Search

A modified multiobjective self-adaptive differential evolution algorithm (MMOSADE) is presented in this paper to improve the accuracy of multiobjective optimization design in the nuclear power system. The performance of the MMOSADE is tested by the ZDT test function set and compared with classical evolutionary algorithms. The results indicate that MMOSADE has a better performance in convergence and diversity. Based on the MMOSADE, a multiobjective optimization design platform for the nuclear power system is proposed, and the application of which is carried out. The evaluation program of the PRHR-HX in AP1000 is developed, and its reliability is verified. The optimal design schemes of PHHR-HX are obtained by utilizing the multiobjective optimization design platform. The results show that the optimal design schemes can envelop the prototype design scheme. This conclusion proves that the optimization design platform proposed in this paper is effective and feasible.

The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employed a recently introduced machine learning technique, Support Vector Regression (SVR), which has a strong theoretical background in statistical learning theory. Superior empirical performance of the method has been reported on difficult regression problems in different fields of science and technology. SVR is a data driven, kernel based, nonlinear modelling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modelling. The starting set of experimental data for training and testing of the machine learning algorithm was obtained using a two-dimensional diffusion theory reactor physics computer code. We illustrate the performance of the solution and discuss its applicability, i.e., complexity, speed and accuracy, with a projection to a more realistic scenario involving machine learning from the results of more accurate and time consuming three-dimensional core modelling code.

The present paper deals with the investigation study on accident management in VVER-1000 reactor type conducted in the framework of a European Commission funded project. The mentioned study involved both experimental and computational fields. The purpose of this paper is to summarize the main findings from the execution of a wide-range analysis focused on AM in VVER-1000 with main regard to the qualification of computational tools and the proposal for an optimal AM strategy for this kind of NPP.

A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.

This paper proposes to design security measures based on the radioactive material package as the basic unit. The principle of four-layer defense in depth is put forward. Based on the concept of self-security intelligence, combined with out-of-vehicle monitoring, in-vehicle monitoring, and Beidou positioning technology, a security system for transport of radioactive materials was designed. It realized the perception, early warning, delay, and alarm functions and greatly improved the security.

The Separation and Safeguards Performance Model (SSPM) uses MATLAB/Simulink to provide a tool for safeguards analysis of bulk handling nuclear processing facilities. Models of aqueous and electrochemical reprocessing, enrichment, fuel fabrication, and molten salt reactor facilities have been developed to date. These models are used for designing the overall safeguards system, examining new safeguards approaches, virtually testing new measurement instrumentation, and analyzing diversion scenarios. The key metrics generated by the models include overall measurement uncertainty and detection probability for various material diversion or facility misuse scenarios. Safeguards modeling allows for rapid and cost-effective analysis for Safeguards by Design. The models are currently being used to explore alternative safeguards approaches, including more reliance on process monitoring data to reduce the need for destructive analysis that adds considerable burden to international safeguards. Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. This paper will describe the SSPM and its use for applying both traditional nuclear material accountancy and newer machine learning options.

Nuclear safeguards aim to confirm that nuclear materials and activities are used for peaceful purposes. To ensure that States are honoring their safeguards obligations, quantitative conclusions regarding nuclear material inventories and transfers are needed. Statistical analyses used to support these conclusions require uncertainty quantification (UQ), usually by estimating the relative standard deviation (RSD) in random and systematic errors associated with each measurement method. This paper has two main components. First, it reviews why UQ is needed in nuclear safeguards and examines recent efforts to improve both top-down (empirical) UQ and bottom-up (first-principles) UQ for calibration data. Second, simulation is used to evaluate the impact of uncertainty in measurement error RSDs on estimated nuclear material loss detection probabilities in sequences of measured material balances.

Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR) and auxiliary bearings are important backup and safety components in AMB systems. The performance of auxiliary bearings significantly affects the reliability, safety, and serviceability of the AMB system, the rotating equipment, and the whole reactor. Research on the dynamic behavior during the touchdown process is crucial for analyzing the severity of the touchdown. In this paper, a data-based dynamic analysis method of the touchdown process is proposed. The dynamic model of the touchdown process is firstly established. In this model, some specific mechanical parameters are regarded as functions of deformation of auxiliary bearing and velocity of rotor firstly; furthermore, a machine learning method is utilized to model these function relationships. Based on the dynamic model and the Kalman filtering technique, the proposed method can offer estimation of the rotor motion state from noisy observations. In addition, the estimation precision is significantly improved compared with the method without learning. The proposed method is validated by the experimental data from touchdown experiments.

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).

CFD simulation for a PWR is an important part for the development of Numerical Virtual Reactor (NVR) in Harbin Engineering University of China. CFD simulation can provide the detailed information of the flow and heat transfer process in a PWR. However, a large number of narrow flow channels with numerous complex structures (mixing vanes, dimples, springs, etc.) are located in a typical PWR. To obtain a better CFD simulation, the challenges created by these structural features were analyzed and some quantitative regularity and estimation were given in this paper. It was found that both computing resources and time are in great need for the CFD simulation of a whole reactor. These challenges have to be resolved, so two schemes were designed to assist/realize the reduction of the simulation burden on resources and time. One scheme is used to predict the combined efficiency of the simulation conditions (configuration of computing resources and application of simulation schemes), so it can assist the better choice/decision of the combination of the simulation conditions. The other scheme is based on the suitable simplification and modification, and it can directly reduce great computing burden.

With the development of information technology, the instrumentation and control system of nuclear power plant nowadays rely heavily on the massive and complex software to ensure the safe and efficient operation of the power plant. The improvement of the software design and development for the safety systems has been a research focus for its decisive impact on the nuclear safety. The framework of the software design and development for reactor protection system in High Temperature Gas-Cooled Reactor-Pebble bed Module was introduced in this paper. Firstly, during the design period, in addition to multichannel redundancy, grouping of protection variables and diverse 2-out-of-4 logics were adopted by different subsystems of each channel in case of common cause failure. Then a series of development characteristics together with strict software verification and validation were performed. Thirdly, during the software test period, an improved software reliability growth model based on the Goel-Okumoto model according to the analysis of fault severity was proposed to help in estimating the reliability of the software product and identifying the software release time.

While more than a decade ago reactor and thermal hydraulic calculations were tedious and often needed a lot of approximations and simplifications that forced the designers to take a very conservative approach, computational resources available nowadays allow engineers to cope with increasingly complex problems in a reasonable time. The use of best-estimate calculations provides tools to justify convenient engineering margins, reduces costs, and maximises economic benefits. In this direction, a suite of coupled best-estimate specific calculation codes was developed to analyse the behaviour of the Atucha II nuclear power plant in Argentina. The developed tool includes three-dimensional spatial neutron kinetics, a channel-level model of the core thermal hydraulics with subcooled boiling correlations, a one-dimensional model of the primary and secondary circuits including pumps, steam generators, heat exchangers, and the turbine with all their associated control loops, and a complete simulation of the reactor control, limitation, and protection system working in closed-loop conditions as a faithful representation of the real power plant. In the present paper, a description of the coupling scheme between the codes involved is given, and some examples of their application to Atucha II are shown.

The evaluation of uncertainty constitutes the necessary supplement of best-estimate calculations performed to understand accident scenarios in water-cooled nuclear reactors. The needs come from the imperfection of computational tools, on the one side, and the interest in using such a tool to get more precise evaluation of safety margins. The paper reviews the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes. Namely, the propagations of code input error and calculation output error constitute the keywords for identifying the methods of current interest for industrial applications, while the adjoint sensitivity-analysis procedure and the global adjoint sensitivity-analysis procedure, extended to performing uncertainty evaluation in conjunction with concepts from data adjustment and assimilation, constitute the innovative approach. Throughout the developed methods, uncertainty bands can be derived (both upper and lower) for any desired quantity of the transient of interest. For one case, the uncertainty method is coupled with the thermal-hydraulic code to get the code with capability of internal assessment of uncertainty, whose features are discussed in more detail.

Since the 1988 amendment of the 10 CFR 50.46 rule in 1988, Westinghouse has been developing and applying realistic or best-estimate methods to perform LOCA safety analyses. A realistic analysis requires the execution of various realistic LOCA transient simulations where the effect of both model and input uncertainties are ranged and propagated throughout the transients. The outcome is typically a range of results with associated probabilities. The thermal/hydraulic code is the engine of the methodology but a procedure is developed to assess the code and determine its biases and uncertainties. In addition, inputs to the simulation are also affected by uncertainty and these uncertainties are incorporated into the process. Several approaches have been proposed and applied in the industry in the framework of best-estimate methods. Most of the implementations, including Westinghouse, follow the Code Scaling, Applicability and Uncertainty (CSAU) methodology. Westinghouse methodology is based on the use of the WCOBRA/TRAC thermal-hydraulic code. The paper starts with an overview of the regulations and its interpretation in the context of realistic analysis. The CSAU roadmap is reviewed in the context of its implementation in the Westinghouse evaluation model. An overview of the code (WCOBRA/TRAC) and methodology is provided. Finally, the recent evolution to nonparametric statistics in the current edition of the W methodology is discussed. Sample results of a typical large break LOCA analysis for a PWR are provided.

In Integrated Deterministic and Probabilistic Safety Analysis (IDPSA), safe scenarios and Prime Implicants (PIs), i.e., minimum combinations of failure events that are capable of leading the system into a fault state are generated by simulation. Post-processing is needed to extract relevant information from these scenarios. In this paper, we propose a novel post-processing method which resorts to a risk-based clustering method for identifying Near Misses among the safe scenarios, i.e., combinations of failure events that lead the system to a quasi-fault state, a condition close to accident. This is important because the possibility of recovering these combinations of failures within a tolerable grace time allows avoiding deviations to accident and, thus, reducing the downtime (and the risk) of the system. The early identification of Near Misses can, then, be useful for online integrated risk monitoring, for rapidly detecting the incipient problems and setting up the recovery strategy of the occurred failures. The post-processing risk-significant features for the clustering are extracted from: i) the probability of a scenario to develop into an accidental scenario, ii) the severity of the consequences that the developing scenario would cause to the system, iii) the combination of i) and ii) into the overall risk of the developing scenario. The optimal selection of the extracted features is done by a wrapper approach, whereby a Modified Binary Differential Evolution (MBDE) embeds a K-means clustering algorithm. The characteristics of the Near Misses scenarios are identified solving a multi-objective optimization problem, using the Hamming distance as a measure of similarity. The feasibility of the analysis is shown with respect to fault scenarios in a dynamic Steam Generator (SG) of a Nuclear Power Plant (NPP).

Chinese integrated pressurized water reactor (CIPWR) has compact configuration and high inherent safety, which is appropriate for nuclear power plants of small and medium scale. Heat balance model has been adopted widely in thermal power calibration of PWRs because of its advantage of accuracy. In this paper, a package based on FORTRAN language is developed and added into RELAP5 to calculate the heat loss value needed in heat balance analysis. The steady-state operation of CIPWR is modelled correctly by RELAP5. The heat loss of CIPWR is calculated by the package, and the comparison of the main values of parameters needed in the heat loss calculation between RELAP5 and the package has been done. It shows that the package has high calculation accuracy and can be applied in reactor design and monitoring.

The international OECD/NRC PSBT benchmark has been established to provide a test bed for assessing the capabilities of thermal-hydraulic codes and to encourage advancement in the analysis of fluid flow in rod bundles. The benchmark was based on one of the most valuable databases identified for the thermal-hydraulics modeling developed by NUPEC, Japan. The database includes void fraction and departure from nucleate boiling measurements in a representative PWR fuel assembly. On behalf of the benchmark team, PSU in collaboration with US NRC has performed supporting calculations using the PSU in-house advanced thermal-hydraulic subchannel code CTF and the US NRC system code TRACE. CTF is a version of COBRA-TF whose models have been continuously improved and validated by the RDFMG group at PSU. TRACE is a reactor systems code developed by US NRC to analyze transient and steady-state thermal-hydraulic behavior in LWRs and it has been designed to perform best-estimate analyses of LOCA, operational transients, and other accident scenarios in PWRs and BWRs. The paper presents CTF and TRACE models for the PSBT void distribution exercises. Code-to-code and code-to-data comparisons are provided along with a discussion of the void generation and void distribution models available in the two codes.

Process monitoring (PM) for nuclear safeguards sometimes requires estimation of thresholds corresponding to small false alarm rates. Threshold estimation dates to the 1920s with the Shewhart control chart; however, because possible new roles for PM are being evaluated in nuclear safeguards, it is timely to consider modern model selection options in the context of threshold estimation. One of the possible new PM roles involves PM residuals, where a residual is defined as residual = data − prediction. This paper reviews alarm threshold estimation, introduces model selection options, and considers a range of assumptions regarding the data-generating mechanism for PM residuals. Two PM examples from nuclear safeguards are included to motivate the need for alarm threshold estimation. The first example involves mixtures of probability distributions that arise in solution monitoring, which is a common type of PM. The second example involves periodic partial cleanout of in-process inventory, leading to challenging structure in the time series of PM residuals.

The research is conducted on the modification of neutron kinetic code for the plate type fuel nuclear reactor. REMARK is a neutron kinetic code that works only for the cylindrical type fuel nuclear reactor. In this research, our main emphasis is on the modification of this code in order to be applicable for the plate type fuel nuclear reactor. For this purpose, detailed mathematical studies have been performed and are subjected to write the program in Fortran language. Since REMARK code is written in Fortran language, so we have developed the program in Fortran and then inserted it into the source library of the code. The main emphasis is on the modification of subroutine in the source library of the code for hexagonal fuel assemblies with plate type fuel elements in it. The number of steps involved in the modification of the code has been included in the paper. The verification studies were performed by considering the small modular reactor with hexagonal assemblies and plate type fuel in it to find out the power distribution of the reactor core. The purpose of the research is to make the code work for the hexagonal fuel assemblies with plate type fuel element.