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Westinghouse is currently developing the MEFISTO code with the main goal to achieve fast, robust, practical and reliable prediction of steady-state dryout Critical Power in Boiling Water Reactor (BWR) fuel bundle based on a mechanistic approach. To achieve this goal, the code resolves the multi-film mass balance equations at the sub-channel level within the annular flow region while relying on a simple two-field (liquid/steam) sub-channel solution to provide the cross-flow information. The MEFISTO code can hence provide detailed solution to the multi-film flow in BWR fuel bundle while enhancing flexibility and reducing the computer time by an order of magnitude as compared to standard three-field subchannel analysis. Models for the numerical computation of the sub-channel multi-film flowrate distributions, including the treatment of cross-flows, part-length rods and spacers grids are presented in this paper. The MEFISTO code is then applied to dryout prediction in BWR fuel bundle using VIPRE-W as a two-field sub-channel driver code (based on a fast and robust four-equation model). The dryout power is numerically predicted by iterating on the bundle power so that the minimum film flowrate in the bundle reaches the dryout criteria. Dryout predicted powers (including trends with flow, pressure, inlet subcooling and power distribution) and predicted dryout locations are compared to experimental results, using a large Westinghouse SVEA-96 dryout database, and are shown to yield excellent results.

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Each BWR fuel design requires a method to predict its dryout performance in order to be licensed. Presently, the assessment of dry-out risk is based on empirical correlations, which sometimes results in inaccurate or non-physical predictions in certain portions of operational space. This poses a number of limitations as plant operators seek to extract additional value from the fuel through more aggressive operation strategies. A new form of BWR dryout correlation is developed. Accuracy of predictions outside of experimental data range is increased by employing a non-linear correlation form and the transformation to axial power profile, which is based on physical considerations. Proper qualitative behavior is assured by the correlation form itself rather than values of regression coefficients.