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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid.

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Title: An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid.
Authors: Hussain, Mohib1,2 (AUTHOR), Lin, Du1,2 (AUTHOR), Waqas, Hassan3 (AUTHOR), Al-Mdallal, Qasem M.4 (AUTHOR) q.almdallal@uaeu.ac.ae
Source: Engineering Applications of Computational Fluid Mechanics. Dec2025, Vol. 19 Issue 1, p1-22. 22p.
Subjects: Artificial intelligence, Machine learning, Computational fluid dynamics, Nanofluids, Biological systems, Heat transfer, Artificial neural networks, Thermal conductivity
Abstract: Optimising heat transfer in biomedical systems, especially in blood-mediated liquids, is essential for precise medication administration and thermal ablation treatments. However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. In this work, we propose a computational fluid dynamics (CFD), machine learning (ML), and an artificial intelligence (AI) based computational framework for hemodynamics simulation of couple-stressed hybrid nano-integrated blood flow through parallel plates under external squeezing. The aim of this study is to enhance the thermal conductivity of blood-integrated tri-hybrid nanofluids, thus increasing the transfer of heat and maintaining temperature in biomedical systems. An AI-integrated, the Levenberg-Marquardt algorithm is employed with a neural network back propagation approach (ANN-LMA) for comprehensive analysis of viscous dissipation and the Lorentz force effects influenced tri-hybrid nano-fluid mixture. Non-linear, coupled partial differential equations are transformed into ordinary differential equations with similarity scaling to characterize heat transfer and fluid flow, which are then numerically solved using the modified finite difference method (the Keller-Box method). The heat transfer ability of ternary nano-fluid is enhanced with an increase in the couple stress parameter while, a rising Hartmann number results in more thermal diffusion. Regression scores equal to 1 indicate a good match between the actual data and the predictions. Conclusively, the proposed investigation provides insightful AI, ML and CFD-proposed analysis of blood-based nano-particles which can improve imaging techniques, provide tailored drug delivery, reduce hyperthermia, improve blood flow, and show potential for application in medicine. Highlights Artificial intelligence and machine learning-based CFD simulation of the blood-mediated tri-hybrid nano-fluid flow is presented. An improved finite difference scheme (the Keller-Box method), is utilized to numerically evaluate the problem. The LMA-ANN forecasts with an absolute error range of $ 10^{-11} $ 10 − 11 to $ 10^{-8} $ 10 − 8 relative to the actual data. Regression scores equal to 1 indicate a strong correlation between forecasts and actual data. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Applications of Computational Fluid Mechanics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 189915349
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  Data: An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid.
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  Data: Optimising heat transfer in biomedical systems, especially in blood-mediated liquids, is essential for precise medication administration and thermal ablation treatments. However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. In this work, we propose a computational fluid dynamics (CFD), machine learning (ML), and an artificial intelligence (AI) based computational framework for hemodynamics simulation of couple-stressed hybrid nano-integrated blood flow through parallel plates under external squeezing. The aim of this study is to enhance the thermal conductivity of blood-integrated tri-hybrid nanofluids, thus increasing the transfer of heat and maintaining temperature in biomedical systems. An AI-integrated, the Levenberg-Marquardt algorithm is employed with a neural network back propagation approach (ANN-LMA) for comprehensive analysis of viscous dissipation and the Lorentz force effects influenced tri-hybrid nano-fluid mixture. Non-linear, coupled partial differential equations are transformed into ordinary differential equations with similarity scaling to characterize heat transfer and fluid flow, which are then numerically solved using the modified finite difference method (the Keller-Box method). The heat transfer ability of ternary nano-fluid is enhanced with an increase in the couple stress parameter while, a rising Hartmann number results in more thermal diffusion. Regression scores equal to 1 indicate a good match between the actual data and the predictions. Conclusively, the proposed investigation provides insightful AI, ML and CFD-proposed analysis of blood-based nano-particles which can improve imaging techniques, provide tailored drug delivery, reduce hyperthermia, improve blood flow, and show potential for application in medicine. Highlights Artificial intelligence and machine learning-based CFD simulation of the blood-mediated tri-hybrid nano-fluid flow is presented. An improved finite difference scheme (the Keller-Box method), is utilized to numerically evaluate the problem. The LMA-ANN forecasts with an absolute error range of $ 10^{-11} $ 10 − 11 to $ 10^{-8} $ 10 − 8 relative to the actual data. Regression scores equal to 1 indicate a strong correlation between forecasts and actual data. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Applications of Computational Fluid Mechanics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/19942060.2025.2459664
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      – Code: eng
        Text: English
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        PageCount: 22
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Computational fluid dynamics
        Type: general
      – SubjectFull: Nanofluids
        Type: general
      – SubjectFull: Biological systems
        Type: general
      – SubjectFull: Heat transfer
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Thermal conductivity
        Type: general
    Titles:
      – TitleFull: An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid.
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            NameFull: Hussain, Mohib
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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