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Developing an ANFIS-based swarm concept model for estimating the relative viscosity of nanofluids
被引:119
作者:
Baghban, Alireza
[1
]
Jalali, Ali
[2
]
Shafiee, Mojtaba
[3
]
Ahmadi, Mohammad Hossein
[4
]
Chau, Kwok-wing
[5
]
机构:
[1] Amirkabir Univ Technol, Tehran Polytech, Dept Chem Engn, Mahshahr, Iran
[2] Amirkabir Univ Technol, Tehran Polytech, Dept Chem Engn, Tehran, Iran
[3] Jundi Shapur Univ Technol, Dept Chem Engn, Dezful, Iran
[4] Shahrood Univ Technol, Fac Mech Engn, Shahrood, Iran
[5] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Hong Kong, Peoples R China
关键词:
nanofluid;
viscosity;
ANFIS;
sensitivity analysis;
correlations;
WATER-BASED NANOFLUIDS;
THERMAL-CONDUCTIVITY;
HEAT-TRANSFER;
COMPUTATIONAL INTELLIGENCE;
PARTICLE-SIZE;
IONIC LIQUIDS;
PREDICTION;
TEMPERATURE;
HYBRID;
AL2O3;
D O I:
10.1080/19942060.2018.1542345
中图分类号:
T [工业技术];
学科分类号:
08 ;
摘要:
Nanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a limited range. In this study, 1277 experimental data points of distinct nanofluid relative viscosity (NF-RV) were gathered from a plenary literature review. In order to create a general model, adaptive network-based fuzzy inference system (ANFIS) code was expanded based on the independent variables of temperature, nanoparticle diameter, nanofluid density, volumetric fraction, and viscosity of the base fluid. A statistical analysis of the data for training and testing (with R-2 = .99997) demonstrates the accuracy of the model. In addition, the results obtained from ANFIS are compared to similar experimental data and show absolute and maximum average relative deviations of about 0.42 and 6.45%, respectively. Comparisons with other theoretical models from previous research is used to verify the model and prove the prediction capabilities of ANFIS. Consequently, this tool can be of huge value in helping chemists and mechanical and chemical engineers - especially those who are dealing with heat transfer applications by nanofluids - by providing highly accurate predictions of NF-RVs.
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页码:26 / 39
页数:14
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