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Other literature type . 2025
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Conference object . 2025
License: CC BY
Data sources: Datacite
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Conference object . 2025
License: CC BY
Data sources: Datacite
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Development of a general supercritical heat transfer correlation based on measurements on low GWP refrigerants

Authors: Jera Van Nieuwenhuyse; Stijn Van Isterdael; Jana Rogiers; Steven Lecompte; Michel De Paepe;

Development of a general supercritical heat transfer correlation based on measurements on low GWP refrigerants

Abstract

In order to design the heat exchangers of supercritical systems (e.g. vapor generators in transcritical ORCs), it is important to accurately predict the heat transfer. For this purpose, heat transfer correlations are developed. They should be specific enough to handle the exact geometry and fluid properties. Yet, even correlations that have been developed for horizontal flow of specific supercritical refrigerants, lack accuracy when applied to other refrigerants. This is an issue because at the moment most correlations are developed for refrigerants with a high Global Warming Potential (GWP). In addition, directives when to apply which correlation are also missing. In this work, the methodology of creating a generally applicable heat transfer correlation for supercritical refrigerants flowing horizontally under heating conditions is elaborated. First, the in-house built experimental test rig to perform heat transfer measurements of supercritical refrigerants is presented. Then, based on data available in literature, existing correlations are evaluated. Especially at the top of the tube, the existing correlations from literature have a low prediction capability. Next, the prediction capability of several correction factors commonly applied to heat transfer correlations for supercritical flows is investigated. Finally, a new, more generally applicable, heat transfer correlation based on this analysis is proposed. Both for the top and the bottom of the tube, incorporating a density-based correction factor to take into account the radial property variations improves the prediction capability. The prediction capability at the top can be further enhanced by incorporating an additional correction term accounting for buoyancy effects. For the bottom of the tube, 91% of the data can be predicted within a relative error below 30%. For the top, this is 90%.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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