Cargando…

Data-driven prediction of the equivalent sand-grain roughness

Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness [Formula: see text] . It is essential to estimate [Formula: see text] for accurate fluid dynamic problem modeling. While numerous roughness correlation f...

Descripción completa

Detalles Bibliográficos
Autores principales: Ma, Haoran, Li, Yuhao, Yang, Xin, Ye, Lili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625589/
https://www.ncbi.nlm.nih.gov/pubmed/37925532
http://dx.doi.org/10.1038/s41598-023-46564-4
_version_ 1785131164884795392
author Ma, Haoran
Li, Yuhao
Yang, Xin
Ye, Lili
author_facet Ma, Haoran
Li, Yuhao
Yang, Xin
Ye, Lili
author_sort Ma, Haoran
collection PubMed
description Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness [Formula: see text] . It is essential to estimate [Formula: see text] for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict [Formula: see text] in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict [Formula: see text] based on the selected surface parameters from previous DNS, LES, and experimental results for flow behavior over various surface roughness. The PSO-BP model’s ability to predict [Formula: see text] in the fully rough region is evaluated and compared with both the existing roughness correction formulas as well as the traditional BP model. An optimized polynomial function is also proposed to serve as a ‘white box’ for predicting [Formula: see text] . It turns out that the PSO-BP method has better performance in the evaluation metrics compared to other methods, yielding a Mean Absolute Error (MAE) of 0.0390, a Mean Squared Error (MSE) of 0.0026 and a Mean Absolute Percentage Error (MAPE) of 28.12%. This novel approach for estimating [Formula: see text] has practical applicability and holds promise for improving the precision and efficiency of calculations related to equivalent sand-grain roughness, and thus provides more accurate and effective solutions for CFD and other engineering applications.
format Online
Article
Text
id pubmed-10625589
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-106255892023-11-06 Data-driven prediction of the equivalent sand-grain roughness Ma, Haoran Li, Yuhao Yang, Xin Ye, Lili Sci Rep Article Surface roughness affects the near-wall fluid velocity profile and surface drag, and is commonly quantified by the equivalent sand-grain roughness [Formula: see text] . It is essential to estimate [Formula: see text] for accurate fluid dynamic problem modeling. While numerous roughness correlation formulas have been proposed to predict [Formula: see text] in the fully rough regime, most of them are restricted to certain roughness types, with various geometric parameters considered in each case, leading to ongoing disagreements regarding its parameterization and lack of universality. In this study, a Particle Swarm Optimized Backpropagation (PSO-BP) method is proposed to predict [Formula: see text] based on the selected surface parameters from previous DNS, LES, and experimental results for flow behavior over various surface roughness. The PSO-BP model’s ability to predict [Formula: see text] in the fully rough region is evaluated and compared with both the existing roughness correction formulas as well as the traditional BP model. An optimized polynomial function is also proposed to serve as a ‘white box’ for predicting [Formula: see text] . It turns out that the PSO-BP method has better performance in the evaluation metrics compared to other methods, yielding a Mean Absolute Error (MAE) of 0.0390, a Mean Squared Error (MSE) of 0.0026 and a Mean Absolute Percentage Error (MAPE) of 28.12%. This novel approach for estimating [Formula: see text] has practical applicability and holds promise for improving the precision and efficiency of calculations related to equivalent sand-grain roughness, and thus provides more accurate and effective solutions for CFD and other engineering applications. Nature Publishing Group UK 2023-11-04 /pmc/articles/PMC10625589/ /pubmed/37925532 http://dx.doi.org/10.1038/s41598-023-46564-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ma, Haoran
Li, Yuhao
Yang, Xin
Ye, Lili
Data-driven prediction of the equivalent sand-grain roughness
title Data-driven prediction of the equivalent sand-grain roughness
title_full Data-driven prediction of the equivalent sand-grain roughness
title_fullStr Data-driven prediction of the equivalent sand-grain roughness
title_full_unstemmed Data-driven prediction of the equivalent sand-grain roughness
title_short Data-driven prediction of the equivalent sand-grain roughness
title_sort data-driven prediction of the equivalent sand-grain roughness
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625589/
https://www.ncbi.nlm.nih.gov/pubmed/37925532
http://dx.doi.org/10.1038/s41598-023-46564-4
work_keys_str_mv AT mahaoran datadrivenpredictionoftheequivalentsandgrainroughness
AT liyuhao datadrivenpredictionoftheequivalentsandgrainroughness
AT yangxin datadrivenpredictionoftheequivalentsandgrainroughness
AT yelili datadrivenpredictionoftheequivalentsandgrainroughness