Application of machine learning for thermal exchange of dissipative ternary nanofluid over a stretchable wavy cylinder with thermal slip
This article explores the enhancement of thermal exchange in a dissipative Triple-nanoparticle (Al2O3+CuO+Cu) hybrid fluid over a stretchable wavy cylindrical surface with slip effect, incorporating Python bvp algorithm with artificial intelligence AI analysis of numerical results. The stochastic AI...
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| Veröffentlicht in: | Case studies in thermal engineering Jg. 60; S. 104599 |
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01.08.2024
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| Abstract | This article explores the enhancement of thermal exchange in a dissipative Triple-nanoparticle (Al2O3+CuO+Cu) hybrid fluid over a stretchable wavy cylindrical surface with slip effect, incorporating Python bvp algorithm with artificial intelligence AI analysis of numerical results. The stochastic AI analysis gives the enhanced and optimized results with predictive modeling, incorporating randomness of influencing parameters and nonlinear turbulent behavior of model. The model has significant importance and application in noise reducing and drag reduction devices or structures. Moreover, the presented geometrical structure is useful in enhancing thermal conduction characteristic. The intricate interplay of constituent nanoparticles and their effect on complex heat transfer in drag optimization devices is the main focus of this study. Mathematical Model of PDEs of this flow problem is converted into system of ODEs by similarity transformations with introducing dimensionless parameters. Numerical solutions of the emerged system are obtained by Python bvp solver algorithm and graphical solutions by Python are presented. To expedite the solution process and enhance the accuracy of prediction, advanced AI algorithm, such as neural network and machine learning technique is adopted. Numerical dataset obtained from Python is embedded for further AI analysis by using Levenberg Marquardt Feed-forward Algorithm (LMFA) with 10 computing neurons and 4 output layers representing results for 4 parametric variations.
A rise in the fluid flow speed is observed with higher of value yield stress or Newtonian-behavior i.e. of Casson parameter a1 and stretching parameter λ for the sheet, but shows a decline with enhancing turbulence s2. Temperature profile show a descending behavior with inclination of Eckert ratio Ec, and Prandtl ratio of momentum-thermal diffusivity. |
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| AbstractList | This article explores the enhancement of thermal exchange in a dissipative Triple-nanoparticle (Al2O3+CuO+Cu) hybrid fluid over a stretchable wavy cylindrical surface with slip effect, incorporating Python bvp algorithm with artificial intelligence AI analysis of numerical results. The stochastic AI analysis gives the enhanced and optimized results with predictive modeling, incorporating randomness of influencing parameters and nonlinear turbulent behavior of model. The model has significant importance and application in noise reducing and drag reduction devices or structures. Moreover, the presented geometrical structure is useful in enhancing thermal conduction characteristic. The intricate interplay of constituent nanoparticles and their effect on complex heat transfer in drag optimization devices is the main focus of this study. Mathematical Model of PDEs of this flow problem is converted into system of ODEs by similarity transformations with introducing dimensionless parameters. Numerical solutions of the emerged system are obtained by Python bvp solver algorithm and graphical solutions by Python are presented. To expedite the solution process and enhance the accuracy of prediction, advanced AI algorithm, such as neural network and machine learning technique is adopted. Numerical dataset obtained from Python is embedded for further AI analysis by using Levenberg Marquardt Feed-forward Algorithm (LMFA) with 10 computing neurons and 4 output layers representing results for 4 parametric variations.
A rise in the fluid flow speed is observed with higher of value yield stress or Newtonian-behavior i.e. of Casson parameter a1 and stretching parameter λ for the sheet, but shows a decline with enhancing turbulence s2. Temperature profile show a descending behavior with inclination of Eckert ratio Ec, and Prandtl ratio of momentum-thermal diffusivity. This article explores the enhancement of thermal exchange in a dissipative Triple-nanoparticle (Al2O3+CuO+Cu) hybrid fluid over a stretchable wavy cylindrical surface with slip effect, incorporating Python bvp algorithm with artificial intelligence AI analysis of numerical results. The stochastic AI analysis gives the enhanced and optimized results with predictive modeling, incorporating randomness of influencing parameters and nonlinear turbulent behavior of model. The model has significant importance and application in noise reducing and drag reduction devices or structures. Moreover, the presented geometrical structure is useful in enhancing thermal conduction characteristic. The intricate interplay of constituent nanoparticles and their effect on complex heat transfer in drag optimization devices is the main focus of this study. Mathematical Model of PDEs of this flow problem is converted into system of ODEs by similarity transformations with introducing dimensionless parameters. Numerical solutions of the emerged system are obtained by Python bvp solver algorithm and graphical solutions by Python are presented. To expedite the solution process and enhance the accuracy of prediction, advanced AI algorithm, such as neural network and machine learning technique is adopted. Numerical dataset obtained from Python is embedded for further AI analysis by using Levenberg Marquardt Feed-forward Algorithm (LMFA) with 10 computing neurons and 4 output layers representing results for 4 parametric variations.A rise in the fluid flow speed is observed with higher of value yield stress or Newtonian-behavior i.e. of Casson parameter a1 and stretching parameter λ for the sheet, but shows a decline with enhancing turbulence s2. Temperature profile show a descending behavior with inclination of Eckert ratio Ec, and Prandtl ratio of momentum-thermal diffusivity. |
| ArticleNumber | 104599 |
| Author | Qureshi, Hamid Alqahtani, Talal Irshad, Kashif Algarni, Salem Shah, Zahoor Pasha, Amjad Ali Zahoor Raja, Muhammad Asif Khan, Waqar Azeem |
| Author_xml | – sequence: 1 givenname: Hamid surname: Qureshi fullname: Qureshi, Hamid organization: Department of Mathematics, Mohi-Ud-Din Islamic University, Nerian Sharif, AJK, Pakistan – sequence: 2 givenname: Amjad Ali surname: Pasha fullname: Pasha, Amjad Ali organization: Aerospace Engineering Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia – sequence: 3 givenname: Zahoor surname: Shah fullname: Shah, Zahoor organization: Department of Mathematics, COMSATS University Islamabad, Islamabad Campus, Islamabad, 43600, Pakistan – sequence: 4 givenname: Muhammad Asif surname: Zahoor Raja fullname: Zahoor Raja, Muhammad Asif email: rajamaz@yuntech.edu.tw organization: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan – sequence: 5 givenname: Salem surname: Algarni fullname: Algarni, Salem organization: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, 9004, Saudi Arabia – sequence: 6 givenname: Talal surname: Alqahtani fullname: Alqahtani, Talal organization: Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, 9004, Saudi Arabia – sequence: 7 givenname: Kashif surname: Irshad fullname: Irshad, Kashif organization: Interdisciplinary Research Centre for Sustainable Energy Systems (IRC-SES), Research Institute, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia – sequence: 8 givenname: Waqar Azeem surname: Khan fullname: Khan, Waqar Azeem email: waqarazeem@bit.edu.cn, Waqar_qau85@yahoo.com organization: School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, 100081, China |
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| Keywords | Levenberg-marquardt algorithm Neural network. tri-nano fluid flow in radiated channels (THFRC) Velocity slip Artificial intelligence Nanofluids |
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