A novel sensitivity analysis model of EANN for F-MWCNTs-Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs

被引:129
作者
Bagherzadeh, Seyed Amin [1 ]
D'Orazio, Annunziata [2 ]
Karimipour, Arash [1 ]
Goodarzi, Marjan [3 ]
Quang-Vu Bach [3 ]
机构
[1] Islamic Azad Univ, Najafabad Branch, Dept Mech Engn, Najafabad, Iran
[2] Sapienza Univ Roma, Dipartimento Ingn Astronaut Elettr & Energet, Via Eudossiana 18, I-00184 Rome, Italy
[3] Ton Duc Thang Univ, Fac Environm & Labour Safety, Sustainable Management Nat Resources & Environm R, Ho Chi Minh City, Vietnam
关键词
Enhanced artificial neural network; Training algorithms; Sensitivity analysis; Nanofluid; LID-DRIVEN CAVITY; CONVECTION HEAT-TRANSFER; COPPER-WATER NANOFLUID; LATTICE BOLTZMANN METHOD; WALL CARBON NANOTUBES; STEAM POWER-PLANT; NATURAL-CONVECTION; SLIP VELOCITY; RHEOLOGICAL BEHAVIOR; MIXED-CONVECTION;
D O I
10.1016/j.physa.2019.01.048
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The new approach of "enhanced artificial neural network" (EANN) is developed based on the new generated hybrid nanocomposite of F-MWCNTs-Fe3O4/EG which represents the Functionalized Multi Walled Carbon Nano Tubes together with Fe3O4 nanoparticles, dispersed in ethylene glycol (EG) as the base fluid. Moreover, a new suitable sensitivity analysis is presented which involves a novel proposed method for the sensitivity analysis via ANNs. In this method, the sensitivity of the outputs predicted by means of an ANN to the inputs is calculated analytically rather than numerically. The proposed method not only provides more perceptive, precise and accurate results, but also requires less computational time and cost. The proposed method can be used for all ANNs having various architectures, training algorithms and input-output data sets. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:406 / 415
页数:10
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