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Technical Papers

Numerical Investigation of Dryout Heat Flux and Heat Transfer Characteristics in Core Debris Bed of SFR After Severe Accident

, , , , , & show all
Pages 115-130 | Received 27 Apr 2018, Accepted 17 Aug 2018, Published online: 29 Oct 2018
 

Abstract

Nuclear reactor severe accidents can lead to the release of a large amount of radioactive material and cause immense disaster to the environment. Based on a heat conduction model, the DEBRIS-HT program for analyzing the heat transfer characteristics of a debris bed after a severe accident of a sodium-cooled fast reactor was developed. The basic methodology of the DEBRIS-HT program is to simplify the complex energy transfer process in the debris bed to a simple heat transfer problem by solving the equivalent thermal conductivity in different situations. In this paper, the models of the DEBRIS-HT code are explained in detail. The comparison between the simulation and experimental results shows that the DEBRIS-HT program can correctly estimate the heat transfer process in the debris bed. In addition, the DEBRIS-HT code is applied to model the core catcher of the China fast reactor. The calculated dryout heat flux of the postulated accident, in which 100% of core melts and drops on the core catcher, agrees well with the prediction result of the Lipinski’s zero-dimensional model. And the error between them is about 10%. The calculated dependence of dryout heat flux on particle size is also in good consistence with the prediction by Lipinski’s zero-dimensional model. Then, the temperature distribution and the temperature excursion of the debris bed during a likely accident are analyzed, which provides significant reference to the severe accident analysis.

Nomenclature

cp ==

coolant specific heat capacity [J/(kg·K)]

Dc ==

hydraulic diameter (m)

d ==

average particle diameter (m)

ΔE ==

phase change energy (J/m3)

H ==

channel length ratio

HB ==

energy required to reach dryout (J/m3)

hlv ==

coolant latent heat of vaporization (J/kg)

KB ==

equivalent thermal conductivity in unboiling area [W/(m2·K)]

KB,boil ==

equivalent thermal conductivity in boiling area [W/(m2·K)]

KB,dry ==

equivalent thermal conductivity in dryout area [W/(m2·K)]

KB\rvprime ==

equivalent thermal conductivity [W/(m2·K)]

KB,boil ==

equivalent thermal conductivity at boiling state [W/(m2·K)]

KB,dry\rvprime ==

equivalent thermal conductivity at dryout state [W/(m2·K)]

KB,unb\rvprime ==

equivalent thermal conductivity at unboiling state [W/(m2·K)]

KC ==

coolant thermal conductivity [W/(m2·K)]

Ke,f ==

fuel equivalent thermal conductivity [W/(m2·K)]

Ke,s ==

stainless steel equivalent thermal conductivity [W/(m2·K)]

KF ==

fuel thermal conductivity [W/(m2·K)]

Ks ==

stainless steel thermal conductivity [W/(m2·K)]

L ==

bed thickness (m)

Lc ==

channel length (m)

Nc ==

density of channel [J/(kg·K)]

Pl ==

liquid pressure (Pa)

Pv ==

gas pressure (Pa)

Q ==

heat generation rate [W/m3]

s ==

degree of saturation

sold ==

degree of saturation of last step

sTdry ==

degree of saturation of dryout state

T ==

coolant temperature (K)

Tdry ==

cooler temperature at dryout state (K)

TN ==

coolant temperature at current step (K)

TN1 ==

coolant temperature at last step (K)

TNF ==

function of the degree of saturation (K)

TN\rvprime ==

coolant temperature at new step (K)

Vl ==

velocity of liquid coolant (m3)

Vv ==

velocity of gas-phase coolant (m3)

x ==

volume fraction of fuel

Greek

ε ==

porosity

κ ==

permeability (m2)

κl ==

liquid permeability (m2)

κv,c ==

gas permeability in channeling area (m2)

κv ==

gas phase permeability (m2)

κl\rvprime ==

liquid flow resistance coefficient (s−1)

κv ==

gas flow resistance coefficient (s−1)

κv,c ==

gas flow resistance coefficient in channeling area (s−1)

μl ==

liquid dynamic viscosity (Pa·s)

μv ==

gas dynamic viscosity (Pa·s)

ρl ==

density of liquid coolant (kg/m3)

ρv ==

density of gas coolant (kg/m3)

σ ==

coefficient of surface tension (N/m)

Acknowledgments

This project is supported by the National Natural Science Foundation of China (grant no. 11005085). The authors would like to thank the National Natural Science Foundation of China for its support in this research.

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