Entropy Generation and Thermal Performance Analysis of MHD Ternary Hybrid Nanofluid Jeffery–Hamel Flow Under Heat Generation/Absorption

IF 2.9 4区 工程技术 Q1 MULTIDISCIPLINARY SCIENCES
Dhahri Hacen, Mhimid Abdallah, Aamir Ali, Kezzar Mohamed, Sari Mohamed Rafik, Sahar Ahmed Idris, Ibrahim Mahariq
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data-semantic-type=\"infixop\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"17\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">F</mi><mo data-semantic-=\"\" data-semantic-added=\"true\" data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"17\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">⁢</mo><msub data-semantic-=\"\" data-semantic-children=\"1,2\" data-semantic-parent=\"17\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"3\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">e</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"3\" data-semantic-role=\"integer\" data-semantic-type=\"number\">3</mn></msub><mo data-semantic-=\"\" data-semantic-added=\"true\" data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"17\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">⁢</mo><msub data-semantic-=\"\" data-semantic-children=\"4,5\" data-semantic-parent=\"17\" data-semantic-role=\"latinletter\" data-semantic-type=\"subscript\"><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"6\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">O</mi><mn data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"6\" data-semantic-role=\"integer\" data-semantic-type=\"number\">4</mn></msub></mrow><mspace width=\"0.16em\"></mspace><mo data-semantic-=\"\" data-semantic-operator=\"infixop,−\" data-semantic-parent=\"18\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\">−</mo><mspace width=\"0.16em\"></mspace><mi data-semantic-=\"\" data-semantic-font=\"normal\" data-semantic-parent=\"18\" data-semantic-role=\"unknown\" data-semantic-type=\"identifier\">SWCNT</mi><mspace width=\"0.16em\"></mspace><mo data-semantic-=\"\" data-semantic-operator=\"infixop,−\" data-semantic-parent=\"18\" data-semantic-role=\"subtraction\" data-semantic-type=\"operator\">−</mo><mspace width=\"0.16em\"></mspace><mi data-semantic-=\"\" data-semantic-font=\"normal\" data-semantic-parent=\"18\" data-semantic-role=\"unknown\" data-semantic-type=\"identifier\">MWCNT</mi></mrow>${\\mathrm{F}}{{{\\mathrm{e}}}_3}{{{\\mathrm{O}}}_4}\\, - \\,{\\mathrm{SWCNT}}\\, - \\,{\\mathrm{MWCNT}}$</annotation></semantics></math></mjx-assistive-mml></mjx-container> nanoparticles) is considered. By utilizing the similarity transformations, the fundamental partial differential equations derived from mathematical modeling are transformed into ordinary differential equations. Thereafter, the computational solution is obtained numerically and analytically. The analytical solution is constructed using an efficient computational technique called the Adomian Decomposition method. To ensure validation, the present results for special cases are compared with those obtained using the Runge–Kutta–Fehlberg 4th–5th order (RKF-45) method. The effects of physical factors on velocity, temperature, and entropy generation are shown graphically. Additionally, the impact of multiple variables on the entropy generation number is demonstrated and examined. It is found that the ternary nanofluid velocity boosts with the increase of the Hartmann number, and hence the reversal flow is entirely delayed. Results obtained also reveal that the presence of ternary nanoparticles within base fluid enhances significantly the heat transfer rate (Nusselt number) in both convergent and divergent channels. In addition, it is also found that the heat source raises the temperature of ternary hybrid nanofluid flow for both converging and diverging channels, whereas the heat sink shows a reverse behavior and mainly leads to a cooling effect. Finally, the heat source/sink parameter, the radiation parameter, and the magnetic field strongly influence the Nusselt number.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"46 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Theory and Simulations","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/adts.202401120","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Heat transfer and hydromagnetic flow of ternary hybrid nanofluids between non-parallel plates are presented in this research work. Lorentz force, nanoparticle shape, heat sink/source, non-linear solar radiation, and stretching/shrinking wall effects are considered. A polymer base fluid containing hybrid nanoparticles (i.e., Fe3O4SWCNTMWCNT${\mathrm{F}}{{{\mathrm{e}}}_3}{{{\mathrm{O}}}_4}\, - \,{\mathrm{SWCNT}}\, - \,{\mathrm{MWCNT}}$ nanoparticles) is considered. By utilizing the similarity transformations, the fundamental partial differential equations derived from mathematical modeling are transformed into ordinary differential equations. Thereafter, the computational solution is obtained numerically and analytically. The analytical solution is constructed using an efficient computational technique called the Adomian Decomposition method. To ensure validation, the present results for special cases are compared with those obtained using the Runge–Kutta–Fehlberg 4th–5th order (RKF-45) method. The effects of physical factors on velocity, temperature, and entropy generation are shown graphically. Additionally, the impact of multiple variables on the entropy generation number is demonstrated and examined. It is found that the ternary nanofluid velocity boosts with the increase of the Hartmann number, and hence the reversal flow is entirely delayed. Results obtained also reveal that the presence of ternary nanoparticles within base fluid enhances significantly the heat transfer rate (Nusselt number) in both convergent and divergent channels. In addition, it is also found that the heat source raises the temperature of ternary hybrid nanofluid flow for both converging and diverging channels, whereas the heat sink shows a reverse behavior and mainly leads to a cooling effect. Finally, the heat source/sink parameter, the radiation parameter, and the magnetic field strongly influence the Nusselt number.

Abstract Image

产热/吸热条件下MHD三元杂化纳米流体Jeffery-Hamel流动的熵产及热性能分析
本文研究了三元杂化纳米流体在非平行板间的传热和磁流。考虑了洛伦兹力、纳米颗粒形状、热源、非线性太阳辐射和拉伸/收缩壁效应。考虑了一种含有杂化纳米粒子的聚合物基液(即F _ e3 _ O4−SWCNT−MWCNT${\mathrm{F}}{{\mathrm{e}}}_3}{{\mathrm{O}} _4}\, - \,{\mathrm{SWCNT}}\, - \,{\mathrm{MWCNT}}$纳米粒子)。利用相似变换,将数学建模得到的基本偏微分方程转化为常微分方程。然后,用数值和解析的方法得到了计算解。解析解是使用一种称为Adomian分解方法的高效计算技术构建的。为了保证验证,本文将特殊情况下的结果与RKF-45方法的结果进行了比较。物理因素对速度、温度和熵产生的影响用图形表示。此外,对多个变量对熵生成数的影响进行了论证和检验。研究发现,三元纳米流体的速度随哈特曼数的增加而增大,从而完全延缓了反转流动。研究结果还表明,基液中三元纳米颗粒的存在显著提高了收敛通道和发散通道的传热速率(努塞尔数)。此外,研究还发现,在收敛通道和发散通道中,热源都提高了三元混合纳米流体流动的温度,而散热器则表现出相反的行为,主要导致冷却效果。最后,热源/汇参数、辐射参数和磁场对努塞尔数的影响较大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advanced Theory and Simulations
Advanced Theory and Simulations Multidisciplinary-Multidisciplinary
CiteScore
5.50
自引率
3.00%
发文量
221
期刊介绍: Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including: materials, chemistry, condensed matter physics engineering, energy life science, biology, medicine atmospheric/environmental science, climate science planetary science, astronomy, cosmology method development, numerical methods, statistics
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