Thermal conductivity estimation of nanofluids with TiO2 nanoparticles by employing artificial neural networks. (28th January 2021)

• Record Type: Journal Article
• Authors: Komeili Birjandi, Ali; Irandoost Shahrestani, Misagh; Maleki, Akbar; Habibi, Ali; Pourfayaz, Fathollah
• Is Part Of: International journal of low carbon technologies. Volume 16:Number 3(2021)
• Page Start: 740
• Page End: 746
• Publication Date: 2021-01-28
• Subjects: nanofluid -- thermal conductivity -- heat transfer -- TiO2 nanoparticles; Climatic changes -- Environmental aspects -- Periodicals; Energy industries -- Technological innovations -- Periodicals; Sustainable architecture -- Periodicals; Sustainable buildings -- Periodicals; 621.042
• DOI: 10.1093/ijlct/ctab003
• Languages: English
• ISSNs: 1748-1317

Abstract

Abstract: Applying nanofluids in energy-related technologies and thermal mediums can lead to remarkable enhancement in their efficiency and performance due to their modified thermophysical properties. Among thermophysical properties, thermal conductivity (TC) performs principal role in heat transfer ability of nanofluids. Artificial neural networks (ANNs) have shown promising performance in modeling nanofluids' TC. In this article, two types of ANNs are used for estimating TC of nanofluids with TiO2 nanoparticles. In this regard, effective factors including particle size, temperature, volume fraction of solid particles and TC of the base fluids are applied at the input of the model. Based on the comparison between the estimated data and the corresponding actual ones, it is concluded that employing multi-layer perceptron (MLP) is superior compared with group method of data handling (GMDH). In the optimal conditions of the networks, the R-squared value of the models based on both MLP and GMDH was 0.999. Moreover, average absolute relative deviations of the mentioned models were around 0.23% and 0.32%, respectively.