Modular Accident Analysis Program Research Articles

Analysis of AP1000 Small-Break Loss-of-Coolant Accident Using Reactor Transient Simulator

The Westinghouse Electric Company’s Advanced Passive Reactor (AP1000) is characterized by the incorporation of passive safety systems (PSSs) designed to ensure core cooling during transient events. The assessment of PSSs requires evaluation of their performance through a combination of experiments and simulations employing various thermal-hydraulic codes. In addition, detailed evaluation of PSSs for a specific reactor system transient analysis such as loss-of-coolant-accident analysis supports understanding representative integral effects test facility development and the further evolution model development and assessment process. Developing a reactor system code is a complex and time-consuming process that requires significant engineering expertise and effort. It can take several months to even years to complete in the early stages of reactor system design and analysis. However, this process can be expedited through the use of transient simulator models for similar reactor systems, which can be used for lesson learning and training purposes. This study uses the Personal Computer Transient Analyzer (PCTRAN) code. The main advantage of PCTRAN is its ease of use and ability to run faster than real time. This study presents the results obtained for a small-break loss-of-coolant accident (SBLOCA) for two breaks using the full version (licensed) of PCTRAN. The purpose of this investigation is to evaluate the overall system behavior during the postulated SBLOCA event as well as assess the capability of the PCTRAN code to reproduce the system response during transient events. The obtained results were compared with the Westinghouse NOTRUMP system code. The PCTRAN code proved to be reliable in predicting the qualitative behavior of the system in both transient cases. As for the system response, it was found that it is contingent on the activation time of the PSSs. The differences in reactor coolant system pressure between the two codes were attributed to the critical flow model and simplification of mass and energy balance. Despite PCTRAN’s limitations, it can still provide a reasonable prediction of various reactor parameters such as pressure, mass flow rate, and void fraction during a SBLOCA scenario. It is worth noting that PCTRAN currently employs a bulk approach similar to that of the Modular Accident Analysis Program (MAAP) and MELCOR codes. However, the upcoming version of PCTRAN will include an artificial intelligence–based detection and accident prevention system, as well as different models for different reactor components. Consequently, PCTRAN has the potential to be upgraded to match the system thermal-hydraulic codes of the U.S. Nuclear Regulatory Commission and become more widely used in cybersecurity to safeguard nuclear power plants from cyberattacks.

Validation of EPG/SAG Rev. 4 of the Kuosheng Nuclear Power Plant Using the MAAP5 Code

The Boiling Water Reactor Owner’s Group released Emergency Procedure Guidelines and Severe Accident Guidelines Revision 4 (EPG/SAG Rev. 4) in 2018. The major improvement to EPG/SAG Rev. 4 was Contingency 1 (Alternate Level/ Pressure Control). Contingency 1 coordinates the reactor pressure vessel (RPV) water level and RPV pressure control action to prolong the availability of steam-driven injections and optimize the transfer to motor-driven systems. In this study, the effectiveness of the EPG/SAG Rev. 4 Contingency 1 strategy was compared with those of EPG/SAG Revision 2 Contingency 1 and Specific Major Incident Guidelines (SMI) using the Modular Accident Analysis Program, Version 5 (MAAP5). SMI was developed by the Taiwan Power Company to mitigate a Fukushima-like accident. The surrogate plant that analyzed is the Kuosheng Nuclear Power Plant (NPP). The Kuosheng NPP is BWR-6 Mark-III containment. MAAP5 is an integral severe accident analysis program that simulates the responses of a light water reactor power plant during a severe accident. This program has been used extensively for probabilistic risk assessments and for verification and validation of mitigation actions specified in severe accident management guidelines. The simulation scenarios were extended loss of alternative-current power and loss of ultimate heat sink. The low-capacity, motor-driven portable pump was the only available system for RPV injection in the first hour of the accident. In this time period, the RPV water level and pressure were controlled by reactor core isolation cooling and safety relief valves. After this study, the strategy of EPG/SAG Rev. 4 Contingency 1 was successfully validated, and the effectiveness of minimum pre-depressurization RPV water level and the low-capacity, motor-driven portable pump were also demonstrated in this study.

Pearson Correlation and Chi-Squared Methods Applied to the Sensitivity and Uncertainty Analysis of Hydrogen Generation During a Boiling Water Reactor STSBO Using MAAP5 and AZTUSIA

Abstract After the nuclear accident at the Fukushima Daiichi nuclear power plant, hydrogen generation has resurfaced as a key issue, with respect to its quantification in the simulation of severe accidents (SAs), because of the potential risk of deflagration or detonation, which would compromise the integrity of the plant facilities. Severe accident simulation codes may have different models to predict cladding oxidation rates and the consequent hydrogen generation. Thus, sensitivity and uncertainty analysis can be applied to determine which input variables of the code have a significant impact in the implemented models, and consequently on the overall predicted values of key figures of merit. In this work, the generation of hydrogen in the reactor core during a short-term station blackout (STSBO) is studied as a figure of merit for a boiling water reactor (BWR). The scope of the analysis is until the failure of the reactor pressure vessel (RPV). This study shows a sensitivity and uncertainty analysis through the complementary calculations of two methods: Chi-squared parameter and Pearson simple correlation coefficient (PSCC). The severe accident simulation code was MAAP 5.0.3 (Modular Accident Analysis Program, EPRI, USA) and the AZTUSIA code (AZtlan Tool for Uncertainty and SensItivity Analysis, Mexico) was used as the statistical tool for the sensitivity and uncertainty analysis. Ten uncertain parameters were chosen, of which two are MAAP models options, and the values were generated via the simple random Monte Carlo technique. The results show that the Chi-squared parameter in conjunction with correlation coefficients provides a more comprehensive understanding of the impact of both quantitative and qualitative input variables on the figures of merit.

Prediction of small-scale leak flow rate in LOCA situations using bidirectional GRU

It is difficult to detect a small-scale leakage in a nuclear power plant (NPP) quickly and take appropriate action. Delaying these procedures can have adverse effects on NPPs. In this paper, we propose leak flow rate prediction using the bidirectional gated recurrent unit (Bi-GRU) method to detect leakage quickly and accurately in small-scale leakage situations because large-scale leak rates are known to be predicted accurately. The data were acquired by simulating small loss-of-coolant accidents (LOCA) or small-scale leakage situations using the modular accident analysis program (MAAP) code. In addition, to improve prediction performance, data were collected by distinguishing the break sizes in more detail. In addition, the prediction accuracy was improved by performing both LOCA diagnosis and leak flow rate prediction in small LOCA situations. The prediction model developed using the Bi-GRU showed a superior prediction performance compared with other artificial intelligence methods. Accordingly, the accurate and effective prediction model for small-scale leakage situations proposed herein is expected to support operators in decision-making and taking actions.

Failure simulation of nuclear pressure vessel under LBLOCA scenarios

This paper presents the finite element deformation and failure simulation of a typical Korean high-power reactor vessel under a severe accident characterized by large break loss of coolant (LBLOCA) with in-vessel retention of molten corium through external reactor vessel cooling (IVR-ERVC) conditions. Temperature distributions calculated using Modular Accident Analysis Program Version 5 (MAAP5) as thermal boundary conditions were used, and ABAQUS thermal and structural analyses were performed. After full ablation, the temperature of the inner surface in the thinnest section remained high (920 °C), but the stress remained relatively low (less than 6 MPa). At the outer surface, the stress was as high as 250 MPa; however, the resulting plastic strain was small owing to the low temperature of 200 °C. Variations in stress, inelastic strain, and temperature with time in the thinnest section suggest that the plastic and creep strains are saturated owing to stress relaxation, resulting in low cumulative damage. Thus, the lower head of the vessel can maintain its structural integrity under LBLOCA with IVR-ERVC conditions. The sensitivity analysis of internal pressure indicates the occurrence of failure in the thinnest section at an internal pressure >9.6 MPa via local necking followed by failure due to high stresses.

Optimization strategy for SAM in nuclear power plants based on NSGA-II

Abstract The Severe Accident Management Guide (SAMG) is an important component of nuclear safety regulations. Many studies are being conducted to optimize severe accident management (SAM) strategies. To ensure the safety of nuclear power plants, decision makers need to monitor multiple parameters with security threats. Therefore, it is particularly important to search optimal SAM strategies under different numbers of mitigation targets. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is an evolutionary algorithm that does not require derivative differentiation and is capable of population search. In this study, a nuclear power plant accident optimization strategy is developed using the Modular Accident Analysis Program (MAAP) in conjunction with NSGA-II. The strategy enables decision makers to consider multiple mitigation objectives in a complex decision environment. Focusing on the CPR1000, this study applies the optimization strategy to automatically search for optimal mitigation strategies for small break loss of coolant accident (SBLOCA) and station blackout hot leg creep rupture accidents (SBOHLCR). Comparing the optimization results with the basic accident sequence, it is found that the reactor pressure vessel (RPV) failure time is delayed from 72,702 s to 128,730 s under SBLOCA and from 23,828 s to 28,363 s under SBOHLCR. This study has also verified that the optimal SAM strategy obtained by the strategy through dual objective optimization has better mitigation effects than a strategy that only considers one objective. This optimization strategy has the potential to be applied to other types of severe accident management studies in the future.

The effects of cladding thermo-physical and thermo-chemical properties on the coping time during a PWR unmitigated LB-LOCA

To evaluate quantitatively the effects of individual thermo-physical and thermo-chemical cladding properties on the coping time, an unmitigated large break loss of coolant accident (LB-LOCA) was simulated using Modular Accident Analysis Program (MAAP) code. For this purpose, hypothetical claddings were created to decompose the influence of each property enhancement. The variations in the cladding properties could not affect the main conditions of the reactor coolant system such as pressure, reactor core water level, etc., but led significantly different variations in the cladding temperature. When the coping time was defined as the time elapsed between departure from normal operation and the moment at which cladding melts, the specific heat capacity (enthalpy) and melting temperature of cladding materials were identified as properties extending the coping time most. The improved oxidation resistance could extend moderately the coping time. On the other hand, the cladding thermal conductivity had no effect on the coping time.

On-site simulator-based initiating event detection and identification in NPPs

Installing safety components to prevent a severe accident from developing from an initiating event is essential. Although various approaches have been presented to detect and identify initiating events, their development relies on the data simulated by computer codes like the Modular Accident Analysis Program. This study focuses on developing approaches to detect and identify initiating events using the data generated by a nuclear power plant (NPP) on-site simulator. By relying on historical data to construct an empirical model that can predict normal operation sensing readings, an abnormal event that leads to excessive discrepancies between the acquired and predicted readings can be detected. Several single- and multiple-sensor feature extraction methods have been presented to find low-dimensional representations for initiating events to facilitate accurate event identification. The results from detailed experiments containing data of 13 event categories with 185 events generated by the Maanshan NPP plant simulator demonstrated the feasibility of developing on-site simulator-based schemes for detecting and identifying initiating events.

Utilization of Nuklearna Elektrarna Krško Full Scope Simulator for Plant Operation Optimization, Nuclear Education and Engineering in 20 Years

Abstract This article gives an impact analysis of utilization of nuclear power plant full scope simulator on operation parameters, training and education in nuclear power plant Krško (NEK). The Slovenian Nuclear Safety Administration issued their simulator decree to NEK in April 1995. The first training session on the simulator was performed in April 17, 2000 and since then the simulator has been used on daily bases to improve operator knowledges, skills, and performances. At the time, this was the first full scope simulator with the capability to simulate Beyond design basis accidents (severe accidents). The ability to simulate core meltdown and containment breach made it very suitable for emergency preparedness drills. After the 2017 simulator upgrade, fuel meltdown in the spent fuel pool can be simulated using the Modular Accident Analysis Program—MAAP5. This capability is still unique for full scope simulators even today. The simulator is also used for pretesting of plant modifications before their implementation on site or for just-in-time training for infrequent performed evolutions or for procedure development and testing. The Pressurized Water Reactor Owners Group used the NEK simulator in 2018 to develop the new set of the Severe Accident Management Guidelines, incorporated with a completely new usage approach. In all of these years, the simulator has been actively participating in the increased reliability and stability of the electricity production and in achieving NEK's vision to be a worldwide leader in nuclear safety and excellence.

Impact of source term release characteristics of nuclear plant on the performance of EURDEP to identify radioactive plumes

This study aims at analysing the performance of the European Radiological Data Exchange Platform (EURDEP) at identifying, delineating, and tracking different radioactivity clouds moving across a territory. To this purpose, the atmospheric dispersion of 21 hypothetical STs, calculated by the Modular Accident Analysis Program (MAAP) code, has been simulated under the same meteorological scenario by the JAVA-based RODOS decision support system (JRODOS). EURDEP's performance is evaluated using spatial and temporal parameters. The present analysis shows a large impact of the total amount of activity and the timing of the release peak on EURDEP's performance. Monitoring stations are able to detect quite rapidly the release (first alarm), and presents more variation in the time needed to detect the peak, and to delineate and track the plume. A two-dimensional analysis is suggested to illustrate strengths and weaknesses of EURDEP to face different types of STs. These results highlight the need to link the understanding of ST characteristics with meteorological conditions to limit the environmental and health effects associated with radioactive emissions into the atmosphere. The present study aims at presenting a methodology to analyse the performance of EURDEP in other territories.

Study of PWR hot leg creep rupture and RCS depressurization strategy during an SBO accident

Abstract Preventing the leakage of radioactive materials is important to nuclear safety. During a station blackout accident in pressurized water reactors, the hot leg creep rupture caused by hot leg countercurrent flow occurs before the reactor pressure vessel failure that caused by lower head rupture. The secondary fission products barrier is lost after hot leg creep rupture. An analysis for this phenomenon was done using the Modular Accident Analysis Program version 4.0.4 code. A station blackout accident for CPR1000 is simulated and the occurrence and influence of hot leg creep rupture phenomenon are analyzed in detail. After that, a sensitivity analysis of the opening of different pressurizer pilot-operated relief valves at five minutes after entering severe accident management guideline (before the hot leg creep rupture occurs) is studied. The results show that reactor pressure vessel failure time can be extended by at least 4 h if at least one pilot-operated relief valve is opened and direct containment heating phenomenon can be eliminated if at least two pilot-operated relief valves are opened.

Leak flow prediction during loss of coolant accidents using deep fuzzy neural networks

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

MAAP5 simulation of AP1000 severe accident induced by pressurizer safety valve stuck-open

The Modular Accident Analysis Program version 5(MAAP5) is a computer code that can simulate the response of light water reactor power plants during severe accident sequences. The present work aims to simulate the severe accident of AP1000 with the Modular Accident Analysis Program version 5(MAAP5). The pressurizer (PZR) safety valve stuck-open is essentially a small break loss-of-coolant accident which becomes one of the major concerns on severe accident initiating event of pressurized water reactor (PWR). For such improbable accidents, four cases of PZR safety valve stuck-open accidents are simulated for comparison. This analysis is performed at 100% core power with different assumptions. For all cases, the primary system pressure can long term be maintained at a low level, and the ERVC system has enough capability to remove the heat from the molten pool. The total mass of hydrogen production in the core of the cases without auto depressurization system (ADS) is more significant than those cases with ADS. The results are meaningful and significant for comprehending the detail process of AP1000 severe accident, which is the basic standard for establishing the severe accident management guidelines.

Finite Element Model of a Melting Terminal Debris Bed in a CANDU Calandria

Abstract Melting of the terminal debris bed at the bottom of a Canada deuterium uranium (CANDU) 6 calandria vessel during a severe accident is studied using a three-dimensional transient finite element analysis. Settling of the debris is modeled, as are the effects of changing temperature and porosity on debris material properties. The time and magnitude of maximum heat rejection from the debris bed, and the maximum mass of molten debris, are calculated as functions of the accident timing. The results are compared with those from the modular accident analysis program (MAAP)-CANDU severe accident code.

Development of Experimental Technology for Simulated Fuel-Assembly Heating to Address Core-Material-Relocation Behavior During Severe Accident

Abstract The authors are developing an experimental technology for simulating severe accident (SA) conditions using simulant fuel material (ZrO2) that would contribute, not only to Fukushima Daiichi (1 F) decommissioning, but also to enhance the safety of worldwide existing and future nuclear power plants through clarification of accident progression behavior. Nontransfer (NTR) type plasma, which has been in practical use with a large torch capacity as high as 2 MW, has the potential to heat subject materials to very high-temperatures without selecting the target to be heated. When simulating 1 F with SA code (Severe Core Damage Analysis Package (SCDAP), Methods for Estimation of Leakages and Consequences of Releases (MELCOR) and Modular Accident Analysis Program (MAAP)), the target of this core-material-melting and relocation (CMMR) experiment was to confirm that NTR plasma has a sufficient heating performance realizing large temperature gradients (>2000 K/m) expected under 1 F conditions. The authors selected NTR-type plasma-heating technology that has the advantage of continuous heating in addition to its high-temperature level. A prototype large-scale experiment (1 m × 0.3 m dia.), called CMMR-0, was conducted in 2016, in which a large temperature gradient was realized and basic characteristics of a heated test assembly were studied. However, the maximum temperature was limited in this test by the instability of the plasma torch under low-oxygen concentrations. It was clarified through this test that an improvement in plasma-heating technology was necessary to heat the large-scale test assembly. The CMMR-1/-2 experiments were carried out in 2017 with a test assembly similar to CMMR-0, applying the improved technology (higher heating power and controlled oxygen concentration). In these two tests, heating history was different, resulting in similar physical responses with more pronounced material melting and relocation in the CMMR-2 experiment. The CMMR-2 experiment was selected from the perspective of establishing an experimental technology. The CMMR-2 experiment adopted a 30-min heating period, wherein the power was increased to a level where a large temperature gradient was expected at the lower part of the core under actual 1 F accident conditions. Most of the control blade and channel box migrated from the original position. After heating, the simulated fuel assembly was measured by X-ray-computed tomography (CT) technology and by electron probe micro-analyzer (EPMA). CT pictures and elemental mapping demonstrated its excellent performance with rather good precision. Based on these results, an excellent perspective, in terms of applicability of the NTR-type plasma-heating technology to the SA experimental study, was obtained.

Preliminary Investigations of the Feasibility of In-Vessel Melt Retention Strategies for a Small Modular Reactor Concept

The study presented in this paper is part of the technological surveillance performed at the Electricité De France (EDF) Research and Development (R&D) Center, in the Pericles department, and investigates the feasibility of modeling in-vessel melt retention (IVMR) phenomena for small modular reactors (SMR) with the modular accident analysis program version 5 in its EDF proprietary version (MAAP5_EDF), applying conservative hypotheses, such as constant decay heat after corium relocation to the lower head. The study takes advantage of a corium stratification model in the lower head of the vessel, developed by EDF R&D for large-sized prospective pressurized water reactors (PWRs). The analysis is based on a stepwise approach in order to evaluate the impact of various effects during IVMR conditions. First, an analytical calculation is performed in order to establish a reference case to which the MAAP5_EDF code results are compared. In a second step, the impact of the lower head geometry, vessel steel ablation, and subsequent relocation on the heat flux has been analyzed for cases where heat dissipation through radiation is neglected (in first approximation). Finally, the impact of heat losses through radiation as well as the crust formation around the pool has been assessed. The results demonstrate the applicability of the MAAP5_EDF code to SMRs, with heat fluxes lower than 1.1 MW/m2 for relevant cases, and identify modeling improvements.

Nuclear reactor vessel water level prediction during severe accidents using deep neural networks

Abstract Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

Smart support system for diagnosing severe accidents in nuclear power plants

Abstract Recently, human errors have very rarely occurred during power generation at nuclear power plants. For this reason, many countries are conducting research on smart support systems of nuclear power plants. Smart support systems can help with operator decisions in severe accident occurrences. In this study, a smart support system was developed by integrating accident prediction functions from previous research and enhancing their prediction capability. Through this system, operators can predict accident scenarios, accident locations, and accident information in advance. In addition, it is possible to decide on the integrity of instruments and predict the life of instruments. The data were obtained using Modular Accident Analysis Program code to simulate severe accident scenarios for the Optimized Power Reactor 1000. The prediction of the accident scenario, accident location, and accident information was conducted using artificial intelligence methods.

Identification of LOCA and Estimation of Its Break Size by Multiconnected Support Vector Machines

Nuclear power plants (NPPs) are composed of very large complex systems. During transient occurrences in NPPs, operators determine the transients of the NPP through information acquired from various measuring instruments. A support vector machine (SVM) based on serial and parallel connections, termed as a multiconnected SVM, is introduced in this paper. The loss of coolant accidents (LOCAs) was identified and their break sizes are estimated using the multiconnected SVM model. The optimal parameter values of the multiconnected SVM models are obtained using a genetic algorithm. In this paper, the modular accident analysis program code was used to simulate the severe accidents occurring due to a variety of design basis accidents. The proposed algorithm uses the short time-integrated simulated sensor signals just after the reactor trip. The results show that the multiconnected SVM model can identify LOCAs and estimate their break sizes accurately. It is expected that the LOCA identification and the accurate estimation of the break size are useful for NPP operators when they try to manage severe accidents.

Research and Development of Validation and Drill System for Full Scope of Severe Accident Management Guideline

To respond to the urgent needs of verification, training, and drill for full scope severe accident management guidelines (FSSAMG) among nuclear regulators, utilities, and research institutes, the FSSAMG verification and drill system is developed. The FSSAMG includes comprehensive scenarios under power condition, shutdown condition, spent fuel pool (SFP) condition, and refueling conditions. This article summarized the research and development of validation and drill system for FSSAMG by using the severe accident analysis computer code modular accident analysis program 5 (MAAP5). Realistic accident scenarios can be verified and exercised in the developed system to support FSSAMG training, drill, examination, and verification.