A comprehensive computational approach for nonlinear thermal instability of the electrically FG-GPLRC disk based on GDQ method

MSH Al-Furjan, H Safarpour, M Habibi… - Engineering with …, 2022 - Springer
This is a fundamental study on the buckling temperature and post-buckling analysis of
functionally graded graphene nanoplatelet-reinforced composite (FG-GPLRC) disk covered …

Estimation of pressure drop of two-phase flow in horizontal long pipes using artificial neural networks

MS Shadloo, A Rahmat… - Journal of …, 2020 - asmedigitalcollection.asme.org
Gas–liquid two-phase flows through long pipelines are one of the most common cases
found in chemical, oil, and gas industries. In contrast to the gas/Newtonian liquid systems …

Prediction of viscosity of biodiesel blends using various artificial model and comparison with empirical correlations

Y Zheng, MS Shadloo, H Nasiri, A Maleki… - Renewable Energy, 2020 - Elsevier
From the perspective of renewability and environmental pollution, biodiesels are appropriate
alternatives to conventional diesel fuels due to their proper combustion behavior and …

Bi-directional thermal buckling and resonance frequency characteristics of a GNP-reinforced composite nanostructure

J Li, F Tang, M Habibi - Engineering with Computers, 2022 - Springer
In this article, thermal buckling and resonance frequency of a composite cylindrical
nanoshell reinforced with graphene nanoplatelets (GNP) under bi-directional thermal …

A review of artificial neural network techniques for environmental issues prediction

K Han, Y Wang - Journal of Thermal Analysis and Calorimetry, 2021 - Springer
The smarter world needs more efforts to purposeful manage and usage of technologies,
science, artificial intelligence, and artificial neural networks, as their product. One of the main …

Hydrogen solubility in aromatic/cyclic compounds: Prediction by different machine learning techniques

Y Jiang, G Zhang, J Wang, B Vaferi - International Journal of Hydrogen …, 2021 - Elsevier
A systematic procedure based on adaptive neuro-fuzzy inference systems (ANFIS), artificial
neural networks, and least-squares support vector machines develop to estimate hydrogen …

A feed-forward back propagation neural network approach to predict the life condition of crude oil pipeline

NB Shaik, SR Pedapati, SAA Taqvi, AR Othman… - Processes, 2020 - mdpi.com
Pipelines are like a lifeline that is vital to a nation's economic sustainability; as such,
pipelines need to be monitored to optimize their performance as well as reduce the product …

Forecasting the thermal conductivity of a nanofluid using artificial neural networks

S Rostami, R Kalbasi, N Sina, AS Goldanlou - Journal of Thermal Analysis …, 2021 - Springer
In this study, the influence of incorporating MWCNT on the thermal conductivity of paraffin
was evaluated numerically. Input variables including mass fraction (0.005–5%) and …

Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques

Y Cao, HA Dhahad, SG Zare, N Farouk, AE Anqi… - International Journal of …, 2021 - Elsevier
Metal-organic frameworks are a new class of materials for hydrogen adsorption/storage
applications. The hydrogen storage capacity of this structure is typically related to pressure …

[HTML][HTML] Combined septum and chamfer fins on threated stretching surface under the influence of nanofluid and the magnetic parameters for rotary seals in computer …

P Shadman, Z Parhizi, R Fathollahi, M Zarinfar… - Alexandria Engineering …, 2023 - Elsevier
In this paper, variation of temperature and velocity in the x-direction and the angular velocity
of the nanofluids flow through triangular and rectangular and chamfer fins are investigated in …