Flow-pattern based prediction of flow boiling heat transfer in horizontal tubes with circumferentially varying heat flux. (February 2020)

• Record Type: Journal Article
• Authors: Weise, Sonja; Dietrich, Benjamin; Wetzel, Thomas
• Is Part Of: International journal of heat and mass transfer. Volume 148(2020)
• Publication Date: 2020-02
• Subjects: Two-phase flow -- Flow boiling -- Heat transfer -- Horizontal tube -- Circumferentially varying heat flux -- Modeling; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2019.119018
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Modeling heat transfer of flow boiling with circumferentially varying heat flux Adaption of flow-pattern based heat transfer models using local heat flux Validation with database of CO2 flow boiling at wide range of operating conditions Improvement from 56% to more than 80% of data within an error interval of ±30% Abstract: In order to describe the heat transfer coefficient during flow boiling in horizontal tubes with circumferentially varying heat flux, flow pattern-based models from the literature are validated and adapted using measurement data of the local and the circumferentially averaged heat transfer coefficient. Such boundary conditions occur when the wall temperature at the circumference is constant and the tube is partially wetted. The most important adaption is the calculation of the nucleate boiling contribution as a function of the local heat flux and not of the circumferentially averaged heat flux. Further adjustments concern the heat transfer coefficient in the dryout regime and during wavy flow. The database used to validate the method encompasses heat transfer coefficients of flow boiling of CO2 at saturation pressures from 10 bar to 60 bar, mass fluxes from 25 kg m -2 s -1 to 300 kg m -2 s -1, local heat fluxes up to 230 kW m -2, and vapor qualities from 10 % to 99 %. By applying the proposed adjustments, the quality of the prediction of the data is improved from 56 % to more than 80 % of the data within an error interval of ± 30 %. In addition, the comparison of local heat transfer coefficients provides a validation of the basic structure of the model and identifies the potential and the need for further refinement.