Ferrofluid bend channel flows for multi-parameter tunable heat transfer enhancement Part 2 Deep Learning and Neural Network Modeling
Applied Physics
2026-02-23 v1 Chemical Physics
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.
Cite
@article{arxiv.2602.17704,
title = {Ferrofluid bend channel flows for multi-parameter tunable heat transfer enhancement Part 2 Deep Learning and Neural Network Modeling},
author = {Nadish Anand and Prashant Shukla and Warren Jasper},
journal= {arXiv preprint arXiv:2602.17704},
year = {2026}
}