Autor según el artículo: Reichardt I; Pallarès J; Sales-Pardo M; Guimerà R

Departamento: Enginyeria Química Enginyeria Mecànica

Autor/es de la URV: Guimera Manrique, Roger / Pallarés Curto, Jorge María / Sales Pardo, Marta

Palabras clave: Growth

Resumen: © 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.

Áreas temáticas: 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

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 0031-9007

Direcció de correo del autor: roger.guimera@urv.cat marta.sales@urv.cat jordi.pallares@urv.cat

Identificador del autor: 0000-0002-3597-4310 0000-0002-8140-6525 0000-0003-0305-2714

Fecha de alta del registro: 2023-02-26

Volumen de revista: 124

Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion

Enlace a la fuente original: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.084503

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Physical Review Letters. 124 (8): 084503-

Referencia de l'ítem segons les normes APA: Reichardt I; Pallarès J; Sales-Pardo M; Guimerà R (2020). Bayesian Machine Scientist to Compare Data Collapses for the Nikuradse Dataset. Physical Review Letters, 124(8), 084503-. DOI: 10.1103/PhysRevLett.124.084503

DOI del artículo: 10.1103/PhysRevLett.124.084503

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications