Advances in the Reliability Analysis of Coherent Systems under Limited Data with Confidence Boxes

This paper proposes an uncertainty quantification framework that enables the analyst to compute statistically calibrated confidence bounds of the reliability of coherent systems, even in the case in which the data are limited. More specifically, we propose to use confidence boxes to do so. Such a proposal is motivated by the fact that confidence boxes offer a guarantee on statistical performances regardless of the amount of available data through repeated use, which is especially useful when considering the fact that reliability or failure data for the reliability analysis are often limited in availability. The aim of this work is to provide tools that allow the analyst to obtain the true confidence intervals over the system failure and reliability at any desired level. This paper first reviews the basics of confidence boxes and reliability analysis before providing general computation tools. From this, a mathematical formalism is presented that relates the component configurations with the corresponding Boolean logic expressions to perform a forward propagation of the confidence boxes under varying dependencies between the components. The feasibility of the proposed framework is then demonstrated through three case studies involving complex systems under varying engineering settings in the form of the (1) pressurized tank system, (2) Training, Research, Isotopes, General Atomics (TRIGA) nuclear research reactor cooling system, and (3) bridge structure system. Through case studies, the validity of our studies is empirically shown, and an evaluation of the strengths and limitations of the proposed framework is presented. Finally, this paper provides perspectives on the future research works that can be undertaken. To provide a better understanding of the proposed framework, open R source code to reproduce the results and perform other related studies is available on GitHub

Assessment of Cesium Compound Behavior during Simultaneous Failure of Reactor Pressure Vessels and Spent Fuel Pools Using Modified ART Mod 2: Fukushima Daiichi Accident Simulation

To support the regional strategy development of ASEAN NPSR using scientific research, Modified ART Mod 2 has been used to assess the fission product release from RPVs and SFPs independently. However, the Fukushima Accident suggested the possibility of simultaneous release from RPV and SFP which indicated the necessity of re-evaluation of the maximum source term. The objective was to assess the fission product behavior during a simultaneous failure in RPVs and SFPs of BWR type with Mark I containment design in multiple units using Modified ART Mod 2 in order to evaluate the maximum source term. The releases of cesium compounds in gas and aerosol forms from RPVs and SFPs of Units 1–3 of the Fukushima Daiichi NPP were selected as the case studies. It was found that the behavior of cesium compounds was mainly governed by the aerosols and atmospheric temperatures, which resulted in different characteristics in adsorption and thermophoresis. It also turned out that the simulation of a simultaneous release led to a smaller release than the summation of independent simulations of releases from RPV and SFP by 25%. This study helped estimate the maximum consequences in order to be able to effectively design the EPR for NPP accidents inside or outside the ASEAN region

Assessment of Cesium Compound Behavior during Simultaneous Failure of Reactor Pressure Vessels and Spent Fuel Pools Using Modified ART Mod 2: Fukushima Daiichi Accident Simulation

To support the regional strategy development of ASEAN NPSR using scientific research, Modified ART Mod 2 has been used to assess the fission product release from RPVs and SFPs independently. However, the Fukushima Accident suggested the possibility of simultaneous release from RPV and SFP which indicated the necessity of re-evaluation of the maximum source term. The objective was to assess the fission product behavior during a simultaneous failure in RPVs and SFPs of BWR type with Mark I containment design in multiple units using Modified ART Mod 2 in order to evaluate the maximum source term. The releases of cesium compounds in gas and aerosol forms from RPVs and SFPs of Units 1–3 of the Fukushima Daiichi NPP were selected as the case studies. It was found that the behavior of cesium compounds was mainly governed by the aerosols and atmospheric temperatures, which resulted in different characteristics in adsorption and thermophoresis. It also turned out that the simulation of a simultaneous release led to a smaller release than the summation of independent simulations of releases from RPV and SFP by 25%. This study helped estimate the maximum consequences in order to be able to effectively design the EPR for NPP accidents inside or outside the ASEAN region.</jats:p

2009–2022 Thailand public perception analysis of nuclear energy on social media using deep transfer learning technique

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies

Validation of Modified ART Mod 2 Code through Comparison with Aerosol Deposition of Cesium Compound in Phébus FPT3 Containment Vessel

Evaluation of aerosol deposition in the containment vessel is an important step for the assessment of radioactive material release to the environment. ART Mod 2 is a calculation code that is used for evaluation of aerosol deposition in the containment vessel. The authors modified aerosol deposition models of ART Mod 2, namely, gravitational settling model, Brownian diffusion model, diffusiophoresis model, and thermophoresis model in order to increase potential of capturing the deposition phenomena. This study aims to compare the simulated results of modified ART Mod 2 with aerosol deposition of cesium compounds in the containment vessel of Phébus FPT3 experiment, in order to validate modified ART Mod 2 code. It is found that aerosol deposition using modified ART Mod 2 agrees with Phébus FPT3. Prediction of Brownian diffusion is significantly improved due to the consideration of turbulent damping process. Cesium mass flow rate and aerosol size are factors that can significantly influence the uncertainty of the results. When conditions of single volumes are carefully selected to match those of the Phébus FPT3 experiment, modified ART Mod 2 can predict aerosol deposition in Phébus FPT3 with relative accuracy

Identifying a probe to visualize the variability of operating teams for supporting the human reliability analysis of nuclear power plants: An explanatory study

Operating teams consisting of several team members still play a critical role in coping with off-normal conditions in socio-technical systems. Thus, various kinds of human reliability analysis methods have been suggested based on the consideration of diverse performance shaping factors that can affect the performance of team members. Unfortunately, since multiple performance shaping factors can vary across operating teams (i.e., crew-to-crew variability), it is crucial to figure out how to visualize this variability in a systematic way. In this regard, comparing the cultural characteristics of operating teams with their performance would be a good starting point. This study investigates how cultural characteristics can be correlated with the occurrence of unsafe acts based on empirical data collected from operating teams working in the main control room of Korean domestic nuclear power plants. The cultural characteristics of the operating teams were visualized using five Hofstede's cultural indices and compared with the number of unsafe acts observed from simulated off-normal conditions. As a result, a statistically significant correlation is found between the occurrence of unsafe acts and one of the Hofstede's indices. From this finding, it is expected that a relevant probe to scrutinize crew-to-crew variability could be soundly determined in future works

Application of Artificial Intelligence to the Source Term Database: Clustering of Accident Scenarios and Prediction of Offsite Consequences

The level 2 probabilistic safety assessment (PSA) is an important risk analysis method of nuclear power plants (NPPs) for estimating large early release frequency (LERF) and the amount of radioactive material release from postulated accidents as the safety goal requirement of the Republic of Korea (RoK). Previously, only dominant accident scenarios among large accident scenarios were selected as representatives for source term estimation using logical tree analysis or expert judgement. Consequently, it is difficult that these source term results from representative scenarios would cover the risk insight of all scenarios in level 2 PSA. To increase the significant imbalance between the number of scenarios in the risk assessment, the Korea Atomic Energy Research Institute (KAERI) proposed an approach called exhaustive simulation to mechanically construct an enormous database of source terms for a large number of severe accident scenarios. This approach allows us to address some legacy issues not previously considered in the existing level 2 PSA. To effectively utilize this database and support the PSAs, KAERI is currently developing a framework based on artificial intelligence (AI) technology. In this paper, the authors introduce two studies of AI applications using this database. The first study quantitatively categorizes severe accident scenarios to select the best representative one using a clustering method after extracting a feature through an autoencoder for the performance of the clustering. It turned out that this approach can reduce the uncertainties in selecting representative scenarios compared to the qualitative logical tree or expert judgement. The second study develops an accident consequence prediction model using source term database with a convolutional neural network to provide appropriate information for quick and better decision-making during an actual accident. The prediction model successfully provides expected accident consequences when various severe accident conditions are given. These studies were performed based on the database of about 650 severe accident scenarios, including the results of source term and accident consequence analysi