182 research outputs found

    Modelling the effects of oscillating stripe cooling during a loca event in a vver440/213 reactor pressure vessel

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    A Loss of Coolant Accident (LOCA) is initiated by damage in the primary circuit and subsequent coolant leak of a pressurised water reactor (PWR). The loss of coolant is then compensated by the Emergency Core Cooling System (ECCS) [1]. This system supplies the primary circuit with cold high pressure coolant during emergencies. The mixing of this cold coolant results in stripe cooling of the RPV wall. This article focuses on the modelling of the mixing processes of this cooling stripe and their effects on the RPV wall in a VVER440/213 Russian type PWR. The mixing processes are modelled in a transient thermohydraulic analysis which models the mixing of the coolant flows in the reactor pressure vessel and results in the overtime temperature and pressure fields within the RPV [2]. The analysis results show that the cooling stripe is not stationary. The turbulent mixing causes an unstable oscillatory motion of the cold stripe which has a notable effect on the RPV wall temperature distribution. Selected results were subsequently transferred into a thermo-mechanical analysis via one way coupling method. This analysis was performed to evaluate the PRV loading state during the LOCA transient. Where the loading stresses were shown to correspond to the oscillatory nature of the cooling strip. In conclusion, an oscillating cooling strip can result in multiple loading cycles [3,4] of the reactor pressure vessel wall during a single LOCA transient or ECCS high pressure coolant injection

    The Challenges and Implications for Energy Education

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    In March 2011, the Fukushima Daiichi Nuclear Power Station in Japan was hit by twin natural disasters of a 9.0 earthquake and a tsunami more than 10 meters high. The combination forced a shut down of the reactor and severed externally provided power. Backup generators and batteries, which at first functioned normally, were swamped and disabled by the tsunami waters. The subsequent loss of reactor core cooling led to rising temperatures that ultimately resulted in a partial melting of the fuel rods and several hydrogen explosions (produced by a steam-zirconium reaction). Eventually, the situation was brought under control, but not before several forms of radioactivity were released, some intentionally and some not. The technical aspects of the events in Japan, the media coverage of them, and the public’s reaction to them, all raise challenges to the engineering education community. For several decade,s nuclear engineering education has declined in the U.S., with many predicting it to be a dying field. While recent years have seen an increase in students and program options, due to workforce issues and the potential of new U.S. plants being built, nuclear engineering is still a relatively minor player in the engineering education field. Additionally, whereas students in other fields are routinely exposed to topics such as solar and wind power during their general studies, coverage of nuclear power in even its broadest aspects is not provided to most engineering students. As indicated by Fukushima, there are currently many complex design challenges in nuclear power that need trained engineers from all fields to address them. In addition, as some reviews of the accident have pointed out, regardless of the future of nuclear energy, there is a need to maintain a skilled nuclear power workforce for the foreseeable future to address the safety of existing plants and spent fuel storage facilities, such as those that now sit at Fukushima. A separate and possibly more crucial role for engineering educators is seen when the media coverage and public reaction to Fukushima is analyzed. The public reaction demonstrated a general lack of scientific understanding and a wealth of mis- and pre-conceptions. When situations like this arise, the public must rely on other reliable sources, such as the media and various governmental agencies to fill in the gaps. Unfortunately, in the case of Fukushima, the various agencies involved provided conflicting reports, and the media proved to be only marginally more knowledgeable than the public. This lecture will go through a timeline of technical events during the Fukushima nuclear accident. Corresponding details on the information and disinformation released by various agencies and media outlets, as well as the public reaction to it, will be overlaid with this. As a post analysis, the way these issues continue to play out in the public and within the areas of energy education will be discussed

    Seismic analysis of the condensate storage tank in a nuclear power plant

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    Following the nuclear power plant accident in Fukushima Japan, seismic capacity evaluation has become a crucial issue in combination building safety. Condensate storage tanks are designed to supplies water to the condensate transfer pumps, the control rod drive hydraulic system pumps, and the condenser makeup. A separate connection to the condensate storage tank is used to supply water for the high pressure coolant injection system, reactor core isolation cooling system, and core spray system pumps. A condensate storage tank is defined as a seismic class I structure, playing the important role of providing flow to the operational system and the required static head for the suction of the condensate transfer pumps and the normal supply pump. According to the latest nuclear safety requirements, soil structure interaction must be considered in all seismic analyses. This study aims to rebuild the computer model of condensate storage tanks in Taiwan using the SAP 2000 program in conjunction with the lumped mass stick model and to evaluate the soil structure interaction by employing the SASSI 2000 program. The differences between the results with the soil structure interaction and spring model are compared via natural frequency and response spectrum curves. This computer model enables engineers to rapidly evaluate the safety margin of condensate storage tank following the occurrence of earthquakes or tsunamis

    A chi-square goodness-of-fit test for non-identically distributed random variables: with application to empirical Bayes

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    A Fully Bayesian Approach for Combining Multilevel Failure Information in Fault Tree Quantification and Corresponding Optimal Resource Allocation

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    This paper presents a fully Bayesian approach that simultaneously combines basic event and statistically independent higher event-level failure data in fault tree quantification. Such higher-level data could correspond to train, sub-system or system failure events. The full Bayesian approach also allows the highest-level data that are usually available for existing facilities to be automatically propagated to lower levels. A simple example illustrates the proposed approach. The optimal allocation of resources for collecting additional data from a choice of different level events is also presented. The optimization is achieved using a genetic algorithm

    An artificial neutral network fault-diagnostic adviser for a nuclear power plant with error prediction

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    Since the accidents at the Three-Mile Island (TMI) and m Chernobyl nuclear power plants (NPPs), the safety of NPPs has become an even more important concern to both the nuclear power industry and the general public. The demand for safer plants has ever since. Responding to the demand, many scientists increased have investigated augmenting NPP safety in various ways. For m example, innovative reactor designs, better safety system R designs, human factor studies, stricter safety regulations, and so on, have been developed and implemented in the years since the above-mentioned accidents

    JMASM 51: Bayesian Reliability Analysis of Binomial Model – Application to Success/Failure Data

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    Reliability data are generated in the form of success/failure. An attempt was made to model such type of data using binomial distribution in the Bayesian paradigm. For fitting the Bayesian model both analytic and simulation techniques are used. Laplace approximation was implemented for approximating posterior densities of the model parameters. Parallel simulation tools were implemented with an extensive use of R and JAGS. R and JAGS code are developed and provided. Real data sets are used for the purpose of illustration

    Nuclear plant diagnostics using neural networks with dynamic input selection

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    The work presented in this dissertation explores the design and development of a large scale nuclear power plant (NPP) fault diagnostic system based on artificial neural networks (ANNs). The viability of detecting a large number of transients in a NPP using ANNs is demonstrated. A new adviser design is subsequently presented where the diagnostic task is divided into component parts, and each part is solved by an individual ANN. This new design allows the expansion of the diagnostic capabilities of an existing adviser by modifying the existing ANNs and adding new ANNs to the adviser;This dissertation also presents an architecture optimization scheme called the dynamic input selection (DIS) scheme. DIS analyzes the training data for any problem and ranks the available input variables in order of their importance to the input-output relationship. Training is initiated with the most important input and one hidden node. As the network training progresses, input and hidden nodes are added as required until the networks have learned the problem. Any hidden or input nodes that were added during training but are unnecessary for subsequent recall are now removed from the network. The DIS scheme can be applied to any ANN learning paradigm;The DIS scheme is used to train the ANNs that form the NPP fault diagnostic adviser. DIS completely eliminates any guesswork related to architecture selection, thus decreasing the time taken to train each ANN. Each ANN uses only a small subset of the available input variables that is required to solve its particular task. This reduction in the dimensionality of the problem leads to a drastic reduction in training time;Data used in this work was collected during the simulation of transients on the operator training simulator at Duane Arnold Energy Center, a boiling water reactor nuclear power plant. An adviser was developed to detect and classify 30 distinct transients based on the simulation of 47 scenarios at different severities. This adviser was then expanded to detect and classify a total of 36 transients based on the simulation of 58 transient scenarios. The noise tolerant characteristics of the adviser are demonstrated

    Time dependent unavailability analysis to standby safety systems

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    "Prepared for Brookhaven National Laboratory."Includes bibliographical references (p. 280-284)Contract no. BNL-54668
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