• Journal of the Korean Institute of Gas
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• v.2 no.1
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• pp.7-13
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• 1998

In this study, for the safety performance of LNG storage tank, the fracture toughness in X-grooved weld HAZ(heat-affected zone) of $9\%$ Ni steel was evaluated qualitatively and quantitatively, and the relation with the change in microstructure was analyzed. The toughness assessment was peformed through the modified CTOD test proposed for thick weldment with X-groove. Additionally, microstructures of HAZ were evaluated by OM, SEM and XRD. From the results, HAZ toughness of SMA(shielded metal arc)-welded $9\%$ Ni steel decreased as the evaluated region approached the fusion line. The decrease in toughness was apparently caused by the increase in the fraction of coarse-grained zone within HAZ. On the other hand, toughness drop with decreasing test temperature in F.L.(fusion line) ${\~}$F.L.+3mm was larger than that in F.L.+5mm${\~}$F.L.+7mm region due to the fact that in the former regions, retained austenite had poor stability.

• Tunnel and Underground Space
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• v.19 no.3
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• pp.203-212
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• 2009

The prediction of near field behavior around an underground high-level radioactive waste repository is important for the repository design as well as the safety assessment. In this study, a sensitivity analysis for seven parameters consisted of design parameters and material properties was carried out using a three-dimensional finite difference code. From the sensitivity analysis, it was found that the effects of borehole spacing, tunnel spacing, cooling time and rock thermal conductivity were more significant than the other parameters. For getting a statistical distribution of buffer and rock temperatures around the repository, an artificial neural network, backpropagation, was applied. The reliability of the trained neural network was tested with the cases with randomly chosen input parameters. When the parameter variation is within ${\pm}10%$, the prediction from the network was found to be reliable with about a 1% error. It was possible to calculate the temperature distribution for many cases quickly with the trained neural network. The buffer and rock temperatures showed a normal distribution with means of $98^{\circ}C$ and $83.9^{\circ}C$ standard deviations of $3.82^{\circ}C$ and $3.67^{\circ}C$, respectively. Using the neural network, it was also possible to estimate the required change in design parameters for reducing the buffer and rock temperatures for $1^{\circ}C$.

• Fire Science and Engineering
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• v.30 no.5
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• pp.74-81
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• 2016

This study evaluated the habitability of operators for main control room fires in nuclear power plants. Fire modeling (FDS v.6.0) was utilized for a fire safety assessment so that it could determine the performance of the smoke ventilation and operator habitability with the main control room. For this study, it categorized fire scenarios into three cases depending on the conditions in the HVAC system. As a result of fire modelling, it showed that Case 1 (with HVAC) would give rise to the worst situation associated with the absolute temperature, radiative heat flux, optical density, and smoke layer height as deliberating the habitability and smoke ventilation. On the other hand, it showed that Cases 2 (w/o HVAC) and 3 can maintain much safer situations than Case 1. In the case of temperature at 820 s, Cases 2 and 3 were up to approximately 63% greater than Case 1 in the wake of ignition. In addition, the influence of radiative heat flux of Case 1 was even larger than Cases 2 and 3. That is, the radiative heat fluxes of Cases 2 and 3 were approximately 68% higher than Case 1. Furthermore, when it comes to considering the optical density, Case 1 was approximately 93% greater than Cases 2 and 3. Accordingly, it expected that the HVAC system can influence a the performance on the smoke ventilation that can be sustainable for operator habitability. On the other hand, it revealed an inconsecutive pattern for the smoke layer height of Cases 2 and 3 because supply vents and exhaust vents were installed within the same surface.