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- W2795077237 abstract "A robust neuromorphic model has been presented for simultaneous predictions of the Nusselt number and friction factor regarding nanofluid flow in heat exchanging devices. The most important influencing factors, namely, particle Peclet number, Reynolds number of the nanofluid, Prandlt number of the nanofluid, and volume fraction of the nanoparticles have been taken into consideration in the proposed models. The predicted results by the proposed model are much more accurate than the other available theoretical and semiempirical correlations. The presented computational technique is an accurate alternative for simultaneous prediction of Nusselt number and friction factor regarding the nanofluid-based heat exchanging equipments." @default.
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- W2795077237 date "2018-03-29" @default.
- W2795077237 modified "2023-10-14" @default.
- W2795077237 title "Robust neuromorphic model for simultaneous prediction of convection heat transfer coefficient and friction factor of nanofluid flow in heat exchanging equipment" @default.
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