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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A polymer base fluid containing hybrid nanoparticles (i.e., <span data-altimg=\"/cms/asset/f577706d-fc67-4e03-960c-9c112ed22760/adts202401120-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/adts202401120-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-children=\"17,10,14\" data-semantic-content=\"8,12\" data-semantic- data-semantic-role=\"subtraction\" data-semantic-speech=\"normal upper F normal e 3 normal upper O 4 minus upper S upper W upper C upper N upper T minus upper M upper W upper C upper N upper T\" data-semantic-type=\"infixop\"><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"0,3,6\" data-semantic-content=\"15,16\" data-semantic- data-semantic-parent=\"18\" data-semantic-role=\"implicit\" data-semantic-type=\"infixop\"><mjx-mi 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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.

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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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