A comprehensive, mechanistic heat transfer modeling package for dispersed flow film boiling - Part 2 - Implementation and assessment

Michael J. Meholic, David L. Aumiller, Fan-bill B. Cheung

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Abstract A mechanistic, first-principles based Dispersed Flow Film Boiling (DFFB) heat transfer package has been implemented within the existing framework of an in-house version of COBRA-TF, called COBRA-IE. Several sensitivities studies were performed on the proposed model to determine how to characterize the droplet size distribution and to validate the use of a quasi-static Lagrangian subscale trajectory calculation within COBRA-IE. Assessing the proposed model against experimental data from 118 experimental runs in four separate facilities has shown a marked improvement over the predictions of the base line COBRA-IE DFFB model set. Over the entire assessment database, the proposed model has reduced the mean error, RMS error, and standard deviation of the error in the wall temperature predictions and improved the prediction in the axial variation of the wall temperature. The proposed DFFB model marks a step-change in the use of mechanistically based DFFB models in reactor safety analysis codes resulting in improved predictive capabilities.

Original languageEnglish (US)
Article number8379
Pages (from-to)302-311
Number of pages10
JournalNuclear Engineering and Design
Volume291
DOIs
StatePublished - Sep 1 2015

Fingerprint

film boiling
Boiling liquids
heat transfer
Heat transfer
modeling
wall temperature
prediction
predictions
reactor safety
droplet
standard deviation
temperature
trajectory
Trajectories
trajectories
Temperature
sensitivity

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Materials Science(all)
  • Safety, Risk, Reliability and Quality
  • Waste Management and Disposal
  • Mechanical Engineering

Cite this

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abstract = "Abstract A mechanistic, first-principles based Dispersed Flow Film Boiling (DFFB) heat transfer package has been implemented within the existing framework of an in-house version of COBRA-TF, called COBRA-IE. Several sensitivities studies were performed on the proposed model to determine how to characterize the droplet size distribution and to validate the use of a quasi-static Lagrangian subscale trajectory calculation within COBRA-IE. Assessing the proposed model against experimental data from 118 experimental runs in four separate facilities has shown a marked improvement over the predictions of the base line COBRA-IE DFFB model set. Over the entire assessment database, the proposed model has reduced the mean error, RMS error, and standard deviation of the error in the wall temperature predictions and improved the prediction in the axial variation of the wall temperature. The proposed DFFB model marks a step-change in the use of mechanistically based DFFB models in reactor safety analysis codes resulting in improved predictive capabilities.",
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A comprehensive, mechanistic heat transfer modeling package for dispersed flow film boiling - Part 2 - Implementation and assessment. / Meholic, Michael J.; Aumiller, David L.; Cheung, Fan-bill B.

In: Nuclear Engineering and Design, Vol. 291, 8379, 01.09.2015, p. 302-311.

Research output: Contribution to journalArticle

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AB - Abstract A mechanistic, first-principles based Dispersed Flow Film Boiling (DFFB) heat transfer package has been implemented within the existing framework of an in-house version of COBRA-TF, called COBRA-IE. Several sensitivities studies were performed on the proposed model to determine how to characterize the droplet size distribution and to validate the use of a quasi-static Lagrangian subscale trajectory calculation within COBRA-IE. Assessing the proposed model against experimental data from 118 experimental runs in four separate facilities has shown a marked improvement over the predictions of the base line COBRA-IE DFFB model set. Over the entire assessment database, the proposed model has reduced the mean error, RMS error, and standard deviation of the error in the wall temperature predictions and improved the prediction in the axial variation of the wall temperature. The proposed DFFB model marks a step-change in the use of mechanistically based DFFB models in reactor safety analysis codes resulting in improved predictive capabilities.

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