Ashton Hetherington , Daniel Serfaty , Adrián Corrochano , Julio Soria , Soledad Le Clainche
{"title":"Data repairing and resolution enhancement using data-driven modal decomposition and deep learning","authors":"Ashton Hetherington , Daniel Serfaty , Adrián Corrochano , Julio Soria , Soledad Le Clainche","doi":"10.1016/j.expthermflusci.2024.111241","DOIUrl":null,"url":null,"abstract":"<div><p>This paper introduces a new series of methods which combine modal decomposition algorithms, such as singular value decomposition and high-order singular value decomposition, and deep learning architectures to repair, enhance, and increase the quality and precision of numerical and experimental data. A combination of two- and three-dimensional, numerical and experimental datasets are used to demonstrate the reconstruction capacity of the presented methods, showing that these methods can be used to reconstruct any type of dataset, showing outstanding results when applied to highly complex data, which is noisy. The combination of benefits of these techniques results in a series of data-driven methods which are capable of repairing and/or enhancing the resolution of a dataset by identifying the underlying physics that define the data, which is incomplete or under-resolved, filtering any existing noise. These methods and the <em>Python</em> codes are included in the first release of ModelFLOWs-app.<span><sup>1</sup></span></p></div>","PeriodicalId":12294,"journal":{"name":"Experimental Thermal and Fluid Science","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0894177724001109/pdfft?md5=858456a15bbabb6c56a44d121e8995ed&pid=1-s2.0-S0894177724001109-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Experimental Thermal and Fluid Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0894177724001109","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This paper introduces a new series of methods which combine modal decomposition algorithms, such as singular value decomposition and high-order singular value decomposition, and deep learning architectures to repair, enhance, and increase the quality and precision of numerical and experimental data. A combination of two- and three-dimensional, numerical and experimental datasets are used to demonstrate the reconstruction capacity of the presented methods, showing that these methods can be used to reconstruct any type of dataset, showing outstanding results when applied to highly complex data, which is noisy. The combination of benefits of these techniques results in a series of data-driven methods which are capable of repairing and/or enhancing the resolution of a dataset by identifying the underlying physics that define the data, which is incomplete or under-resolved, filtering any existing noise. These methods and the Python codes are included in the first release of ModelFLOWs-app.1
期刊介绍:
Experimental Thermal and Fluid Science provides a forum for research emphasizing experimental work that enhances fundamental understanding of heat transfer, thermodynamics, and fluid mechanics. In addition to the principal areas of research, the journal covers research results in related fields, including combined heat and mass transfer, flows with phase transition, micro- and nano-scale systems, multiphase flow, combustion, radiative transfer, porous media, cryogenics, turbulence, and novel experimental techniques.