Robust weighted least squares support vector regression algorithm to estimate the nanofluid thermal properties of water/graphene oxide-silicon carbide mixture

DOI10.1016/J.PHYSA.2019.03.086OpenAlexW2929852282WikidataQ128145640 ScholiaQ128145640MaRDI QIDQ2159691

Truong Khang Nguyen, Masoud Afrand, Boshra Mahmoudi, Seyed Amin Bagherzadeh, Amin Shahsavar, Ahmad Hajizadeh

Publication date: 2 August 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2019.03.086

zbMATH Keywords

experimental resultsnanofluidrobust weighted least squares support vector regression

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• Early sample measures of variability.
• Fluid flow and heat transfer of non-Newtonian nanofluid in a microtube considering slip velocity and temperature jump boundary conditions
• Multilayer feedforward networks are universal approximators
• Comparison of the finite volume and lattice Boltzmann methods for solving natural convection heat transfer problems inside cavities and enclosures
• Weighted least squares support vector machines: robustness and sparse approximation
• Unnamed Item

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