• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.532-541
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• 2018

Decision support systems are a key focus of research on developing control rooms to aid operators in making reliable decisions and reducing incidents caused by human errors. For this purpose, models of complex systems can be developed to diagnose causes or consequences for specific alarms. Models applied in safety systems of complex and safety-critical systems require rigorous and reliable model building and testing. Multilevel flow modeling is a qualitative and discrete method for diagnosing faults and has previously only been validated by subjective and qualitative means. To ensure reliability during operation, this work aims to synthesize a procedure to measure model performance according to diagnostic requirements. A simple procedure is proposed for validating and evaluating the concept of multilevel flow modeling. For this purpose, expert statements, dynamic process simulations, and pilot plant experiments are used for validation of simple multilevel flow modeling models of a hydrocyclone unit for oil removal from produced water.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.553-561
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• 2018

To easily understand and systematically express the behaviors of the industrial systems, various system modeling techniques have been developed. Particularly, the importance of system modeling has been greatly emphasized in recent years since modern industrial systems have become larger and more complex. Multilevel flow modeling (MFM) is one of the qualitative modeling techniques, applied for the representation and reasoning of target system characteristics and phenomena. MFM can be applied to industrial systems without additional domain-specific assumptions or detailed knowledge, and qualitative reasoning regarding event causes and consequences can be conducted with high speed and fidelity. However, current MFM techniques have a limitation, i.e., the dynamic features of a target system are not considered because time-related concepts are not involved. The applicability of MFM has been restricted since time-related information is essential for the modeling of dynamic systems. Specifically, the results from the reasoning processes include relatively less information because they did not utilize time-related data. In this article, the concepts of time-to-detect and time-to-effect were adopted from the system failure model to incorporate time-related issues into MFM, and a methodology for enhancing MFM-based reasoning with time-series data was suggested.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.542-552
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• 2018

Operators face challenges to plan alternative countermeasures when no procedure exists to address the current plant state. A model-based approach is desired to aid operators in acquiring plant resources and deriving response plans. Multilevel flow modeling (MFM) is a functional modeling methodology that can represent intentional knowledge about systems, which is essential in response planning. This article investigates the capabilities of MFM to plan alternatives. It is concluded that MFM has a knowledge capability to represent alternative means that are designed for given ends and a reasoning capability to identify alternative functions that can causally influence the goal achievement. The second capability can be applied to find originally unassociated means to achieve a goal. This is vital in a situation where all designed means have failed. A technique of procedure synthesis can be used to express identified alternatives as a series of operations. A case of station blackout occurring at the boiling water reactor is described. An MFM model of a boiling water reactor is built according to the analysis of goals and functions. The accident situations are defined by the model, and several alternative countermeasures in terms of operating procedures are generated to achieve the goal of core cooling.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.340-345
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• 1996

Because of limited resources of time and information processing capability during abnormal situation, diagnosis is difficult tasks in nuclear power plant (NPP) operators. Moreover since minimizing of adverse consequences according to process abnormalities is vital for the safety of NPP, introducing of diagnosis support systems have particularly emphasized. However, considerable works to develop effective diagnostic support system are not sufficiently fulfilled because of the complexity of NPP is one of the major problems. To cope with this complexity, a lot of model-based diagnosis support systems have considered and implemented worldwide. In this paper, as a prior step to development of model-based diagnosis support systems, primary side of pressurized water reactor is functionally modeled by multilevel flow modeling (MFM) concept. MFM is suitable for complex system modeling and for diagnosis of abnormalities. Furthermore, knowledge-based diagnosis process, of NPP operator could be supported because this diagnosis strategy can represent operator's one.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2453-2466
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• 2022

Nuclear power plant operators in the main control room are exposed to stressful conditions in emergency situations as immediate and appropriate mitigations are required. While emergency operating procedures (EOPs) provide operators with the appropriate tasks and diagnostic guidelines, EOPs have static properties that make it difficult to reflect the dynamic changes of the plant. Due to this static nature, operator workloads increase because unrelated information must be screened out and numerous displays must be checked to obtain the plant status. Generally, excessive workloads should be reduced because they can lead to human errors that may adversely affect nuclear power plant safety. This paper presents a framework for an operator support system that can substitute the initial responses of the EOPs, or in other words the immediate actions and diagnostic procedures, in the early stages of an emergency. The system assists operators in emergency operations as follows: performing the monitoring tasks in parallel, identifying current risk and latent risk causality, diagnosing the accident, and displaying all information intuitively with a master logic diagram. The risk causalities are analyzed with a functional modeling methodology called multilevel flow modeling. This system is expected to reduce workloads and the time for performing initial emergency response procedures.

• Nuclear Engineering and Technology
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• v.37 no.1
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• pp.118-126
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• 2005

While emergency operating procedures (EOPs) occupy an important role in the management of various abnormal situations in nuclear power plants (NPPs), current technology for the validation of EOPs still largely depends on manual review. A validation method for EOPs of NPPs is thus proposed based on dynamic multi-level flow modeling (MFM). The MFM modeling procedure and the EOP validation procedure are developed and provided in the paper. Application of the proposed method to EOPs of an actual NPP shows that the proposed method provides an efficient means of validating EOPs. It is also found that the information on state transitions in MFM models during the management of abnormal situations is also useful for further analysis on EOPs including their optimization.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4408-4425
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• 2023

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.