Synthesizes, characterization, measurements and modeling thermal conductivity and viscosity of graphene quantum dots nanofluids
F Sedaghat, F Yousefi - Journal of Molecular Liquids, 2019 - Elsevier
The present study is aimed to measure the thermophysical properties of graphene quantum
dots (GQDs) nanoparticles that dispersed in water, ethylene glycol and water-ethylene …
dots (GQDs) nanoparticles that dispersed in water, ethylene glycol and water-ethylene …
Ridge regression and artificial neural network to predict the thermodynamic properties of alkali metal Rankine cycles for space nuclear power
Q Sun, HC Zhang, Z Sun, Y Xia - Energy Conversion and Management, 2022 - Elsevier
The alkali metal Rankine cycle system has great advantages in space nuclear power
system. The investigation of thermodynamic properties of alkali metals is fundamental to …
system. The investigation of thermodynamic properties of alkali metals is fundamental to …
Automated flowsheet synthesis using hierarchical reinforcement learning: proof of concept
Recently we showed that reinforcement learning can be used to automatically generate
process flowsheets without heuristics or prior knowledge. For this purpose, SynGameZero, a …
process flowsheets without heuristics or prior knowledge. For this purpose, SynGameZero, a …
Thermal conductivity and structuring of multiwalled carbon nanotubes based nanofluids
M Moghaddari, F Yousefi, S Aparicio… - Journal of Molecular …, 2020 - Elsevier
Abstract the thermal conductivity of OH functionalized multiwalled carbon nanotube and its
composites with Ag, Au and Pd in water, ethylene glycol and ethylene glycol-water (60: 40 …
composites with Ag, Au and Pd in water, ethylene glycol and ethylene glycol-water (60: 40 …
[HTML][HTML] A simple approach for the sonochemical loading of Au, Ag and Pd nanoparticle on functionalized MWCNT and subsequent dispersion studies for removal of …
M Moghaddari, F Yousefi, M Ghaedi… - Ultrasonics sonochemistry, 2018 - Elsevier
In this study, the artificial neural network (ANN) and response surface methodology (RSM)
based on central composite design (CCD) were applied for modeling and optimization of the …
based on central composite design (CCD) were applied for modeling and optimization of the …
[HTML][HTML] High temperature Cesium-sCO2 combined cycle for concentrating solar power applications
V Naumov, M Doninelli, G Di Marcoberardino, P Iora… - Solar Energy, 2024 - Elsevier
The temperature potential of concentrating solar power plants is not fully utilized using
existing steam cycles. The increase of turbine inlet temperature is limited by the materials …
existing steam cycles. The increase of turbine inlet temperature is limited by the materials …
Estimation of vaporization properties of pure substances using artificial neural networks
GY Ottaiano, INS da Cruz, HS da Cruz… - Chemical Engineering …, 2021 - Elsevier
Vaporization properties are important for equipment modeling and process control involving
liquid-vapor equilibrium. The aim of this work was to obtain an Artificial Neural Network …
liquid-vapor equilibrium. The aim of this work was to obtain an Artificial Neural Network …
A new model to predict the densities of nanofluids using statistical mechanics and artificial intelligent plus principal component analysis
F Yousefi, Z Amoozandeh - Chinese journal of chemical engineering, 2017 - Elsevier
In this work, some thermodynamic properties of nanofluids such as Sb 2 O 5; SnO 2/(EG+ H
2 O), ZnO/(EG+ H 2 O), Al 2 O 3/(EG+ H 2 O), ZnO/(PEG+ H 2 O), ZnO/PEG, and TiO 2/EG …
2 O), ZnO/(EG+ H 2 O), Al 2 O 3/(EG+ H 2 O), ZnO/(PEG+ H 2 O), ZnO/PEG, and TiO 2/EG …
Estimation of PC-SAFT binary interaction coefficient by artificial neural network for multicomponent phase equilibrium calculations
F Abbasi, Z Abbasi, RB Boozarjomehry - Fluid Phase Equilibria, 2020 - Elsevier
Abstract Perturbed-Chain Statistical Associating Fluid Theory Equation of State (PC-SAFT
EoS) requires cross interaction parameter for each binary pair in the mixture. For real …
EoS) requires cross interaction parameter for each binary pair in the mixture. For real …
Application of artificial neural network and PCA to predict the thermal conductivities of nanofluids
F Yousefi, S Mohammadiyan, H Karimi - Heat and Mass Transfer, 2016 - Springer
This paper applies a model including back-propagation network (BPN) and principal
component analysis (PCA) to compute the effective thermal conductivities of nanofluids such …
component analysis (PCA) to compute the effective thermal conductivities of nanofluids such …