposed by Prandtl and Nikuradse over 80 years ago provides the best description of the data so far, and that it also agrees well with recent experimental data, provided that some model parameters are allowed to vary across experiments.
  • Others:

    Author, as appears in the article.: Reichardt, Ignasi; Pallares, Jordi; Sales-Pardo, Marta; Guimera, Roger
    Department: Enginyeria Química Enginyeria Mecànica
    URV's Author/s: Guimera Manrique, Roger / Pallarés Curto, Jorge María / Pallarès Marzal, Josep / Sales Pardo, Marta
    Keywords: Growth
    Abstract: © 2020 American Physical Society. Ever since Nikuradse's experiments on turbulent friction in 1933, there have been theoretical attempts to describe his measurements by collapsing the data into single-variable functions. However, this approach, which is common in other areas of physics and in other fields, is limited by the lack of rigorous quantitative methods to compare alternative data collapses. Here, we address this limitation by using an unsupervised method to find analytic functions that optimally describe each of the data collapses for the Nikuradse dataset. By descaling these analytic functions, we show that a low dispersion of the scaled data does not guarantee that a data collapse is a good description of the original data. In fact, we find that, out of all the proposed data collapses, the original one proposed by Prandtl and Nikuradse over 80 years ago provides the best description of the data so far, and that it also agrees well with recent experimental data, provided that some model parameters are allowed to vary across experiments.
    Thematic Areas: Química Physics, multidisciplinary Physics and astronomy (miscellaneous) Physics and astronomy (all) Physics Medicina ii Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General physics and astronomy General medicine Filosofía Farmacia Ensino Engenharias iv Engenharias iii Engenharias ii Economia Ciências biológicas ii Ciências agrárias i Ciência da computação Biotecnología Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 0031-9007
    Author's mail: roger.guimera@urv.cat josep.pallares@urv.cat jordi.pallares@urv.cat marta.sales@urv.cat
    Author identifier: 0000-0002-3597-4310 0000-0001-7221-5383 0000-0003-0305-2714 0000-0002-8140-6525
    Record's date: 2024-10-19
    Journal volume: 124
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.084503
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Physical Review Letters. 124 (8): 084503-
    APA: Reichardt, Ignasi; Pallares, Jordi; Sales-Pardo, Marta; Guimera, Roger (2020). Bayesian Machine Scientist to Compare Data Collapses for the Nikuradse Dataset. Physical Review Letters, 124(8), 084503-. DOI: 10.1103/PhysRevLett.124.084503
    Article's DOI: 10.1103/PhysRevLett.124.084503
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    Physics,Physics and Astronomy (Miscellaneous),Physics, Multidisciplinary
    Growth
    Química
    Physics, multidisciplinary
    Physics and astronomy (miscellaneous)
    Physics and astronomy (all)
    Physics
    Medicina ii
    Materiais
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    General physics and astronomy
    General medicine
    Filosofía
    Farmacia
    Ensino
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Economia
    Ciências biológicas ii
    Ciências agrárias i
    Ciência da computação
    Biotecnología
    Astronomia / física
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