Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature

Abstract A nanofluid containing water and nanoparticles made of copper (Cu) inside a cavity with square shape is simulated utilizing the computational fluid dynamics (CFD) approach. The nanoparticles made up 15% of the nanofluid. By performing the simulation, the CFD output is characterized by the c...

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Autores principales:	Meisam Babanezhad, Ali Taghvaie Nakhjiri, Azam Marjani, Mashallah Rezakazemi, Saeed Shirazian
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/768cf09976a4425b83b0b239d4088bae
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spelling	oai:doaj.org-article:768cf09976a4425b83b0b239d4088bae2021-12-02T13:58:10ZEvaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature10.1038/s41598-020-79293-z2045-2322https://doaj.org/article/768cf09976a4425b83b0b239d4088bae2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79293-zhttps://doaj.org/toc/2045-2322Abstract A nanofluid containing water and nanoparticles made of copper (Cu) inside a cavity with square shape is simulated utilizing the computational fluid dynamics (CFD) approach. The nanoparticles made up 15% of the nanofluid. By performing the simulation, the CFD output is characterized by the coordinates in the x, y, nanofluid temperature, and velocity in the y-direction that these outputs are obtained for different physical time iterations. Moreover, the CFD outputs are examined by one of the artificial techniques, i.e. adaptive network-based fuzzy inference system (ANFIS). For this purpose, the data was clustered via grid partition clustering, and the type of membership functions (MFs) was chosen product of two sigmoidal membership functions (psigmf). After reaching 99.9% of intelligence in ANFIS, the nanofluid temperature is predicted for the entire data, which are included in the learning processes. The results showed that the method of ANFIS can predict the thermal properties in different physical times at different computing points without having a training background at those times. Additionally, this study shows that with three membership functions at each input, the model’s accuracy is higher than four functions.Meisam BabanezhadAli Taghvaie NakhjiriAzam MarjaniMashallah RezakazemiSaeed ShirazianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Meisam Babanezhad Ali Taghvaie Nakhjiri Azam Marjani Mashallah Rezakazemi Saeed Shirazian Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature
description	Abstract A nanofluid containing water and nanoparticles made of copper (Cu) inside a cavity with square shape is simulated utilizing the computational fluid dynamics (CFD) approach. The nanoparticles made up 15% of the nanofluid. By performing the simulation, the CFD output is characterized by the coordinates in the x, y, nanofluid temperature, and velocity in the y-direction that these outputs are obtained for different physical time iterations. Moreover, the CFD outputs are examined by one of the artificial techniques, i.e. adaptive network-based fuzzy inference system (ANFIS). For this purpose, the data was clustered via grid partition clustering, and the type of membership functions (MFs) was chosen product of two sigmoidal membership functions (psigmf). After reaching 99.9% of intelligence in ANFIS, the nanofluid temperature is predicted for the entire data, which are included in the learning processes. The results showed that the method of ANFIS can predict the thermal properties in different physical times at different computing points without having a training background at those times. Additionally, this study shows that with three membership functions at each input, the model’s accuracy is higher than four functions.
format	article
author	Meisam Babanezhad Ali Taghvaie Nakhjiri Azam Marjani Mashallah Rezakazemi Saeed Shirazian
author_facet	Meisam Babanezhad Ali Taghvaie Nakhjiri Azam Marjani Mashallah Rezakazemi Saeed Shirazian
author_sort	Meisam Babanezhad
title	Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature
title_short	Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature
title_full	Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature
title_fullStr	Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature
title_full_unstemmed	Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature
title_sort	evaluation of product of two sigmoidal membership functions (psigmf) as an anfis membership function for prediction of nanofluid temperature
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/768cf09976a4425b83b0b239d4088bae
work_keys_str_mv	AT meisambabanezhad evaluationofproductoftwosigmoidalmembershipfunctionspsigmfasananfismembershipfunctionforpredictionofnanofluidtemperature AT alitaghvaienakhjiri evaluationofproductoftwosigmoidalmembershipfunctionspsigmfasananfismembershipfunctionforpredictionofnanofluidtemperature AT azammarjani evaluationofproductoftwosigmoidalmembershipfunctionspsigmfasananfismembershipfunctionforpredictionofnanofluidtemperature AT mashallahrezakazemi evaluationofproductoftwosigmoidalmembershipfunctionspsigmfasananfismembershipfunctionforpredictionofnanofluidtemperature AT saeedshirazian evaluationofproductoftwosigmoidalmembershipfunctionspsigmfasananfismembershipfunctionforpredictionofnanofluidtemperature
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Evaluation of product of two sigmoidal membership functions (psigmf) as an ANFIS membership function for prediction of nanofluid temperature

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