Xue model exploration for oxytactic microbes in radiative MHD hybrid nanoliquid using machine learning technique

Area of Science:

• Fluid Dynamics
• Heat Transfer
• Biomedical Engineering
• Nanotechnology

Background:

• Microorganism-nanofluid interactions are crucial in biomedical engineering, environmental research, and thermal systems.
• Understanding bioconvection flow dynamics is essential for designing microfluidic devices, bioreactors, and renewable energy systems.
• Hybrid nanofluids offer enhanced heat and mass transport properties.

Purpose of the Study:

• To analyze and validate a proposed model for bioconvection flow of hybrid nanofluid incorporating the Xue model.
• To investigate the impact of temperature and solutal gradients on oxytactic microbes.
• To assess the model's applicability in nanomedicine and renewable energy systems.

Main Methods:

• Utilized the back-propagation Levenberg-Marquardt (BLM) methodology for model training and validation.
• Employed mean square error (MSE), error histograms (EH), regression plots, and suggested solutions for verification.
• Incorporated the Xue model to describe microorganism-nanofluid interactions.

Main Results:

• The BLM methodology proved effective in training and validating the bioconvection model.
• The model accurately predicted the influence of thermal radiation and Soret-Dufour effects on mass and heat transport.
• Efficiency and precision of the methodology were confirmed through statistical validation metrics.

Conclusions:

• The validated model provides a robust framework for predicting bioconvection phenomena in hybrid nanofluids.
• The study highlights the model's utility in optimizing nanomedicine applications, such as targeted drug delivery.
• The research demonstrates the model's potential for advancing renewable energy technologies and bio-inspired cooling systems.