Two-phase pressure drop prediction in helically coiled steam generators for nuclear power applications

Andrea Cioncolini*, Lorenzo Santini

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

This study considers the prediction of the pressure gradient with water-steam two-phase flows through helically coiled steam generator tubes, focusing in particular on the operating conditions of low-medium pressure, low mass flux and low heat flux typical of once-through steam generators with in-tube boiling adopted in small modular nuclear reactor systems. Twenty-five widely used empirical correlations have been tested against an experimental pressure drop databank drawn together in this study containing 980 data points. Since no existing correlation is capable of collapsing and satisfactorily fitting the collected databank, a new pressure drop prediction method for helically coiled tubes is proposed. This new prediction method is very simple to implement, as it is based on the homogeneous flow model, is asymptotically consistent with straight tube two-phase flows and is largely superior in accuracy to existing prediction methods (mean absolute error of 7.3%, and 9 points out of 10 captured to within ±15%). The new prediction method is applicable for operating pressures in the range of 0.75-9.0 MPa, mass fluxes from 400 kg/m2s to 1191 kg/m2s, heat fluxes up to 750 kW/m2, tube diameters within 5-20 mm and coil to tube diameter ratio above 32.4. Curvature effects on the pressure gradient in helical coil two-phase flows can be significant, particularly with high velocity flows in tight curvature coils where the centrifugal force is intense.

Original languageEnglish
Pages (from-to)825-834
Number of pages10
JournalInternational Journal of Heat and Mass Transfer
Volume100
DOIs
StatePublished - 2016
Externally publishedYes

Keywords

  • Curvature effect
  • Helical coil pressure drop
  • Small modular nuclear reactor
  • Steam generator
  • Two-phase flow

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