Price-performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes. (5th January 2018)

• Record Type: Journal Article
• Authors: Alirezaie, Ali; Hajmohammad, Mohammad Hadi; Hassani Ahangar, Mohammad Reza; Hemmat Esfe, Mohammad
• Is Part Of: Applied thermal engineering. Volume 128(2018)
• Page Start: 373
• Page End: 380
• Publication Date: 2018-01-05
• Subjects: Heat transfer -- Nanofluid -- Thermal conductivity -- Price-performance analysis -- Artificial neural network -- Modeling; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.08.143
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Comparison between thermal conductivity of MgO and Fe/EG based nanofluid. Introduction of a new index for measuring the efficiency of nanofluids. Comparison between 2 nanofluids in terms of PPF. Proposing a new model to calculate the thermal conductivity of nanofluids. Modeling data of nanofluids using artificial neural network. Abstract: The nanofluid's thermal conductivity has been one of the most fascinating topics for researchers in this field. So far, a large number of aqueous, non-aqueous and also oleic nanofluids are experimentally studied. These nanofluids have been investigated with different volume fractions, various particle sizes and different production methods. But, the question seems to be very important now is that among available nanofluids, which one can meet our needs better? What particle sizes will work best for a chosen nanoparticle? Whether pure experimental measuring is reliable for using a nanofluid in the industry or not? Two nanofluids of Fe and MgO in Ethylene Glycol base-fluid with various particle sizes have been probed in this research, and the preparation cost of these nanofluids is compared with their thermal performance and proportionate responses are given to above questions. As well, a correlation is provided in order to measure thermal conductivity of nanofluids in terms of thermal conductivity, particle size, temperature and volume fraction of nanoparticles, and the data were compared with the result obtained from a neural network modeling.