{"title":"旋转系统中非线性纳米流体流动的预测见解:一种机器学习方法","authors":"Naveed Ahmad Khan, Muhammad Sulaiman, Benzhou Lu","doi":"10.1007/s00366-024-01993-1","DOIUrl":null,"url":null,"abstract":"<p>This research seeks to explore the heat shift mechanisms in a rotating system that contains a hybrid nanofluid comprising of graphene oxide and copper particles mixed with pure water, using a novel methodology. The fluid flow in a rotating system is described by mathematical equations that involve nonlinear partial differential equations (PDEs). These equations are simplified by using similarity transformations, resulting in a system of ordinary differential equations. In general, it is not feasible to find a closed-form analytical solution for nonlinear ordinary differential equations (ODEs), which implies that determining an exact mathematical expression that characterizes the behavior of the solution to such ODEs is often challenging or impossible. To that end, we have utilized the controlled learning procedure of machine learning algorithms to predict the solutions for the nonlinear nanofluid problem flowing in the rotating system. The surrogated model are developed for different cases and scenarios, to review the might of differences in various physical parameters on the profiles of the fluid. Furthermore, the solutions are supported by performing an extensive statistical analysis based on different errors. It is concluded that machine learning-based method can potentially provide insights into the underlying physics of nonlinear flow problems, which can aid in the progress of more advanced and accurate models for prognosticating the behavior of nonlinear systems.</p>","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":null,"pages":null},"PeriodicalIF":8.7000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive insights into nonlinear nanofluid flow in rotating systems: a machine learning approach\",\"authors\":\"Naveed Ahmad Khan, Muhammad Sulaiman, Benzhou Lu\",\"doi\":\"10.1007/s00366-024-01993-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This research seeks to explore the heat shift mechanisms in a rotating system that contains a hybrid nanofluid comprising of graphene oxide and copper particles mixed with pure water, using a novel methodology. The fluid flow in a rotating system is described by mathematical equations that involve nonlinear partial differential equations (PDEs). These equations are simplified by using similarity transformations, resulting in a system of ordinary differential equations. In general, it is not feasible to find a closed-form analytical solution for nonlinear ordinary differential equations (ODEs), which implies that determining an exact mathematical expression that characterizes the behavior of the solution to such ODEs is often challenging or impossible. To that end, we have utilized the controlled learning procedure of machine learning algorithms to predict the solutions for the nonlinear nanofluid problem flowing in the rotating system. The surrogated model are developed for different cases and scenarios, to review the might of differences in various physical parameters on the profiles of the fluid. Furthermore, the solutions are supported by performing an extensive statistical analysis based on different errors. It is concluded that machine learning-based method can potentially provide insights into the underlying physics of nonlinear flow problems, which can aid in the progress of more advanced and accurate models for prognosticating the behavior of nonlinear systems.</p>\",\"PeriodicalId\":11696,\"journal\":{\"name\":\"Engineering with Computers\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.7000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering with Computers\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00366-024-01993-1\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering with Computers","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00366-024-01993-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
Predictive insights into nonlinear nanofluid flow in rotating systems: a machine learning approach
This research seeks to explore the heat shift mechanisms in a rotating system that contains a hybrid nanofluid comprising of graphene oxide and copper particles mixed with pure water, using a novel methodology. The fluid flow in a rotating system is described by mathematical equations that involve nonlinear partial differential equations (PDEs). These equations are simplified by using similarity transformations, resulting in a system of ordinary differential equations. In general, it is not feasible to find a closed-form analytical solution for nonlinear ordinary differential equations (ODEs), which implies that determining an exact mathematical expression that characterizes the behavior of the solution to such ODEs is often challenging or impossible. To that end, we have utilized the controlled learning procedure of machine learning algorithms to predict the solutions for the nonlinear nanofluid problem flowing in the rotating system. The surrogated model are developed for different cases and scenarios, to review the might of differences in various physical parameters on the profiles of the fluid. Furthermore, the solutions are supported by performing an extensive statistical analysis based on different errors. It is concluded that machine learning-based method can potentially provide insights into the underlying physics of nonlinear flow problems, which can aid in the progress of more advanced and accurate models for prognosticating the behavior of nonlinear systems.
期刊介绍:
Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.