1
|
Wang H, Chen X. A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Helin Wang
- Faculty of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou, Liaoning 121001, China
| | - Xueye Chen
- College of Transportation, Ludong University, Yantai, Shandong 264025, China
| |
Collapse
|
2
|
Hemmat Esfe M, Goodarzi M, Esfandeh S. Statistical and Intelligent Analysis of Viscosity behavior of MgO-MWCNT (25–75%)/10W40 Hybrid Nanolubricant Using Artificial Neural Network Modeling and Response Surface Methodology. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-06068-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
3
|
Ilyas SU, Ridha S, Sardar S, Estellé P, Kumar A, Pendyala R. Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.115509] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
|
4
|
Shateri M, Sobhanigavgani Z, Alinasab A, Varamesh A, Hemmati-Sarapardeh A, Mosavi A, S S. Comparative Analysis of Machine Learning Models for Nanofluids Viscosity Assessment. NANOMATERIALS 2020; 10:nano10091767. [PMID: 32906742 PMCID: PMC7558292 DOI: 10.3390/nano10091767] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 08/29/2020] [Accepted: 08/31/2020] [Indexed: 11/16/2022]
Abstract
The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.
Collapse
Affiliation(s)
- Mohammadhadi Shateri
- Department of Electrical & Computer Engineering, McGill University, Montreal, QC H3A 2K6, Canada; (M.S.); (Z.S.); (A.A.)
| | - Zeinab Sobhanigavgani
- Department of Electrical & Computer Engineering, McGill University, Montreal, QC H3A 2K6, Canada; (M.S.); (Z.S.); (A.A.)
| | - Azin Alinasab
- Department of Electrical & Computer Engineering, McGill University, Montreal, QC H3A 2K6, Canada; (M.S.); (Z.S.); (A.A.)
| | - Amir Varamesh
- Department of Chemical & Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran
- College of Construction Engineering, Jilin University, Changchun 130600, China
- Correspondence: (A.H.-S.); (A.M.); (S.S.)
| | - Amir Mosavi
- Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
- School of Economics and Business, Norwegian University of Life Sciences, 1430 Ås, Norway
- Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
- Correspondence: (A.H.-S.); (A.M.); (S.S.)
| | - Shahab S
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
- Correspondence: (A.H.-S.); (A.M.); (S.S.)
| |
Collapse
|