Enhanced heat transport in magneto-nanofluidic thermal systems: adiabatic block effects in grooved channels and ANN modeling

  • This study investigates heat transfer enhancement in magneto-nanofluidic systems through the strategic placement of adiabatic blocks in grooved channels. Using CuO–H2O nanofluid in a bottom-heated channel with circular expansion, we examine the complex interactions between forced convection, magnetic fields, and uoyancy effects. Through systematic numerical analysis, we explore the combined influences of Rayleigh, Reynolds, and Hartmann numbers on thermal performance. Our findings reveal significant heat transfer enhancement (up to 137 %) under optimal conditions, particularly with vertical magnetic field orientation at Re = 100 and Ha = 30. The results demonstrate how adiabatic blocks modify flow structures, with larger blocks diminishing vortex intensity while elevated Ra generates secondary vortices that interact with primary circulations. Magnetic field effects show notable dependence on orientation, with vertical fields generally promoting better heat transfer than horizontalThis study investigates heat transfer enhancement in magneto-nanofluidic systems through the strategic placement of adiabatic blocks in grooved channels. Using CuO–H2O nanofluid in a bottom-heated channel with circular expansion, we examine the complex interactions between forced convection, magnetic fields, and uoyancy effects. Through systematic numerical analysis, we explore the combined influences of Rayleigh, Reynolds, and Hartmann numbers on thermal performance. Our findings reveal significant heat transfer enhancement (up to 137 %) under optimal conditions, particularly with vertical magnetic field orientation at Re = 100 and Ha = 30. The results demonstrate how adiabatic blocks modify flow structures, with larger blocks diminishing vortex intensity while elevated Ra generates secondary vortices that interact with primary circulations. Magnetic field effects show notable dependence on orientation, with vertical fields generally promoting better heat transfer than horizontal configurations. To complement the numerical analysis, we develop a predictive model using Artificial Neural Network (ANN) for Nusselt numbers across various operating conditions, achieving over 99 % accuracy. The integrated computational-ANN approach offers significant advancements in optimizing thermal systems in various areas, ranging from electronics cooling to microfluidic devices.show moreshow less

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Author:Dipak Kumar MandalORCiD, Nirmal K. MannaORCiD, Nirmalendu BiswasORCiD, Tansu Rudra, Rajesh Kumar, Ali Cemal BenimORCiD
Qualitätssicherung:peer reviewed
open access:Gold - Erstveröffentlichung mit Lizenzhinweis
agreement:DEAL Elsevier
Research fields:Materialien / Materialien - Allgemein
Technologie / Technologie - Allgemein
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Maschinenbau und Verfahrenstechnik
Document Type:Article
Year of Completion:2026
Language of Publication:English
Publisher:Elsevier
Parent Title (English):International Journal of Thermofluids
Volume:31
Article Number:101515
URN:urn:nbn:de:hbz:due62-opus-60127
DOI:https://doi.org/10.1016/j.ijft.2025.101515
ISSN:2666-2027
Tag:HSD Publikationsfonds
Adiabatic block; Grooved channel; Heat transfer augmentation; Mixed convective flow; Nanofluids; Neural network
Corresponding Author:Ali Cemal Benim
Information on the Research Data:Data will be made available on request.
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2026/01/26
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