• Plant Journal
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• v.8 no.3
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• pp.42-48
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• 2012

Under traditional analysis of the capital budgeting, NPV, it is assumed that management cannot react to deviation from the expected scenario of cash flow at the time of evaluation. In practice, however, it is less likely that the expected scenario will come true when new information arrives and uncertainty is resolved. Uncertainty and risk can be influenced through 'managerial flexibility', which becomes a central instrument for value creation. Real option framework including option to defer, option for staged investment, option to alter, option to abandon, option to switch, etc. takes this managerial flexibility into account. Therefore, it is more appropriate to use real option method to evaluate the project than the traditional DCF(discounted cash flow) tool if the firm has high volatility of the expected returns.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.32-45
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• 2023

Implementing Severe Accident Management (SAM) strategies is crucial for enhancing a nuclear power plant's resilience and safety against severe accidents conditions represented in the analysis of Station Blackout (SBO) event. Among these critical approaches, the In-Vessel Retention (IVR) through External Reactor Vessel Cooling (IVR-ERVC) strategy plays a key role in preventing vessel failure. This work is designed to evaluate the efficacy of the IVR strategy for a high-power density reactor APR1400. The APR1400's plant is represented and simulated under steady-state and transient conditions for a station blackout (SBO) accident scenario using the computer code, ASYST. The APR1400's thermal-hydraulic response is analyzed to assess its performance as it progresses toward a severe accident scenario during an extended SBO. The effectiveness of emergency operating procedures (EOPs) and severe accident management guidelines (SAMGs) are systematically examined to assess their ability to mitigate the accident. A group of associated key phenomena selected based on Phenomenon Identification and Ranking Tables (PIRT) and uncertain parameters are identified accordingly and then propagated within DAKOTA Uncertainty Quantification (UQ) framework until a statistically representative sample is obtained and hence determine the uncertainty bands of key system parameters. The Systems Engineering methodology is applied to direct the progression of work, ensuring systematic and efficient execution.

• Nuclear Engineering and Technology
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• v.23 no.3
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• pp.285-298
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• 1991

An example application of the fuzzy set theory is first made to a simple portion of a given accident progression event tree with typical qualitative fuzzy input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated with example applications. Then the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e., a typical plant damage state‘SEC’of the Zion Nuclear Power Plant risk assessment. The results show that the fuzzy averages of the fuzzy outcomes are very close to the mean values obtained by current methods. The main purpose of this paper is to provide a formal procedure for application of the fuzzy set theory to accident progression event trees with imprecise and qualitative branch probabilities and/or with a number of phenomenological uncertainty issues.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.562-571
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• 2003

A series of factors such as sampling, pretreatment measurement, volume estimation which induces uncertainty of the calculated radioactivity in liquid effluent released from a nuclear power plant were analyzed. It is innately impossible to estimate exact error of the calculated radioactivity, since most of the input parameters are determined by a single measurement and true value of the released radioactivity cannot be known. In this paper, a systematic model to calculate uncertainty of the released liquid radioactivity was developed based upon the guidance report published by the ISO in 1993, and the model was applied to a set of hypothetical batch release conditions. As a result, the Priority of each input parameter was turned out to be (1) wastewater volume, (2) sample volume, and (3) measured radioactivity of the sample. In addition, probability distribution of the released radioactivity was simulated by Monte Carlo method combining the probability distribution of each input parameter It was shown that the radioactivity released to the environment, which has been reported as a single value, has a certain form of probability distribution.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Nuclear Engineering and Technology
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• v.45 no.6
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• pp.767-790
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• 2013

MAAP4 is a computer code that can simulate the response of a light water reactor power plant during severe accident sequences, including actions taken as part of accident management. The code quantitatively predicts the evolution of a severe accident starting from full power conditions given a set of system faults and initiating events through events such as core melt, reactor vessel failure, and containment failure. Furthermore, models are included in the code to represent the actions that could mitigate the accident by in-vessel cooling, external cooling of the reactor pressure vessel, or cooling the debris in containment. A key element tied to using a code like MAAP4 is an uncertainty analysis. The purpose of this paper is to present a MAAP4 based analysis to examine the sensitivity of a key parameter, in this case hydrogen production, to a set of model parameters that are related to a Level 2 PRA analysis. The Level 2 analysis examines those sequences that result in core melting and subsequent reactor pressure vessel failure and its impact on the containment. This paper identifies individual contributors and MAAP4 model parameters that statistically influence hydrogen production. Hydrogen generation was chosen because of its direct relationship to oxidation. With greater oxidation, more heat is added to the core region and relocation (core slump) should occur faster. This, in theory, would lead to shorter failure times and subsequent "hotter" debris pool on the containment floor.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.323-338
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• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.

• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.273-278
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• 2005

Key concepts in South Korean nuclear safety regulation are safety and risk. Nuclear regulation in South Korea has required reactor designs and safeguards that reduce the risk of a major accident to less than one in a million reactor-years-a risk supposedly low enough to be acceptable. To date, in South Korean nuclear safety regulation has involved the establishment of many technical standards to enable administration enforcement. In scientific lawsuits in which the legal issue is the validity of specialized technical standards that are used for judge whether a particular nuclear power plant is to be licensed, the concept of uncertainty law is often raised with regard to what extent the examination and judgment by the judicial power affects a discretion made by the administrative office. In other words, the safety standards for nuclear power plants has been adapted as a form of the scientific technical standards widely under the idea of uncertainty law. Thus, the improvement of nuclear safety regulation in South Korea seems to depend on the rational lawmaking and a reasonable, judicial examination of the scientific standards on nuclear safety.

• The KSFM Journal of Fluid Machinery
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• v.7 no.5 s.26
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• pp.29-35
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• 2004

The turbine efficiency is an important factor in power plant, and accurate evaluation of steam turbine performance is the key issue in turbo machinery industry. The difficulty of evaluating the steam turbine performance due to its high steam temperature and pressure environment makes the most steam turbine tests to be replaced by air similarity test. This paper presents how to decide the similarity conditions of the steam turbine test and describes its limitations and assumptions. The test facility was developed and arranged to conduct an air similarity turbine performance test with various inlet pressure, temperature and mass flow rate. The eddy-current type dynamometer measures the turbine-generated shaft power and controls the rotating speed. Pressure ratio of turbine can be controled by back pressure control valve. To verify its test results, uncertainty analysis was performed and relative uncertainty of turbine efficiency was obtained.