• 한국안전학회지
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• 제37권6호
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• pp.166-173
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• 2022

A variety of input parameters are taken into consideration while performing a Level 3 PSA. Some parameters related to plume segments, spatial grids, and particle size distribution have flexible input formats. Fine modeling performed by splitting a number of segments or grids may enhance the accuracy of analysis but is time-consuming. Analysis speed is highly important because a considerably large number of calculations is required to handle Level 2 PSA scenarios for a single-unit or multi-unit Level 3 PSA. This study developed a sensitivity analysis supporting interface called MACCSsense to compare the results of the trials of plume segmentation with the results of the base case to determine its impact (in terms of time and accuracy) and to support the development of a modeling approach, which saves calculation time and improves accuracy. MACCSense is an automation tool that uses a large amount of plume segmentation analysis results obtained from MUST Converter and Mr. Manager developed by KAERI to generate a sensitivity report that includes impact (time and accuracy) by comparing them with the base-case result. In this study, various plume segmentation approaches were investigated, and both the accuracy and speed of offsite consequence analysis were evaluated using MACCS as a consequence analysis tool. A simultaneous evaluation revealed that execution time can be reduced using multi-threading. In addition, this study can serve as a framework for the development of a modeling strategy for plume segmentation in order to perform accurate and fast offsite consequence analyses.

• Nuclear Engineering and Technology
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• 제56권9호
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• pp.3851-3863
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• 2024

The speed of offsite consequence analysis is highly important due to the extensive calculations required to handle all the scenarios for a single-unit or multi-unit Level 3 PSA (probabilistic safety assessment). To perform an offsite consequence analysis as part of Level 3 PSA, various input parameters are considered, amongst which certain parameters, such as plume segments, spatial grids, and particle size distributions, have flexible input formats. This study describes the development of an effective optimization method to reduce the analysis time as much as possible while maintaining the accuracy of the offsite consequence analysis results. The effect of plume segmentation on offsite consequence analysis was investigated by observing deviations in analysis results and changes in the required analysis times following changes in plume release. Then a plume segmentation optimization method based on the cumulative release fraction slope was developed to intensively analyze the sections with rapid release and to simplify the analysis for the sections with nonsignificant release. As a result of applying this method, the analysis time was reduced by about 54.5 % compared to the base case, while the resulting health effects showed very small deviations of 0.03 % and 1.77 % for early fatality risk and cancer fatality risk, respectively.

• Journal of Information Processing Systems
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• 제9권4호
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• pp.621-632
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• 2013

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.

• 한국안전학회지
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• 제39권1호
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• pp.96-103
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• 2024

The demand for mass calculation of offsite consequence analysis to conduct exhaustive single-unit or multi-unit Level 3 PSA is increasing. In order to perform efficient offsite consequence analyses, the Korea Atomic Energy Research Institute is conducting model optimization studies to minimize the analysis time while maintaining the accuracy of the results. A previous study developed a model optimization method using efficient plume segmentation and verified its effectiveness. In this study, we investigated the possibility of optimizing the model through particle size distribution setting by checking the reduction in analysis time and deviation of the results. Our findings indicate that particle size distribution setting affects the results, but its effect on analysis time is insignificant. Therefore, it is advantageous to set the particle size distribution as fine as possible. Furthermore, we evaluated the effect of multithreading and confirmed its efficiency. Future optimization studies should be conducted on various input factors of offsite consequence analysis, such as spatial grid settings.