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Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

Thermal performance of energy conversion systems is one of the most important goals to improve the system’s efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in additi...

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Autores principales: Rashidi, Mohammad M., Nazari, Mohammad Alhuyi, Mahariq, Ibrahim, Assad, Mamdouh El Haj, Ali, Mohamed E., Almuzaiqer, Redhwan, Nuhait, Abdullah, Murshid, Nimer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623954/
https://www.ncbi.nlm.nih.gov/pubmed/34835847
http://dx.doi.org/10.3390/nano11113084
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author Rashidi, Mohammad M.
Nazari, Mohammad Alhuyi
Mahariq, Ibrahim
Assad, Mamdouh El Haj
Ali, Mohamed E.
Almuzaiqer, Redhwan
Nuhait, Abdullah
Murshid, Nimer
author_facet Rashidi, Mohammad M.
Nazari, Mohammad Alhuyi
Mahariq, Ibrahim
Assad, Mamdouh El Haj
Ali, Mohamed E.
Almuzaiqer, Redhwan
Nuhait, Abdullah
Murshid, Nimer
author_sort Rashidi, Mohammad M.
collection PubMed
description Thermal performance of energy conversion systems is one of the most important goals to improve the system’s efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models.
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spelling pubmed-86239542021-11-27 Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study Rashidi, Mohammad M. Nazari, Mohammad Alhuyi Mahariq, Ibrahim Assad, Mamdouh El Haj Ali, Mohamed E. Almuzaiqer, Redhwan Nuhait, Abdullah Murshid, Nimer Nanomaterials (Basel) Review Thermal performance of energy conversion systems is one of the most important goals to improve the system’s efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models. MDPI 2021-11-16 /pmc/articles/PMC8623954/ /pubmed/34835847 http://dx.doi.org/10.3390/nano11113084 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Rashidi, Mohammad M.
Nazari, Mohammad Alhuyi
Mahariq, Ibrahim
Assad, Mamdouh El Haj
Ali, Mohamed E.
Almuzaiqer, Redhwan
Nuhait, Abdullah
Murshid, Nimer
Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study
title Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study
title_full Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study
title_fullStr Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study
title_full_unstemmed Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study
title_short Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study
title_sort thermophysical properties of hybrid nanofluids and the proposed models: an updated comprehensive study
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8623954/
https://www.ncbi.nlm.nih.gov/pubmed/34835847
http://dx.doi.org/10.3390/nano11113084
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