Artificial neural network modeling to examine spring turbulators influence on parabolic solar collector effectiveness with hybrid nanofluids

DOI10.1016/J.ENGANABOUND.2022.06.026OpenAlexW4285081926WikidataQ113875180 ScholiaQ113875180MaRDI QIDQ2085943

Anas Abdelrahman, S. Mohammad Sajadi, Hikmet Ş. Aybar, Nima Sina, Shi Fuxi, Mustafa Z. Mahmoud

Publication date: 19 October 2022

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.06.026

zbMATH Keywords

machine learninghybrid nanofluidfield synergy coefficientflat solar collectorperformance evaluation coefficientspring turbulator

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Fluid mechanics (76-XX)

Cites Work

• Unnamed Item
• A performance evaluation plot of enhanced heat transfer techniques oriented for energy-saving
• A novel concept for convective heat transfer enhancement
• Hydrothermal analysis of turbulent boehmite alumina nanofluid flow with different nanoparticle shapes in a minichannel heat exchanger using two-phase mixture model
• Heat transfer augmentation by coiled wire inserts during forced convection condensation of R-22 inside horizontal tubes
• Friction and heat transfer characteristic of laminar swirl flow through a circular tube fitted with regularly spaced twisted-tape elements

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