Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids
Loading...
Date
Authors
Mehrabi, M.
Sharifpur, Mohsen
Meyer, Josua P.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
By using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial
neural network as well as experimental data, two models were established in order to
predict the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In these
models, the target parameter was the thermal conductivity ratio, and the nanoparticle
volume concentration, temperature and Al2O3 nanoparticle size were considered as the
input (design) parameters. The empirical data were divided into train and test sections
for developing the models. Therefore, they were instructed by 80% of the experimental
data and the remaining data (20%) were considered for benchmarking. The results,
which were obtained by the proposed FCM-based Neuro-Fuzzy Inference System (FCMANFIS)
and Genetic Algorithm-Polynomial Neural Network (GA-PNN) models, were
provided and discussed in detail.
Description
Keywords
Thermal conductivity ratio, FCM-based Neuro-Fuzzy Inference System (FCM-ANFIS), Genetic Algorithm-Polynomial Neural Network (GA-PNN), Group Method of Data Handling (GMDH)
Sustainable Development Goals
Citation
M. Mehrabi, M. Sharifpur & J.P. Meyer, Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids, International Communications in Heat and Mass Transfer, vol. 39, no. 7, pp. 971-977 (2012), doi: 10.1016/j.icheatmasstransfer.2012.05.017