A new pressure and temperature dependent relative viscosity model for water-Al2O3 nanofluids using GMDH neural network. (19th April 2022)

• Record Type: Journal Article
• Authors: Shirbazo, Armin; Keshavarz Moraveji, Mostafa
• Is Part Of: Petroleum science and technology. Volume 40:Number 14(2022)
• Page Start: 1735
• Page End: 1755
• Publication Date: 2022-04-19
• Subjects: GMDH -- nanoparticles -- pressure -- temperature -- viscosity -- water-Al2O3 system; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2022.2030354
• Languages: English
• ISSNs: 1091-6466

Abstract

Abstract: The introduction of nanoparticles into fluids have been used in various sciences such as; heat transfer, drilling mud, hydraulic fracturing, EOR, and widely in biotechnology science. The dispersion of nanoparticles in the fluid can affect significantly the viscosity of the system. In this study, a self-organizing polynomial neural network based on a group method of data handling (GMDH) is developed to investigate the viscosity of water-based nanofluids regarding temperature and especially pressure changes. The influence of high pressure has not been previously considered in any correlation. The results were estimated using the GMDH model and have higher accuracy than various theoretical models and empirical equations and show the coefficient of determination of 0.999.