Assessment of existing two phase heat transfer coefficient and critical heat flux correlations for cryogenic flow boiling in pipe quenching experiments. (February 2016)

• Record Type: Journal Article
• Authors: Hartwig, Jason; Darr, Samuel; Asencio, Anthony
• Is Part Of: International journal of heat and mass transfer. Volume 93(2016:Feb.)
• Page Start: 441
• Page End: 463
• Publication Date: 2016-02
• Subjects: Liquid hydrogen -- Liquid nitrogen -- Quenching -- Critical heat flux -- Nucleate boiling -- Film boiling -- SINDA/FLUINT; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2015.09.028
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: 214 cryogenic quenching critical heat flux data points are gathered. 29, 367 cryogenic quenching heat transfer coefficient data points gathered. Trends in cryogenic quenching film boiling and nucleate boiling are identified. Universal correlations and correlations in SINDA/FLUENT and GFSSP are compared to data. Correlations over-predict by as much as 30, 000%. Abstract: To enable efficient design and analysis of cryogenic propellant transfer systems, high accuracy models are required for predicting two phase flow boiling and heat transfer at reduced temperatures. The penalty for poor models translates into higher margin, safety factor, and ultimately cost in design. Recently, there has been a drive towards developing universal correlations to cover a broad range of fluids, tube diameters, and thermodynamic conditions for predicting heat flux and pressure drop. These correlations do not, however, cover cryogenic fluids like liquid hydrogen. Therefore the purpose of this paper is to apply popular two phase heat transfer correlations used in commercial codes against available flow boiling data for cryogenic fluids. Specifically, quenching test data for critical heat flux and two phase heat transfer coefficient are compared against the correlations. Results show that existing correlations over-predict heat transfer by as much as 20, 000% and that significant model improvements are warranted.