Prediction of Nanofluid Characteristics and Flow Pattern on Artificial Differential Evolution Learning Nodes and Fuzzy Framework

Meisam Babanezhad; Samyar Zabihi; Ali Taghvaie Nakhjiri; Azam Marjani; Iman Behroyan; Saeed Shirazian

doi:10.1021/acsomega.0c02121

Prediction of Nanofluid Characteristics and Flow Pattern on Artificial Differential Evolution Learning Nodes and Fuzzy Framework

ACS Omega. 2020 Aug 25;5(35):22091-22098. doi: 10.1021/acsomega.0c02121. eCollection 2020 Sep 8.

Authors

Meisam Babanezhad^{1

2}, Samyar Zabihi³, Ali Taghvaie Nakhjiri⁴, Azam Marjani⁵, Iman Behroyan⁶, Saeed Shirazian^{7

8}

Affiliations

¹ Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
² Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang 550000, Vietnam.
³ Department of Process Engineering, Research and Development Department, Shazand-Arak Oil Refinery Company, Arak 38671-41111, Iran.
⁴ Department of Petroleum and Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran.
⁵ Department of Chemistry, Arak Branch, Islamic Azad University, Arak 31136-98562, Iran.
⁶ Mechanical and Energy Engineering Department, Shahid Beheshti University, Tehran 1983969411, Iran.
⁷ Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
⁸ Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam.

Abstract

A combination of a fuzzy inference system (FIS) and a differential evolution (DE) algorithm, known as the differential evolution-based fuzzy inference system (DEFIS), is developed for the prediction of natural heat transfer in Cu-water nanofluid within a cavity. In the development of the hybrid model, the DE algorithm is used for the training process of FIS. For this purpose, first, the case study is simulated using the computational fluid dynamic (CFD) method. The CFD outputs, including velocity in the y-direction, the temperature of the nanofluid, and the nanoparticle content (Ø), are employed for the learning process of the DEFIS model. By choosing the optimum number of inputs and the number of population, the underlying DEFIS variable parameters are studied. After reaching the high value of DEFIS intelligence, in the learning step, a variety of Ø values (e.g., 0.5, 1, and 2) are reviewed. For the full intelligence of DEFIS, the velocity of the nanofluid is predicted in further nodes of the cavity domain. Finally, the velocity of the nanofluid is predicted by using the data at Ø = 0.15, which are absent in the DEFIS process.