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Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning

Crossref DOI link: https://doi.org/10.1007/s10973-023-12083-7

Published Online: 2023-04-07

Published Print: 2023-06

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Ullah, Atta https://orcid.org/0000-0001-8010-3904
Kilic, Mustafa

Habib, Ghulam

Sahin, Mahir

Khalid, Rehan Zubair

Sanaullah, Khairuddin
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Text and Data Mining valid from 2023-04-07

Version of Record valid from 2023-04-07
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Article History

Received: 2 September 2022

Accepted: 10 March 2023

First Online: 7 April 2023

Declarations

:

: The authors declare that they have no conflict of interest.

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