Performance and stability evaluation of nanoadditives for engine oil applications

Kumar, Ajay; Gupta, Tcsm; Shukla, Atindra D.

doi:10.1007/s40430-022-03986-x

Cited by 5 publications

(1 citation statement)

References 48 publications

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“…Nano uids, suspensions containing small percentages (< 5% vol.) of various nanoparticles in conventional working uids, have emerged as an excellent solution to address thermal challenges in advanced heat transfer systems [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Modeling Convective Heat Transfer Augmentation of TiO 2 Nanofluids Using Neural Networks

Pouramini,

Fathabadi,

Shahryari

2024

Preprint

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In this study, a neural network methodology was employed to estimate the forced convective heat transfer coefficient of nanofluids. Various operational parameters, including heat flux, thermal conductivity of fluids, nanoparticle concentration, and flow Reynolds number, were investigated to quantify the convective heat transfer coefficient. These operational parameters were introduced as inputs into an artificial neural network (ANN) to model the convective heat transfer coefficient. The addition of nanoparticles to the base fluid enhanced the forced convective heat transfer coefficient, with more significant effects observed in base fluids with lower thermal conductivity and flows characterized by higher Reynolds numbers and elevated heat fluxes. Good agreement between experimental data and the predicted results of the ANN demonstrates that the ANN can accurately model this process, except for higher heat flux.

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Section: Introductionmentioning

confidence: 99%