CNN-based flow field prediction for bus aerodynamics analysis
dc.contributor.author | García Fernández, Roberto | |
dc.contributor.author | Portal Porras, Koldo | |
dc.contributor.author | Irigaray, Oscar | |
dc.contributor.author | Ansa Otxoa, Zugatz | |
dc.contributor.author | Fernández Gámiz, Unai | |
dc.date.accessioned | 2024-01-16T17:48:35Z | |
dc.date.available | 2024-01-16T17:48:35Z | |
dc.date.issued | 2023-10 | |
dc.identifier.citation | Scientific Reports 13 : (2023) // Article ID 21213 | es_ES |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/10810/64041 | |
dc.description.abstract | The aim of this article is to evaluate the ability of a convolutional neural network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles. For training and testing the developed CNN, various CFD simulations of three different vehicle geometries have been conducted, considering the RANS-based k-ω SST turbulent model. Two geometries correspond to the SC7 and SC5 coach models of the bus manufacturer SUNSUNDEGUI and the third one corresponds to Ahmed body. By generating different variants of these three geometries, a large number of representations of the velocity and pressure fields are obtained that will be used to train, verify, and evaluate the convolutional neural network. To improve the accuracy of the CNN, the field representations obtained are discretized as a function of the expected velocity gradient, so that in the areas where there is a greater variation in velocity, the corresponding neuron is smaller. The results show good agreement between numerical results and CNN predictions, being the CNN able to accurately represent the velocity and pressure fields with very low errors. Additionally, a substantial improvement in the computational time needed for each simulation is appreciated, reducing it by four orders of magnitude. | es_ES |
dc.description.sponsorship | The authors appreciate the support of the government of Navarre through AEROSUN research program. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Nature | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.title | CNN-based flow field prediction for bus aerodynamics analysis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2023. 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/ | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.nature.com/articles/s41598-023-48419-4 | es_ES |
dc.identifier.doi | 10.1038/s41598-023-48419-4 | |
dc.departamentoes | Ingeniería Energética | es_ES |
dc.departamentoeu | Energia Ingenieritza | es_ES |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's license is described as © The Author(s) 2023. 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/