# Analysis of the EMHD nanofluid flow for geothermal pipelines using physics-driven deep learning

## TL;DR

This paper uses deep learning to model hybrid nanofluid flow in geothermal pipelines, showing how electric and magnetic fields affect temperature and velocity.

## Contribution

A novel unsupervised deep neural network is introduced to predict EMHD hybrid nanofluid dynamics in geothermal systems.

## Key findings

- The velocity profile is significantly influenced by the electric field and thermal Grashof number.
- The presence of nanoparticles reduces the overall thermal profile along the pipe’s length.
- Lowering the pressure affects both velocity profiles similarly.

## Abstract

In recent years data-driven machine learning techniques attract the attention of researchers in analyzing many complex systems. This study introduces a novel unsupervised deep neural network approach to predict the temperature and velocity behaviour of electro-magneto-hydrodynamics hybrid nanofluid flow for geothermal pipelines application.The exceptional flow and thermal characteristics of hybrid nanofluids making them ideal for use in geothermal energy extraction applications. The dynamics of hybrid nanofluid flow through a pipe are examined using a third-grade sodium alginate model, which has a lot of potential for geothermal applications. The copper oxide (CuO) and zinc oxide (ZnO) nanoparticles make up the nanofluid. It is also investigated how the flow dynamics are affected by electric and magnetic fields. The energy equation takes into account the effects of Joule heating and viscous dissipation as the fully developed incompressible fluid passes through the pipe. Consequently, an unsupervised deep neural network (DNN) method is used to predict the dynamics of nonlinear differential equations (DEs). The accuracy of the deep neural network ranges from \documentclass[12pt]{minimal}
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\begin{document}$$10^{-13}$$\end{document} across different cases. The velocity profile exhibits a clear symmetrical pattern and is found to be significantly influenced by both the electric field and the thermal Grashof number. The overall thermal profile along the pipe’s length decreases as a result of the nanoparticles. Additionally, lowering the pressure has a similar effect on both velocities. This study makes important contributions to the comprehension of the intricate dynamics of electro-magneto-hydrodynamics hybrid nanofluid flow, thereby laying a foundational framework for optimizing thermodynamic systems in geothermal. The findings of this research hold significant practical implications for the design and engineering of systems aimed at energy conservation and improved heat transfer efficiency in geothermal pipelines.