Nuclear Power Plant Transients Research Articles

Prediction of steam/water stratified flow characteristics in NPPs transients using SVM learning algorithm with combination of thermal-hydraulic model and new data mapping technique

Steam/water stratified flow would occur in transient condition (e.g. LOCA) in light water Nuclear Power Plants (NPPs). Due to high gradient of flow characteristics at the interface of steam/water flow, the prediction of flow characteristics (e.g. temperature, pressure, velocity, and Turbulent Kinetic Energy (TKE)) requires further attention and special interfacial models. Also, accurate simulation of these mentioned characteristics needs fine spatial mesh and very small time steps based on Computational Fluid Dynamics (CFD) standard criteria. In order to reduce the computational cost, the combination of thermal–hydraulic modelling and soft computing is considered as a new strategy in this study. The steam/water stratified flow in a rectangular channel (case 3 of Lim et al test section) is examined as case study and calculated values of the characteristics by thermal–hydraulic model are fed as training/test data to the Support Vector Machine (SVM) learning algorithm. SVM in combination with the proposed data mapping technique which is a type of autocorrelation finding predicts the value of each characteristic at a specific position/ time using the value of that characteristic at previous time at that position and previous position. The results show that the proposed methodology is appropriate for prediction of steam/water flow characteristics. Velocity, temperature, and TKE are predicted with reasonable accuracy. The predicted pressure shows a trend similar to the values obtained from the thermal–hydraulic modelling. For precise prediction of parameters similar to the pressure, it seems deep learning in combination with the proposed data mapping technique and a kind of features selection technique are needed. This method is under development and will be reported as the subsequent.

Use of fundamental condensation heat transfer experiments for the development of a sub-grid liquid jet condensation model

Condensation on liquid jets is an important phenomenon for many different facets of nuclear power plant transients and analyses such as containment spray cooling. An experimental facility constructed at the Pennsylvania State University, the High Pressure Liquid Jet Condensation Heat Transfer facility (HPLJCHT), has been used to perform steady-state condensation heat transfer experiments in which the temperature of the liquid jet is measured at different axial locations allowing the condensation rate to be determined over the jet length. Test data have been obtained in a pure steam environment and with varying concentrations of noncondensable gas. This data extends the available jet condensation data from near atmospheric pressure up to a pressure of 1.7MPa. An empirical correlation for the liquid side condensation heat transfer coefficient has been developed based on the data obtained in pure steam. The data obtained with noncondensable gas were used to develop a correlation for the renewal time as used in the condensation suppression model developed by Young and Bajorek.This paper describes a new sub-grid liquid jet condensation heat transfer model. In the current work, mass and energy balance equations are solved in a marching scheme in each sub-grid node along the path of the jet trajectory. Jet specific condensation heat transfer closure relations are used. The jet sub-grid method has been implemented as a boundary condition in an in-house version of the sub-channel analysis code COBRA-TF (COBRA-IE). COBRA-IE fluid nodes provide the required vapor and noncondensable gas conditions for the heat transfer solution. The sub-grid model solves the liquid side heat transfer and the condensation rates for each volume in the sub-grid solution. These terms are summed along all of the sub-grid cells that pass through each COBRA-IE control volume to provide mass and energy transfer rates for the COBRA-IE solution. Results using the new jet injection boundary condition show an improved ability to simulate jet condensation experimental data.

Effects of mixed strain rates on low cycle fatigue behaviors of austenitic stainless steels in a simulated PWR environment

Though strain rates are hardly fixed during the actual transients in nuclear power plants (NPP), most of the existing environmental fatigue ε–N data were obtained under simple loading histories with constant strain rates. In an effort to incorporate the actual loading conditions, strain rate was changed during the low cycle fatigue (LCF) test. The LCF tests were performed in a strain control mode in both simulated pressurized water reactor (PWR) environment and 310°C air to evaluate the effects of mixed strain rates on fatigue life. The test results indicated that the modified strain rate approach and average strain rate method could be applied to the fatigue evaluation considering environmental effects. The cyclic stress response showed that the trend of hardening with mixed strain rate was consistent with a negative strain rate sensitivity, which was observed in the constant strain rate test. In addition, the increase in striation spacing on the fracture surface is in good agreement with the increase of bulk dislocation density and reduction of fatigue life.

A pattern recognition approach based on DTW for automatic transient identification in nuclear power plants

Automatic identification of transients in nuclear power plants (NPPs) allows monitoring the fatigue damage accumulated by critical components during plant operation, and is therefore of great importance for ensuring that usage factors remain within the original design bases postulated by the plant designer. Although several schemes to address this important issue have been explored in the literature, there is still no definitive solution available. In the present work, a new method for automatic transient identification is proposed, based on the Dynamic Time Warping (DTW) algorithm, largely used in other related areas such as signature or speech recognition. The novel transient identification system is evaluated on real operational data following a rigorous pattern recognition protocol. Results show the high accuracy of the proposed approach, which is combined with other interesting features such as its low complexity and its very limited requirements of training data.

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

The Effect on the Voltage Sag of Safety Related Bus and the Operating Characteristics of Motor due to Electrical Transients in Nuclear Power Plants

When electric faults take place in onsite power system of nuclear power plants, the voltage of safety related bus can be affected, as a form of voltage sag, by electrical transients. The safety related buses in nuclear power plants have a role of power supplying to essential loads for safety shutdown of reactor. If voltage sag of the safety related bus would be occurred and impact on the operating characteristics of motor would be existed, the risk of nuclear power plants will be increased. Therefore, it is important to analyze the effect on voltage sag of safety related bus and operating characteristics of motor due to electrical transients in nuclear power plants. This study performed that the phenomenon of voltage sag and the effect on motor operation by various electrical transients are simulated and analyzed.

OPTIMIZATION OF THE PARAMETERS OF FEEDWATER CONTROL SYSTEM FOR OPR1000 NUCLEAR POWER PLANTS

In this study, the parameters of the feedwater control system (FWCS) of the OPR1000 type nuclear power plant (NPP) are optimized by response surface methodology (RSM) in order to acquire better level control performance from the FWCS. The objective of the optimization is to minimize the steam generator (SG) water level deviation from the reference level during transients. The objective functions for this optimization are relationships between the SG level deviation and the parameters of the FWCS. However, in this case of FWCS parameter optimization, the objective functions are not available in the form of analytic equations and the responses (the SG level at plant transients) to inputs (FWCS parameters) can be evaluated by computer simulations only. Classical optimization methods cannot be used because the objective function value cannot be calculated directely. Therefore, the simulation optimization methodology is used and the RSM is adopted as the simulation optimization algorithm. Objective functions are evaluated with several typical transients in NPPs using a system simulation computer code that has been utilized for the system performance analysis of actual NPPs. The results show that the optimized parameters have better SG level control performance. The degree of the SG level deviation from the reference level during transients is minimized and consequently the control performance of the FWCS is remarkably improved.

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project.

Uncertainty and sensitivity analyses of the Kozloduy pump trip test using coupled thermal–hydraulic 3D kinetics code

The modeling of complex transients in nuclear power plants (NPP) remains a challenging topic for best estimate three-dimensional coupled code computational tools. This technique is, nowadays, extensively used since it allows decreasing conservatism in the calculation models and performs more realistic simulation and more precise consideration of multidimensional effects under complex transients in NPPs. Therefore, large international activities are in progress aiming to assess the capabilities of coupled codes and the new frontiers for the nuclear technology that could be opened by this technique. In the current paper, a contribution to the assessment and validation of coupled code technique through the Kozloduy VVER100 pump trip test is performed. For this purpose, the coupled RELAP5/3.3-PARCS/2.6 code is used. The code results were assessed against experimental data. Deviations between code predictions and measurements are mainly due to the used models for evaluating and modeling of the Doppler feedback effect. Further investigations through the use of two “antagonist” uncertainty GRS and the CIAU methods, were considered in order to evaluate and quantify the origin of the observed discrepancies. It was revealed on one hand that relative error quantification discrepancies exist between the two approaches, and further enhancements for both methods are needed.

Assessment study of the coupled code RELAP5/PARCS against the Peach Bottom BWR turbine trip test

The modeling of complex transients in nuclear power plants (NPP) remains a challenging topic for best estimate three-dimensional coupled code computational tools. This technique is, nowadays, extensively used for simulating transients that involve core spatial asymmetric phenomena and strong feedback effects between core neutronics and reactor loop thermal–hydraulics. In this framework, the Peach Bottom BWR turbine trip experiment 2 is considered. The test involves a rapid positive reactivity addition into the core generated by a water hammer load. To perform a numerical simulation of such phenomenon a reference case was calculated using the coupled code RELAP5/PARCS. An overall data comparison shows good agreement between calculated and measured pressure wave trend in the core region. However, the predicted power response during the excursion phase did not match correctly the experimental tendency. For this purpose, a series of sensitivity analyses have been carried out to identify the most probable reasons of such discrepancy. It was found out that the uncertainties related to the cross-sections modeling and to the thermal–hydraulic closure relationships are the main source of the incorrect power feedback response during the transient.

Hybrid Identification of Nuclear Power Plant Transients With Artificial Neural Networks

Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear power plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear power plant malfunctions. The correct identification of unlabeled transients-or transients of the "don't-know" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes were used for finding the best (sensitive and robust) algorithms. Various neural network based models were successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear power plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions.

Fuzzy identification of transients in nuclear power plants

The identification of transients is of fundamental importance for the timely monitoring of nuclear plants operation. The main target is detecting the occurrence of the onset of a transient, in its early stages, and identifying its kind so as to be able to readily act to fix its causes. Given the safety and economical importance of the problem, various approaches have been investigated and applied for transient identification, and many efforts are still devoted to the improvement of the results so far obtained. In this paper, a fuzzy-logic based method for the identification of transients is proposed. The method is ‘model-free’ in that the if-then rules, which constitute the heart of the approach, are inferred only from the available input-output signal data. The method is tested on an example of identification of reactor transients generated by four forcing functions of different nature. The necessary data for the identification have been simulated by the QUAndry-based Reactor Kinetics code (QUARK, distributed freely by NEA) configured so as to model the operations of the Westinghouse Advanced Pressurized water reactor, AP600.

Application and In-depth Assessment of RETRAN-3D for Best Estimate Analysis of Nuclear Power Plant Transients

The code RETRAN-3D has been extensively applied in the project STARS at PSI to perform BE analysis for a range of operational and other (non-LOCA) transients for the Swiss NPPs, which include both PWRs and BWRs. Extensive assessment employing experimental and plant data has provided confidence in the applicability and accuracy of RETRAN-3D for the analysis of transients in both types of LWRs. In this context, this paper presents an in-depth study of the performance of the code for two types of applications. First, a detailed analysis of BWR/6 recirculation pump trip tests shows that the code is able to accurately predict the coupled neutronic and thermal-hydraulic behaviour during an operational transient in which plant-specific system features (e.g. SRI insertion, response of the recirculation line valves to changes in flow, etc.) play an important role. Second, RETRAN-3D is applied to BE analysis of a PWR beyond-design basis scenario, namely the failure of the RHR system during reactor shutdown. In-depth assessment of the results obtained demonstrates the applicability of RETRAN-3D and the need for employing a BE approach to predict a more accurate, shorter “time window” for operator remedial action than that estimated using the assumption of uniform primary temperature distribution.

Hidden Markov model-based real-time transient identifications in nuclear power plants

In this article, a transient identification method based on a stochastic approach with the hidden Markov model (HMM) has been suggested and evaluated experimentally for the classification of nine types of transients in nuclear power plants (NPPs). A transient is defined as when a plant proceeds to an abnormal state from a normal state. Identification of the types of transients during an early accident stage in NPPs is crucial for proper action selection. The transient can be identified by its unique time-dependent patterns related to the principal variables. The HMM, a double-stochastic process, can be applied to transient identification that is a spatial and temporal classification problem under a statistical pattern-recognition framework. The trained HMM is created for each transient from a set of training data by the maximum-likelihood estimation method which uses a forward-backward algorithm and the Baum-Welch re-estimation algorithm. The transient identification is determined by calculating which model has the highest probability for given test data using the Viterbi algorithm. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. There are also a few experimental tests that have been performed, including superimposing random noise, adding systematic error, and adding untrained transients to verify its performance and robustness. The proposed real-time transient identification system has been proven to have many advantages, although there are still some problems that should be solved before applying it to an operating NPP. Further efforts are being made to improve the system performance and robustness in order to demonstrate reliability and accuracy to the required level. © 2002 Wiley Periodicals, Inc.

Application and In-depth Assessment of RETRAN-3D for Best Estimate Analysis of Nuclear Power Plant Transients.

The code RETRAN-3D has been extensively applied in the project STARS at PSI to perform BE analysis for a range of operational and other (non-LOCA) transients for the Swiss NPPs, which include both PWRs and BWRs. Extensive assessment employing experimental and plant data has provided confidence in the applicability and accuracy of RETRAN-3D for the analysis of transients in both types of LWRs. In this context, this paper presents an in-depth study of the performance of the code for two types of applications. First, a detailed analysis of BWR/6 recirculation pump trip tests shows that the code is able to accurately predict the coupled neutronic and thermal-hydraulic behaviour during an operational transient in which plant-specific system features (e.g. SRI insertion, response of the recirculation line valves to changes in flow, etc.) play an important role. Second, RETRAN-3D is applied to BE analysis of a PWR beyond-design basis scenario, namely the failure of the RHR system during reactor shutdown. In-depth assessment of the results obtained demonstrates the applicability of RETRAN-3D and the need for employing a BE approach to predict a more accurate, shorter time window for operator remedial action than that estimated using the assumption of uniform primary temperature distribution.

Predicting the Severity of Nuclear Power Plant Transients Using Nearest Neighbors Modeling Optimized by Genetic Algorithms on a Parallel Computer

The importance of automatic diagnostic systems for nuclear power plants (NPPs) has been discussed in numerous studies, and various such systems have been proposed. None of those systems were designed to predict the severity of the diagnosed scenario. A classification and severity prediction system for NPP transients is developed. The system is based on nearest neighbors modeling, which is optimized using genetic algorithms. The optimization process is used to determine the most important variables for each of the transient types analyzed. An enhanced version of the genetic algorithms is used in which a local downhill search is performed to further increase the accuracy achieved. The genetic algorithms search was implemented on a massively parallel supercomputer, the KSR1-64, to perform the analysis in a reasonable time. The data for this study were supplied by the high-fidelity simulator of the San Onofre unit 1 pressurized water reactor.

Error Prediction for a Nuclear Power Plant Fault-Diagnostic Advisor Using Neural Networks

AbstractThe objective of this research is to develop a fault-diagnostic advisor for nuclear power plant transients that is based on artificial neural networks. A method is described that provides an error bound and therefore a figure of merit for the diagnosis provided by this advisor. The data used in the development of the advisor contain ten simulated anomalies for the San Onofre Nuclear Power Generating Station. The stacked generalization approach is used with two different partitioning schemes. The results of these partitioning schemes are compared. It is shown that the advisor is capable of recognizing all ten anomalies while providing estimated error bounds on each of its diagnoses.

Computer simulation of two-phase flow in nuclear reactors

Two-phase flow models dominate the requirements of economic resources for the development and use of computer codes which serve to analyze thermohydraulic transients in nuclear power plants. An attempt is made to reduce the effort of analyzing reactor transients by combining purpose-oriented modeling with advanced computing techniques. Six principles are presented on mathematical modeling and the selection of numerical methods, along with suggestions on programming and machine selection, all aimed at reducing the cost of analysis. Computer simulation is contrasted with traditional computer calculation. The advantages of run-time interactive access operation in a simulation environment are demonstrated. It is explained that the drift-flux model is better suited than the two-fluid model for the analysis of two-phase flow in nuclear reactors, because of the latter's closure problems. The advantage of analytical over numerical integration is demonstrated. Modeling and programming techniques are presented which minimize the number of needed arithmetical and logical operations and thereby increase the simulation speed, while decreasing the cost.

On choices between the two-fluid and the mixture models for simulation of severe transients in nuclear power plants

Severe transients can lead to severe accidents if the prescribed remedies are not undertaken. Although rare, the severe transients do occur from time to time. Only one of them (the Three Mile Island incident) actually became a severe accident in the United States, partly because the operator training, prior to that incident, was based on simulators which were incapable of dealing with a notion of a water filled pressurizer in conjunction with vapor in the reactor core. A great deal of research and development has, since then, been undertaken by all simulator vendors in the attempt to base the simulation on the laws of nature. Our knowledge of these very complex laws is not complete and, therefore, that effort is still continuing and will continue into the foreseeable future. The purpose of this paper is to briefly comment on the modeling trends for thermohydraulics in existing or planned engineering (or advanced or high fidelity) simulators and on their solution techniques, to indicate the need for further basic research in order to improve reliability of the two-fluid model, and to make a case for application of simpler, mixture-based models in the meantime.