• Nuclear Engineering and Technology
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• v.20 no.1
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• pp.54-64
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• 1988

This paper assesses the models in the SCDAP code using the results of the FLHT-2 test. Calculations show that the SCDAP correctly predicts Ire temperatures, oxidation front movement, overall hydrogen generation and peak generation rate, internal fuel rod pressure, and cladding rupture due to ballooning. A comparison of the calculated results with measured data shows that two phase level is underpredicted, and that radiation heat transfer and auto-catalytic reaction temperature of zircaloy are overpredicted. These models are recommended to be modified. The analysis also shows that the simulation of the gap in a fuel rod improves the code prediction on core damage progression.

• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.921-928
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• 2009

As part of an evaluation for an in-vessel severe accident management strategy, a coolant injection into the reactor vessel under depressurization of the reactor coolant system (RCS) has been evaluated in detail using the SCDAP/RELAP5 computer code. A high-pressure sequence of a small break loss of coolant accident (SBLOCA) has been analyzed in the Optimized Power Reactor (OPR) 1000. The SCDAP/RELAP5 results have shown that safety injection timing and capacity with RCS depressurization timing and capacity are very effective on the reactor vessel failure during a severe accident. Only one train operation of the high pressure safety injection (HPSI) for 30,000 seconds with RCS depressurization prevents failure of the reactor vessel. In this case, the operation of only the low pressure safety injection (LPSI) without a HPSI does not prevent failure of the reactor vessel.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.463-464
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• 2004

• Nuclear Engineering and Technology
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• v.48 no.4
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• pp.975-981
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• 2016

The containment response during the first 24 hours of a low-pressure severe accident scenario in a nuclear power plant with a two-loop Westinghouse-type pressurized water reactor was simulated with the CONTAIN 2.0 computer code. The accident considered in this study is a large-break loss-of-coolant accident, which is not successfully mitigated by the action of safety systems. The analysis includes pressure and temperature responses, as well as investigation into the influence of spray on the retention of fission products and the prevention of hydrogen combustion in the containment.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.806-811
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• 1998

핵연료 피복관의 산화반응 현상은 중대사고시 원자로와 격납건물의 건전성을 위협하는 중요한 원인중의 하나이다 본 논문에서는 MELCOR에서 사용증인 Urbanic-Heidrich 상관식과 SCDAP/RELAP5/MOD3.1에서 사용중인 MATPRO-EG&G 상관식을 사용하여 산화 반응 모델이 노심손상에 미치는 영향을 울진원전3,4호기를 대상으로 MELCOR의 입력변수의 변화에 따른 민감도를 분석하였다. 분석결과, Urbanic-Heidrich 상관식이 MATPRO-EG&G상관식에 비해 핵연료 용융시작을 약 394초, 원자로 노심 하부에서의 용융물 재배치 (relocation)시작을 약 434초 가량 빨리 초래하여 사고진행에는 큰영향이 없음을 나타내고 있으나 노심하부 파손시점까지 발생한 수소량은 Urbanic-Heidrich 상관식이 MATPRO-EG&G상관식에 비해 약 1.4배정도 더 많이 발생시켜 격납건물 건전성에 대한 영향이 매우 크므로 보다 자세한 모델검토가 요구된다.

• Nuclear Engineering and Technology
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• v.47 no.4
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• pp.434-442
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• 2015

The advanced passive pressurized water reactor (PWR) is being constructed in China and the passive residual heat removal (PRHR) system was designed to remove the decay heat. During accident scenarios with increase of heat removal from the primary coolant system, the actuation of the PRHR will enhance the cooldown of the primary coolant system. There is a risk of power excursion during the cooldown of the primary coolant system. Therefore, it is necessary to analyze the thermal hydraulic behavior of the reactor coolant system (RCS) at this condition. The advanced passive PWR model, including major components in the RCS, is built by SCDAP/RELAP5 code. The thermal hydraulic behavior of the core is studied for two typical accident sequences with PRHR actuation to investigate the core cooling capability with conservative assumptions, a main steam line break (MSLB) event and inadvertent opening of a steam generator (SG) safety valve event. The results show that the core is ultimately shut down by the boric acid solution delivered by Core Makeup Tank (CMT) injections. The effects of CMT boric acid concentration and the activation delay time on accident consequences are analyzed for MSLB, which shows that there is no consequential damage to the fuel or reactor coolant system in the selected conditions.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.772-777
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• 1997

Previous MELCOR and SCDAP/RELAP5 nodalizations for simulating the counter-current, natural circulation behavior of vapor flow within the RCS hot legs and SG U-tubes when core damage progress can not be applied to the steady state and water-filled conditions during the initial period of accident progression because of the artificially high loss coefficients in the hot legs and SG U-tubes which were chosen from results of COMMIX calculation and the Westinghouse natural circulation experiments in a 1/7-scale facility for simulating steam natural circulation behavior in the vessel and in the hot leg and SG during the TMLB' scenrio. The objective of this study is to develop a natural circulation modeling which can be used both for the liquid flow condition at steady state and for the vapor flow condition at the later period of in-vessel core damage. For this, the drag forces resulting from the momentum exchange effects between the two vapor streams in the hot leg was modeled as a pressure drop by pump model. This hot leg natural circulation modeling of MELCOR was able to reproduce similar mass flow rates with those predicted by previous models.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.807-816
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• 2012

The Three Mile Island Unit 2 (TMI-2) accident has been studied extensively, as part of both post-accident technical assessment and follow-up computer code calculations. The models used in computer codes for severe accidents have improved significantly over the years due to better understanding. It was decided to reanalyze the severe accident scenario using current state of the art codes and methodologies. This reanalysis was adopted as a part of the joint standard problem exercise for the Atomic Energy Regulatory Board (AERB) - United States Regulatory Commission (USNRC) bilateral safety meet. The accident scenario was divided into four phases for analysis viz., Phase 1 covers from the accident initiation to the shutdown of the last Reactor Coolant Pumps (RCPs) (0 to 100 min), Phase 2 covers initial fuel heat up and core degradation (100 to 174 min), Phase 3 is the period of recovery of the core water level by operating the reactor coolant pump, and the core reheat that followed (174 to 200 min) and Phase 4 covers refilling of the core by high pressure injection (200 to 300 min). The base case analysis was carried out for all four phases. The majority of the predicted parameters are in good agreement with the observed data. However, some parameters have significant deviations compared to the observed data. These discrepancies have arisen from uncertainties in boundary conditions, such as makeup flow, flow during the RCP 2B transient (Phase 3), models used in the code, the adopted nodalisation schemes, etc. In view of this, uncertainty and sensitivity analyses are carried out using simulation based techniques. The paper deals with uncertainty and sensitivity analyses carried out for the first three phases of the accident scenario.

• 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.