Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis
Trihybrid nanofluids exhibit superior heat transfer capabilities as compared to single or binary nanofluids. The synergistic interaction between the various types of nanoparticles results in an increased thermal conductivity as well as heat transfer rate. Trihybrid nanofluids are ideal for heat exch...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020723 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846099857990746112 |
|---|---|
| author | Hamad AlMohamadi Qammar Rubbab Hakim AL Garalleh Gulnaz Atta Muhammad Amjad Wasim Jamshed Fayza Abdel Aziz ElSeabee Mustafa Bayram |
| author_facet | Hamad AlMohamadi Qammar Rubbab Hakim AL Garalleh Gulnaz Atta Muhammad Amjad Wasim Jamshed Fayza Abdel Aziz ElSeabee Mustafa Bayram |
| author_sort | Hamad AlMohamadi |
| collection | DOAJ |
| description | Trihybrid nanofluids exhibit superior heat transfer capabilities as compared to single or binary nanofluids. The synergistic interaction between the various types of nanoparticles results in an increased thermal conductivity as well as heat transfer rate. Trihybrid nanofluids are ideal for heat exchange applications such as solar thermal systems, power plants, heat exchangers, and electronic cooling systems. Our concern in this paper is to incorporate the artificial intelligence (AI) and numerical simulation technique to assess the thermal attributes of trihybrid structured flow over a curved geometry. The nano-composition of gold (Au), single-walled carbon nanotubes (SWCNTs) and, aluminium oxide (Al2O3) make amalgamation in the base fluid H2O to prepare the trihybrid mixture. Two methods are employed to model and analyze the governing system. The first one is Quasi-linearization method (QLM), a numerical technique used to linearize and solve non-linear differential equations. The second method is Bayesian regression neural network (BRNN), a machine learning approach that integrates Bayesian statistics with neural networks to predict outcomes. The results are compared, under limiting conditions, with the earlier ones to validate the model. For the higher curvature parameter, higher will be the velocity and lower will be the temperature in the flow regime. The AI-driven numerical simulation for trihybrid structured flows over different geometries opens new avenues in the engineering applications involving aerodynamics, biomedical devices, and industrial processes. |
| format | Article |
| id | doaj-art-db543366963a42c89f0c6e60682ca7d0 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-db543366963a42c89f0c6e60682ca7d02024-12-31T04:13:15ZengElsevierResults in Engineering2590-12302025-03-0125103829Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysisHamad AlMohamadi0Qammar Rubbab1Hakim AL Garalleh2Gulnaz Atta3Muhammad Amjad4Wasim Jamshed5Fayza Abdel Aziz ElSeabee6Mustafa Bayram7Department of Chemical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah, Saudi Arabia; Sustainability Research Center, Islamic University of Madinah, Madinah, Saudi ArabiaDepartment of Mathematics, The Women University Multan, Pakistan; Corresponding author.Department of Mathematical Science, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi ArabiaUniversity of Education Lahore, DG Khan Campus, Dera Ghazi Khan, PakistanDepartment of Mathematics, COMSATS University Islamabad Vehari Campus, Islamabad 63100, PakistanDepartment of Mathematics, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan; Department of Computer Engineering, Biruni University, Topkapi, Istanbul, TurkeyDepartment of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Computer Engineering, Biruni University, Istanbul 34010, TurkeyTrihybrid nanofluids exhibit superior heat transfer capabilities as compared to single or binary nanofluids. The synergistic interaction between the various types of nanoparticles results in an increased thermal conductivity as well as heat transfer rate. Trihybrid nanofluids are ideal for heat exchange applications such as solar thermal systems, power plants, heat exchangers, and electronic cooling systems. Our concern in this paper is to incorporate the artificial intelligence (AI) and numerical simulation technique to assess the thermal attributes of trihybrid structured flow over a curved geometry. The nano-composition of gold (Au), single-walled carbon nanotubes (SWCNTs) and, aluminium oxide (Al2O3) make amalgamation in the base fluid H2O to prepare the trihybrid mixture. Two methods are employed to model and analyze the governing system. The first one is Quasi-linearization method (QLM), a numerical technique used to linearize and solve non-linear differential equations. The second method is Bayesian regression neural network (BRNN), a machine learning approach that integrates Bayesian statistics with neural networks to predict outcomes. The results are compared, under limiting conditions, with the earlier ones to validate the model. For the higher curvature parameter, higher will be the velocity and lower will be the temperature in the flow regime. The AI-driven numerical simulation for trihybrid structured flows over different geometries opens new avenues in the engineering applications involving aerodynamics, biomedical devices, and industrial processes.http://www.sciencedirect.com/science/article/pii/S2590123024020723Bayesian regression neural networkCurved geometryPartial differential equationsTrihybrid fluidQuasi-linearization methodNumerical results |
| spellingShingle | Hamad AlMohamadi Qammar Rubbab Hakim AL Garalleh Gulnaz Atta Muhammad Amjad Wasim Jamshed Fayza Abdel Aziz ElSeabee Mustafa Bayram Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis Results in Engineering Bayesian regression neural network Curved geometry Partial differential equations Trihybrid fluid Quasi-linearization method Numerical results |
| title | Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis |
| title_full | Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis |
| title_fullStr | Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis |
| title_full_unstemmed | Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis |
| title_short | Artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry: Thermalized case analysis |
| title_sort | artificial intelligence and numerical simulation based assessment of trihybrid structured flow over a curved geometry thermalized case analysis |
| topic | Bayesian regression neural network Curved geometry Partial differential equations Trihybrid fluid Quasi-linearization method Numerical results |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024020723 |
| work_keys_str_mv | AT hamadalmohamadi artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT qammarrubbab artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT hakimalgaralleh artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT gulnazatta artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT muhammadamjad artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT wasimjamshed artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT fayzaabdelazizelseabee artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis AT mustafabayram artificialintelligenceandnumericalsimulationbasedassessmentoftrihybridstructuredflowoveracurvedgeometrythermalizedcaseanalysis |