Ferrofluid bend channel flows for multi-parameter tunable heat transfer enhancement Part 2 Deep Learning and Neural Network Modeling

TL;DR

This paper employs deep learning and neural networks to model ferrofluid bend channel flows, aiming to predict heat transfer influenced by magnetic fields for improved thermal management in microscale systems.

Contribution

It introduces a machine learning-based modeling approach for ferrofluid flows in bend channels, building on CFD data to enhance heat transfer prediction accuracy.

Findings

Neural network models accurately predict convective heat transfer.

Machine learning models outperform traditional CFD in speed and efficiency.

Enhanced understanding of ferrofluid flow behavior under magnetic influence.

Abstract

This work is the second in a series focused on ferrofluid bend channel flows. Here, ferrofluid flows in bend channels are modeled using machine learning methods, based on data generated from the CFD simulation discussed in the first work in this series. Predicting convective heat transfer in ferrofluid flows influenced by magnetic fields is key to advancing thermal management in microscale and energy-intensive systems.