Influence of porous inserts on flow boiling heat transfer in horizontal tubes
•Analyzing local heat transfer data of flow boiling CO2 in tubes with porous inserts.•Identification of the main influencing factors: wetting and convection.•Modeling based on established sub-models without fitting parameters.•Proposed model describes more than 90% of the data within an error interv...
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Veröffentlicht in: | International journal of heat and mass transfer 2021-06, Vol.171, p.121087, Article 121087 |
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Sprache: | eng |
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Zusammenfassung: | •Analyzing local heat transfer data of flow boiling CO2 in tubes with porous inserts.•Identification of the main influencing factors: wetting and convection.•Modeling based on established sub-models without fitting parameters.•Proposed model describes more than 90% of the data within an error interval of ±30%.
In this paper we want to assess the mechanisms by which metal porous inserts influence flow boiling heat transfer in horizontal tubes. For this purpose, measurement data of the local and circumferentially averaged heat transfer coefficient of CO2 flow boiling in horizontal tubes with two different porous inserts are presented. As inserts, different sponges (open-cell network structures) and a wire matrix element are chosen and compared with each other. Different operating conditions were investigated. The mass flux was varied in a range from 25 kg m−2 s−1 to 190 kg m−2 s−1 and the vapor quality from 10% to 100%. Saturation pressures of 12 bar, 19 bar and 26.5 bar and average heat fluxes up to 65 kW m−2 were realized. By comparing the local heat transfer coefficient with empty tube data, wetting and convective heat transfer can be identified as the main influencing factors. Thus, we can obtain guidelines for the development of a model to describe the heat transfer coefficient of two-phase flow in porous inserts. A model derived in this way describes the measurement data of flow boiling of CO2 in horizontal tubes with inserts with a high degree of accuracy. The model is based on established sub-models without any additional fitting parameter. More than 90% of all measured data are predicted with less than 30% deviation |
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ISSN: | 0017-9310 1879-2189 |
DOI: | 10.1016/j.ijheatmasstransfer.2021.121087 |