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[en] The research at this point can be divided into three streams, focused on the development of self powered sensors and instrumentation, developing intelligent systems that can diagnose and accident type and developing indirect ways that is, methods to assess the safety critical parameters from other statistically related parameters. This first approach is quite expensive, second approach suffers from the limitation that infinite number of accident scenarios cannot be simulated. However, the only way to access the parameters during severe accidents is through simulation codes. Even-though, the process parameters data contain uncertainty, this is the only thing to start with severe accident management. International Nuclear Energy Research Initiative (Inert) project has started research to address various aspects of safety management during severe accidents. As a part of Inert team, we are investigating correlations among process parameters in such a way that safety critical information could be secured by means of other non-safety or virtual parameters during a severe accident. This is known as virtual redundancy of information. This will improve the availability of information in case one channel for information is lost. In this paper, we will discuss methodology, preliminary results and directions for further study. We found that several process parameters exhibit distinct variation pattern for a particular accident and several other parameters can also have the similar trends which strengthens the possibility of having virtual redundancy of information
[en] When a severe accident happens, it is hard to obtain the necessary information to understand of internal status because of the failure or damage of instrumentation and control systems. We learned the lessons from Fukushima accident that internal instrumentation system should be secured and must have ability to react in serious conditions. While there might be a number of methods to reinforce the integrity of instrumentation systems, we focused on the use of redundant behavior of plant parameters without additional hardware installation. Specifically, the objective of this study is to estimate the replaced value which is able to identify internal status by using set of available signals when it is impossible to use instrumentation information in a severe accident, which is the continuation of the paper which was submitted at the last KNS meeting. The concept of the VPN was suggested to improve the quality of parameters particularly to be logged during severe accidents in NPPs using a software based approach, and quantize the importance of each parameter for further maintenance. In the future, we will continue to perform the same analysis to other accident scenarios and extend the spectrum of initial conditions so that we are able to get more sets of VPNs and ANN models to predict the behavior of accident scenarios. The suggested method has the uncertainty underlain in the analysis code for severe accidents. However, In case of failure to the safety critical instrumentation, the information from the VPN would be available to carry out safety management operation
[en] Highlights: •We identify unreliable instrumentation and provide alternative signals. •Proposed the Empirical Parameter Network based on statistically related parameters. •Connectivity determines which parameters are most important from an I&C perspective. •Proposed method was demonstrated through various SBLOCA scenarios. -- Abstract: During the Fukushima Daiichi nuclear accident, the plant operators' ability to observe the status of the power plant using the instrumentation and control (I&C) system was severely hampered by breakdowns in the plant's network, caused by the earthquake and tsunami. Thus it was difficult to obtain essential information for monitoring the internal situation of the power plant. Also, missing and incorrect information on status caused confusion, which then led to an accident. Clearly, it is crucial that I&C systems maintain the ability to monitor the internal state of reactors, even in such an inferior working environment. Herein we propose a method to identify unreliable instrumentation and to provide alternative signals. Our method, called the Empirical Parameter Network (EPN), provides estimates to replace faulty information based on statistically related parameters, and includes visualizations and other tools to enable recognition of various scenarios. The EPN included essential parameters that were selected on the basis of a literature survey, and was based on statistical analysis of an array of simulated post-accident data. The behavior of each parameter was identified and a data visualization technique was developed to intuitively display parameter correlation information. Connectivity analysis to reveal associations was performed based on the data visualization results. By incorporating the concept of connectivity, we were able to determine which parameters were most important from an I&C perspective, allowing further construction of the EPN. This newly constructed EPN will propose an alternative signal when an incorrect input signal is generated, or even when a specific parameter is missing altogether. The proposed method was demonstrated through various scenarios originating from an initial SBLOCA event, which is considered to be one of the greatest contributors to overall severe accident risk. In this research, the relationships between parameters were confirmed based on analysis of connectivity during an accident. Overall, in the damaged network condition, the reliability of the monitoring system can be improved by using the relationships between the parameters. This research can be helpful in managing accidents.