Experimental and Machine Learning Study on the Influence of Nanoparticle Size and Pulsating Flow on Heat Transfer Performance in Nanofluid-Jet Impingement Cooling

Q: What problem does the study address for solar PV/T systems?

The study targets efficient heat dissipation from PV cell back plates, motivated by the large fraction of captured solar energy lost as heat and the need for higher heat transfer performance.

Q: How are pulsating flow and nanoparticle size investigated?

The work evaluates thermal dissipation under jet impingement cooling while varying pulsatile cooling conditions and nanoparticle size within hybrid Al2O3/MWCNT nanofluids across selected volume fractions and Reynolds numbers.

Q: Which factor is most significant for heat transfer prediction in the machine learning results?

Reynolds number is identified as the most significant factor influencing heat transfer, and Random Forest and Gradient Boosting classifiers show strong accuracy in predicting Nusselt number (Nu).

Aug 6, 2026

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Maximizing heat transfer efficiency is crucial for improving performance and durability in engineering applications such as fuel cells, EV batteries, and solar PV/T systems. This paper examines thermal dissipation from a simulated heat sink aligned with a PV cell back plate using jet impingement cooling. It evaluates pulsatile cooling and nanoparticle size effects in hybrid Al2O3/MWCNT nanofluids over multiple volume fractions and Reynolds numbers, reporting that nanoparticle size, concentration, and pulsating flow strongly govern heat transfer. Machine learning models identify Reynolds number as the most influential factor for predicting Nu.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Research Scope and Background

Solar PV heat loss and net-zero context

Nanofluid jet impingement and heat sink cooling

Prior jet impingement studies

Nomenclature and Symbols

Temperature, geometry, and flow parameters

Dimensionless numbers and key variables

Abstract and Key Findings

Heat transfer enhancement results

ML-based identification of dominant parameters

Applied Thermal Engineering 258 (2025) 124631 Contents lists available at ScienceDirect Applied Thermal Engineering journal [homepage:](homepage: www.elsevier.com/locate/apthermeng)[ www.elsevier.com/locate/apthermeng](homepage: www.elsevier.com/locate/apthermeng) | Research Paper
Experimental and machine learning study on the influence of nanoparticlesize and pulsating flow on heat transfer performance in nanofluid-jet impingement cooling
Emmanuel O. Atofaratia,*, Mohsen Sharifpur b,c,d,*, Zhongjie Huana, Olushina Olawale Awee, Josua P. Meyer f
a Department of Mechanical and Mechatronics Engineering, Tshwane University of Technology, Pretoria, Private Bag X 680, Pretoria 0001, South Africa
b Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, Private Bag X20, Hatfield 0028, South Africa c School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Private Bag 3, Wits 2050, South Africa d Medical Research Department, China Medical University Hospital, China Medical University, Taichung, Taiwan
e Statistical Learning Lab, Federal University of Bahia, El Salvador
f Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Stellenbosch, South Africa | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
PV cell
Pulsating jet impingement Heat transfer
Hybrid nanofluids Particle size | | Maximizing heat transfer efficiency is crucial for enhancing performance and durability in diverse engineering applications, including fuel cells, EV batteries, and solar PV/T systems, thereby advancing sustainable energy innovation. This study investigates thermal dissipation from a simulated heat sink aligned with a PV cell’s back plate via jet impingement cooling. Specifically, it examines the impacts of pulsatile cooling and nanoparticle size in hybrid nanofluids, comprising combinations of Al2O3 and MWCNT in water, with varied nanofluid volume fraction (0.05 vol% ≤ ɸ ≤ 0.3 vol%) and flow Reynolds number (15000 < Re < 40000). Key findings reveal significant influences of nanoparticle size, nanofluid concentration, and pulsating flow on heat transfer performance. Notably, sample D demonstrated the highest heat transfer enhancement, achieving approximately 52.94 % and 79.06 % improvement in continuous and pulsating jet cooling compared to de-ionized water under continuous jet cooling. Machine learning classifiers were employed to identify critical thermal performance parameters, with Reynolds number identified as the most significant factor influencing heat transfer. Random Forest and Gradient Boosting classifiers showed notable accuracy in predicting Nu, emphasizing the role of machine learning techniques in optimizing thermal management strategies for improved heat dissipation from solar PV cell backplates. | 1. Introduction In pursuing the ambitious 2050 Net Zero Emission goal, the global focus has gravitated towards harnessing solar power for both electrical and thermal energy generation. Solar Photovoltaic (PV) cells are pivotal in this transformative journey. Despite their significance, research indicates that more than 70 % of the energy captured by solar cells is lost as heat [1]. This underscores the urgency for an efficient method to dissipate and utilize this thermal energy for various domestic and commercial applications. Nanofluid jet impingement is renowned for its prowess in eradicating hotspot surfaces through the impressive combination of exceptional heat and mass transfer rates of jet cooling coupled with improved nanofluid thermal conductivity. Several studies have delved into experimental and numerical exploration aimed at harnessing the full potential of solar energy, optimizing heat dissipation, and propelling us closer to a sustainable and emission-free future. Some of these studies are targeted towards utilization of heat-exchanging serpentine pipe [2], pin–f

FAQs

What problem does the study address for solar PV/T systems?

The study targets efficient heat dissipation from PV cell back plates, motivated by the large fraction of captured solar energy lost as heat and the need for higher heat transfer performance.

How are pulsating flow and nanoparticle size investigated?

The work evaluates thermal dissipation under jet impingement cooling while varying pulsatile cooling conditions and nanoparticle size within hybrid Al2O3/MWCNT nanofluids across selected volume fractions and Reynolds numbers.

Which factor is most significant for heat transfer prediction in the machine learning results?

Reynolds number is identified as the most significant factor influencing heat transfer, and Random Forest and Gradient Boosting classifiers show strong accuracy in predicting Nusselt number (Nu).

Aug 6, 2026

Upload by Aurora