Gyunyoung Heo

Development of a cyber security risk model using Bayesian networks

Cyber security is an emerging safety issue in the nuclear industry, especially in the instrumentation and control (I&C) field. To address the cyber security issue systematically, a model that can be used for cyber security evaluation is required. In this work, a cyber security risk model based on a Bayesian network is suggested for evaluating cyber security for nuclear facilities in an integrated manner. The suggested model enables the evaluation of both the procedural and technical aspects of cyber security, which are related to compliance with regulatory guides and system architectures, respectively. The activity-quality analysis model was developed to evaluate how well people and/or organizations comply with the regulatory guidance associated with cyber security. The architecture analysis model was created to evaluate vulnerabilities and mitigation measures with respect to their effect on cyber security. The two models are integrated into a single model, which is called the cyber security risk model, so that cyber security can be evaluated from procedural and technical viewpoints at the same time. The model was applied to evaluate the cyber security risk of the reactor protection system (RPS) of a research reactor and to demonstrate its usefulness and feasibility.

A framework for evaluating the effects of maintenance-related human errors in nuclear power plants

This paper proposes a framework for estimating the qualitative and quantitative consequences of human errors that occur during maintenance tasks involving the balance of plant in nuclear power plants. One of the remedies for unexpected reactor shutdowns may be a methodical tool designed to warn potential hazards arising from given maintenance tasks, taking into account human error modes in a proactive manner, in terms of risk and/or loss of electrical power. The entire framework that we established is composed of four components: (1) the human-error analyzer to connect possible failure modes resulting from human errors with other estimators, (2) the frequency estimator to quantify the occurrence of maintenance-related failure modes, (3) the risk estimator to determine minimal cutsets and to compute the variation of the core damage frequency using the fault tree analysis and turbine cycle simulation, and (4) the derate estimator to determine the electrical power loss under abnormal plant configurations caused by human error. The final result is characterized by a cost metric that can be used for decision-making possibly resulting in revisions of procedures, or task modifications. This paper also discusses case studies to illustrate the feasibility of the proposed framework.

AN AXIOMATIC DESIGN APPROACH OF NANOFLUID- ENGINEERED NUCLEAR SAFETY FEATURES FOR GENERATION III+ REACTORS

A variety of Generation III/III+ reactor designs featuring enhanced safety and improved economics are being proposed by nuclear power industries around the world to solve the future energy supply shortfall. Nanofluid coolants showing an improved thermal performance are being considered as a new key technology to secure nuclear safety and economics. However, it should be noted that there is a lack of comprehensible design works to apply nanofluids to Generation III+ reactor designs. In this work, the review of accident scenarios that consider expected nanofluid mechanisms is carried out to seek detailed application spots. The Axiomatic Design (AD) theory is then applied to systemize the design of nanofluid-engineered nuclear safety systems such as Emergency Core Cooling System (ECCS) and External Reactor Vessel Cooling System (ERVCS). The various couplings between Gen-III/III+ nuclear safety features and nanofluids are investigated and they try to be reduced from the perspective of the AD in terms of prevention/mitigation of severe accidents. This study contributes to the establishment of a standard communication protocol in the design of nanofluid-engineered nuclear safety systems.

Design of safety-critical systems using the complementarities of success and failure domains with a case study

Abstract A safety-critical system has to qualify the performance-related requirements and the safety-related requirements simultaneously. Conceptually, design processes should consider both of them simultaneously but the practices do not and/or cannot follow such a theoretical approach due to the limitation of design resources. From our experience, we found that safety-related functions must be simultaneously resolved with the development of performance-related functions, particularly, in case of safety-critical systems. Since, success and failure domain analyses are essential for the investigation of performance-related and safety-related requirements, respectively, we articulated our perception to Axiomatic Design (AD), Fault Tree Analysis (FTA), and TRIZ. A design evolution procedure considering feedbacks from AD to identify functional couplings, TRIZ methodology to explore uncoupling solutions and FTA to improve reliability in a systematic way is presented here. A case study regarding design of safety injection tank installed in a nuclear power plant is also included to illustrate the proposed framework. It is expected that several iterations between AD–TRIZ–FTA would result into an optimized design which could be tested against the desired performance and safety criteria.

Internal leakage detection for feedwater heaters in power plants using neural networks

As interest in safety and performance of power plants becomes more serious and wide-ranging, the significance of research on turbine cycles has attracted more attention. This paper particularly focuses on thermal performance analysis under the conditions of internal leakages inside closed-type feedwater heaters (FWHs) and their diagnosis to identify the locations and to quantify leak rates. Internal leakage is regarded as flow movement through the isolated path but remaining inside the system boundary of a turbine cycle. For instance, leakages through the cracked tubes, tube-sheets, or pass partition plates in a FWH are internal leakages. Internal leakages impact not only plant efficiency, but also direct costs and/or even plant safety associated with the appropriate repairs. Some types of internal leakages are usually critical to get the parts fixed and back in a timely manner. The FWHs installed in a Korean standard nuclear power plant were investigated in this study. Three technical steps have been, then, conducted: (1) the detailed modeling of FWHs covering the leakage from tubes, tube-sheets, or pass partition plates using the simulation model, (2) thermal performance analysis under various leakage conditions, and (3) the development of a diagnosis model using a feed-forward neural network, which is the correlation between thermal performance indices and leakage conditions. Since the operational characteristics of FWHs are coupled with one another and/or with other neighbor components such as turbines or condensers, recognizing internal leakages is difficult with only an analytical model and instrumentation at the inlet and outlet of tube- and shell-sides. The proposed neural network-based correlation was successfully validated for test cases.

Detection of process anomalies using an improved statistical learning framework

Maintenance technologies have been progressed from a time-based to a condition-based manner. The fundamental idea of condition-based maintenance (CBM) is built on the real-time diagnosis of impending failures and/or the prognosis of residual lifetime of equipment by monitoring health conditions using various sensors. The success of CBM, therefore, hinges on the capability to develop accurate diagnosis/prognosis models. Even though there may be an unlimited number of methods to implement models, the models can normally be classified into two categories in terms of their origins: using physical principles or historical observations. We have focused on the latter method (sometimes referred as the empirical model based on statistical learning) because of some practical benefits such as context-free applicability, configuration flexibility, and customization adaptability. While several pilot-scale systems using empirical models have been applied to work sites in Korea, it should be noted that these do not seem to be generally competitive against conventional physical models. As a result of investigating the bottlenecks of previous attempts, we have recognized the need for a novel strategy for grouping correlated variables such that an empirical model can accept not only statistical correlation but also some extent of physical knowledge of a system. Detailed examples of problems are as follows: (1) missing of important signals in a group caused by the lack of observations, (2) problems of signals with the time delay, and (3) problems of optimal kernel bandwidth. This paper presents an improved statistical learning framework including the proposed strategy and case studies illustrating the performance of the method.

Fusion DEMO Program of Korea: Overview and DEMO R&D Plans

The Fusion DEMO Program of Korea is a mega program consisted of phased three programs: DEMO Preparatory Program from 2009 through 2011, DEMO R&D Program from 2012 through 2021 and DEMO Construction Program from 2022 through the 2036. The DEMO R&D Program is further divided into three sub-programs: DEMO-Plant Design-Concept Study from 2012 through 2014, DEMO-Plant Design Study from 2015 through 2018 and DEMO Plant FEED (Front End Engineering Design) from 2019 through 2021 at the same time with initiating validation tests for the design methods. Until 2011, preparatory works for DEMO, such as developing the strategic plans, defining a pathway to DEMO and initiating some R&D works necessarily required for defining the pathway, will have been carried out. In this paper, the R&D activities planned in the 2nd phase sub-program, with an overview of the strategic plans and preparatory works of the Fusion DEMO Program of Korea, are discussed.

APPLICATION OF MONITORING, DIAGNOSIS, AND PROGNOSIS IN THERMAL PERFORMANCE ANALYSIS FOR NUCLEAR POWER PLANTS

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decisionmaking about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

ADVANCED MMIS TOWARD SUBSTANTIAL REDUCTION IN HUMAN ERRORS IN NPPS

This paper aims to give an overview of the methods to inherently prevent human errors and to effectively mitigate the consequences of such errors by securing defense-in-depth during plant management through the advanced man-machine interface system (MMIS). It is needless to stress the significance of human error reduction during an accident in nuclear power plants (NPPs). Unexpected shutdowns caused by human errors not only threaten nuclear safety but also make public acceptance of nuclear power extremely lower. We have to recognize there must be the possibility of human errors occurring since humans are not essentially perfect particularly under stressful conditions. However, we have the opportunity to improve such a situation through advanced information and communication technologies on the basis of lessons learned from our experiences. As important lessons, authors explained key issues associated with automation, man-machine interface, operator support systems, and procedures. Upon this investigation, we outlined the concept and technical factors to develop advanced automation, operation and maintenance support systems, and computer-based procedures using wired/wireless technology. It should be noted that the ultimate responsibility of nuclear safety obviously belongs to humans not to machines. Therefore, safety culture including education and training, which is a kind of organizational factor, should be emphasized as well. In regard to safety culture for human error reduction, several issues that we are facing these days were described. We expect the ideas of the advanced MMIS proposed in this paper to lead in the future direction of related researches and finally supplement the safety of NPPs.

Study on Conceptual Design and Technical Safety Issues for Korean Demonstration Fusion Reactors

Abstract This paper surveyed the safety issues and the related engineered safety features for designing Korean demonstration fusion power plant. Since the design process was staying at a conceptual stage and regulatory requirements were not fully matured, it was significant to investigate the broad options and select feasible candidates. In order to straddle system’s performance and risk, the study followed the principles of Axiomatic Design (AD) and Fault Tree Analysis (FTA). The interplay of AD and FTA facilitates developing the design of fusion power plants for enhancing performance (power generation) and reducing risk (radiation hazard). While AD is a synthesis process in the success domain to compromise functional requirements and design options in terms of a functional hierarchy tree, FTA considers a safety analysis process in the failure domain. The functional hierarchy tree, which is also named as a functional requirement and design parameter tree, showed the entire fusion power plant with multiple design candidates in a hierarchic manner. This tree can be transformed into a fault tree. While developing the fault tree, the list of DBAs which are the failure modes for the leaves of the fault tree could be recognized, and the associated engineered safety features were proposed depending on the consequences of a DBA. As a demonstration for analyzing a DBA, the mass and energy release calculation for in-vessel loss of coolant accident was described.