Machine Learning Investigation of Ternary-Hybrid Radiative Nanofluid over Stretching and Porous Sheet

Area of Science:

• Nanotechnology
• Fluid Dynamics
• Heat Transfer

Background:

• Ternary hybrid nanofluids offer diverse applications, including biomedical engineering, energy, and automotive sectors.
• Nanofluids, like MWCNT-Au/Ag dispersed in blood, show significant potential in practical engineering problems.
• Understanding fluid flow characteristics is crucial for optimizing nanofluid applications.

Purpose of the Study:

• To investigate the flow behavior of a ternary hybrid nanofluid (MWCNT-Au/Ag in blood) over a bidirectional stretching sheet.
• To analyze the impact of various physical parameters on flow characteristics and boundary layer phenomena.
• To examine horizontal and vertical velocity profiles and temperature distribution.

Main Methods:

• Developing a mathematical model for the three-phase nanofluid flow.
• Transforming coupled nonlinear partial differential equations (PDEs) into dimensionless ordinary differential equations (ODEs).
• Utilizing an AI-based technique (Levenberg-Marquardt Feedforward Algorithm) with data generated in MATHEMATICA to solve the ODEs.

Main Results:

• The stretching ratio significantly influences vertical velocity, with increased stretching leading to higher vertical velocity.
• Analysis of horizontal and vertical velocity profiles reveals boundary layer behavior under different physical parameters.
• Temperature distribution was analyzed, showing its dependence on various flow conditions.

Conclusions:

• The study provides insights into the complex flow dynamics of ternary hybrid nanofluids.
• The findings suggest that optimizing stretching parameters can enhance fluid flow efficiency.
• This research contributes to the understanding of nanofluid behavior for potential applications in heat exchange and other fields.