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Machine learning examination based on Bayesian regularized algorithm for slip effects on solarized Boger nanofluid

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This study models magnetohydrodynamic solarized Boger nanofluid heat transfer using AI. Bayesian regularization optimizes a neural network, showing chemical reaction effects on concentration profiles for thermal management applications.

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Area of Science:

  • Thermodynamics and Fluid Mechanics
  • Materials Science
  • Artificial Intelligence

Background:

  • Precise heat and mass transfer control is crucial for thermal management and complex energy systems.
  • Nanofluids offer enhanced heat absorption and transfer capabilities for solar thermal collectors, photovoltaic cooling, and energy storage.
  • Micro/nano-scale devices and non-Newtonian fluid dynamics present complex modeling challenges.

Purpose of the Study:

  • To investigate the heat generation influence on magnetohydrodynamic (MHD) solarized Boger nanofluid.
  • To analyze the effects of slip velocity and activation energy on fluid flow and heat transfer.
  • To develop a predictive model for complex nonlinear behaviors in such systems.

Main Methods:

  • Utilized an artificial intelligence-based neural network framework for modeling.
  • Optimized the neural network using the Intelligent Bayesian Regularization technique.
  • Trained and tested the neural network on a generated dataset (80% training, 20% testing).

Main Results:

  • The model accurately predicts nonlinear flow and heat transfer behaviors.
  • Slip effects, thermophoresis, and Brownian motion are incorporated for micro/nano-scale applicability.
  • Increasing chemical reaction parameter values lead to a decrease in the concentration profile.

Conclusions:

  • The AI-optimized model provides accurate and dependable predictions for MHD solarized Boger nanofluid.
  • The findings are applicable to diverse fields including thermal management, MEMS, and industrial process optimization.
  • The study highlights the effectiveness of Bayesian regularization in machine learning for complex fluid dynamics.