{"title":"利用 Galerkin 有限元法计算多纳米级粒子交叉流体中广义热传输增强的三维建模与模拟","authors":"","doi":"10.1007/s40571-024-00727-w","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Using ionized fluids in a magnetic field has numerous applications in engineering and industry. Therefore, heat transport in ionized fluids with thermal memory effects should be predicted using numerical simulations. To achieve this objective, the generalized heat transport in ionized fluid (following a cross-rheological constitutive relation) is modeled, and the governing system is solved numerically using the Galerkin finite element method (GFEM). After the successful implementation of GFEM, the solutions are made grid-independent and convergent. Furthermore, the results are validated with existing literature. Our numerical results show that the memory effects are favorable factors in enhancing heat transport. The Joule heating and heat generation are the characteristics that adversely affect thermal performance. Therefore, heat-absorbing and non-Ohmic dissipative fluids are recommended for optimized heat transport. Similarly, using ionized fluid in the presence of a magnetic field is recommended, as Hall and ion slip currents significantly reduce the Ohmic dissipation in the fluid during heat transport. Hall and ion slip currents induced by the movement of ionized fluid subjected to a variable magnetic field tend to cancel out the retarding effects of Lorentz force, due to which the friction force between fluid particles and the solid surface is reduced. Thus, it is concluded that if stress at the surface caused by fluid movement is required to minimize, then ionized fluid is recommended as a working fluid for transporting heat. Thermal memory effects in mono-nanofluid are stronger than those in fluids with di- and tri-nanoparticles. Moreover, the heat transfer of fluid dispersed with tri-nanoparticles is the best working fluid for thermal efficiency in transporting heat.</p>","PeriodicalId":524,"journal":{"name":"Computational Particle Mechanics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational 3D-modeling and simulations of generalized heat transport enhancement in cross-fluids with multi-nanoscale particles using Galerkin finite element method\",\"authors\":\"\",\"doi\":\"10.1007/s40571-024-00727-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Abstract</h3> <p>Using ionized fluids in a magnetic field has numerous applications in engineering and industry. Therefore, heat transport in ionized fluids with thermal memory effects should be predicted using numerical simulations. To achieve this objective, the generalized heat transport in ionized fluid (following a cross-rheological constitutive relation) is modeled, and the governing system is solved numerically using the Galerkin finite element method (GFEM). After the successful implementation of GFEM, the solutions are made grid-independent and convergent. Furthermore, the results are validated with existing literature. Our numerical results show that the memory effects are favorable factors in enhancing heat transport. The Joule heating and heat generation are the characteristics that adversely affect thermal performance. Therefore, heat-absorbing and non-Ohmic dissipative fluids are recommended for optimized heat transport. Similarly, using ionized fluid in the presence of a magnetic field is recommended, as Hall and ion slip currents significantly reduce the Ohmic dissipation in the fluid during heat transport. Hall and ion slip currents induced by the movement of ionized fluid subjected to a variable magnetic field tend to cancel out the retarding effects of Lorentz force, due to which the friction force between fluid particles and the solid surface is reduced. Thus, it is concluded that if stress at the surface caused by fluid movement is required to minimize, then ionized fluid is recommended as a working fluid for transporting heat. Thermal memory effects in mono-nanofluid are stronger than those in fluids with di- and tri-nanoparticles. Moreover, the heat transfer of fluid dispersed with tri-nanoparticles is the best working fluid for thermal efficiency in transporting heat.</p>\",\"PeriodicalId\":524,\"journal\":{\"name\":\"Computational Particle Mechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Particle Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s40571-024-00727-w\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Particle Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40571-024-00727-w","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Computational 3D-modeling and simulations of generalized heat transport enhancement in cross-fluids with multi-nanoscale particles using Galerkin finite element method
Abstract
Using ionized fluids in a magnetic field has numerous applications in engineering and industry. Therefore, heat transport in ionized fluids with thermal memory effects should be predicted using numerical simulations. To achieve this objective, the generalized heat transport in ionized fluid (following a cross-rheological constitutive relation) is modeled, and the governing system is solved numerically using the Galerkin finite element method (GFEM). After the successful implementation of GFEM, the solutions are made grid-independent and convergent. Furthermore, the results are validated with existing literature. Our numerical results show that the memory effects are favorable factors in enhancing heat transport. The Joule heating and heat generation are the characteristics that adversely affect thermal performance. Therefore, heat-absorbing and non-Ohmic dissipative fluids are recommended for optimized heat transport. Similarly, using ionized fluid in the presence of a magnetic field is recommended, as Hall and ion slip currents significantly reduce the Ohmic dissipation in the fluid during heat transport. Hall and ion slip currents induced by the movement of ionized fluid subjected to a variable magnetic field tend to cancel out the retarding effects of Lorentz force, due to which the friction force between fluid particles and the solid surface is reduced. Thus, it is concluded that if stress at the surface caused by fluid movement is required to minimize, then ionized fluid is recommended as a working fluid for transporting heat. Thermal memory effects in mono-nanofluid are stronger than those in fluids with di- and tri-nanoparticles. Moreover, the heat transfer of fluid dispersed with tri-nanoparticles is the best working fluid for thermal efficiency in transporting heat.
期刊介绍:
GENERAL OBJECTIVES: Computational Particle Mechanics (CPM) is a quarterly journal with the goal of publishing full-length original articles addressing the modeling and simulation of systems involving particles and particle methods. The goal is to enhance communication among researchers in the applied sciences who use "particles'''' in one form or another in their research.
SPECIFIC OBJECTIVES: Particle-based materials and numerical methods have become wide-spread in the natural and applied sciences, engineering, biology. The term "particle methods/mechanics'''' has now come to imply several different things to researchers in the 21st century, including:
(a) Particles as a physical unit in granular media, particulate flows, plasmas, swarms, etc.,
(b) Particles representing material phases in continua at the meso-, micro-and nano-scale and
(c) Particles as a discretization unit in continua and discontinua in numerical methods such as
Discrete Element Methods (DEM), Particle Finite Element Methods (PFEM), Molecular Dynamics (MD), and Smoothed Particle Hydrodynamics (SPH), to name a few.