Download or read book Performing Probabilistic Risk Assessment Through RAVEN written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Reactor Analysis and Virtual control ENviroment (RAVEN) code is a software tool that acts as the control logic driver and post-processing engine for the newly developed Thermal-Hydraulic code RELAP-7. RAVEN is now a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities: Derive and actuate the control logic required to simulate the plant control system and operator actions (guided procedures), allowing on-line monitoring/controlling in the Phase Space Perform both Monte-Carlo sampling of random distributed events and Dynamic Event Tree based analysis Facilitate the input/output handling through a Graphical User Interface (GUI) and a post-processing data mining module.

Download or read book RAVEN and Dynamic Probabilistic Risk Assessment written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory. Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism has been employed by providing Application Programming Interfaces (APIs). These interfaces are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable to investigate the system response, investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The paper presents an overview of the software capabilities and their implementation schemes followed by some application examples.

Download or read book Raven as a Tool for Dynamic Probabilistic Risk Assessment written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: RAVEN is a software tool under development at the Idaho National Laboratory (INL) that acts as the control logic driver and post-processing tool for the newly developed Thermo-Hydraylic code RELAP- 7. The scope of this paper is to show the software structure of RAVEN and its utilization in connection with RELAP-7. A short overview of the mathematical framework behind the code is presented along with its main capabilities such as on-line controlling/monitoring and Monte-Carlo sampling. A demo of a Station Black Out PRA analysis of a simplified Pressurized Water Reactor (PWR) model is shown in order to demonstrate the Monte-Carlo and clustering capabilities.

Download or read book Advanced Probabilistic Risk Analysis Using RAVEN and RELAP 7 written by and published by . This book was released on 2014 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program [1], is advancing its capability to perform statistical analyses of stochastic dynamic systems. This is aligned with its mission to provide the tools needed by the Risk Informed Safety Margin Characterization (RISMC) path-lead [2] under the Department Of Energy (DOE) Light Water Reactor Sustainability program [3]. In particular this task is focused on the synergetic development with the RELAP-7 [4] code to advance the state of the art on the safety analysis of nuclear power plants (NPP). The investigation of the probabilistic evolution of accident scenarios for a complex system such as a nuclear power plant is not a trivial challenge. The complexity of the system to be modeled leads to demanding computational requirements even to simulate one of the many possible evolutions of an accident scenario (tens of CPU/hour). At the same time, the probabilistic analysis requires thousands of runs to investigate outcomes characterized by low probability and severe consequence (tail problem). The milestone reported in June of 2013 [5] described the capability of RAVEN to implement complex control logic and provide an adequate support for the exploration of the probabilistic space using a Monte Carlo sampling strategy. Unfortunately the Monte Carlo approach is ineffective with a problem of this complexity. In the following year of development, the RAVEN code has been extended with more sophisticated sampling strategies (grids, Latin Hypercube, and adaptive sampling). This milestone report illustrates the effectiveness of those methodologies in performing the assessment of the probability of core damage following the onset of a Station Black Out (SBO) situation in a boiling water reactor (BWR). The first part of the report provides an overview of the available probabilistic analysis capabilities, ranging from the different types of distributions available, possible sampling strategies, and post processing analysis capabilities. The first part of the report provides an extensive description of two major developments introduced this year: adaptive sampling for limit surface sampling and multi variate distributions. The document concludes with a description of the demo case (BWR-SBO) and a discussion of the results obtained.

Download or read book RAVEN written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics are not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed to perform two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, the control logic infrastructure is used to model stochastic events, such as components failures, and perform uncertainty propagation. Such stochastic modeling is deployed using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This report focuses on the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, a DPRA analysis, using DET, of a simplified pressurized water reactor for a Station Black-Out (SBO) scenario is presented.

Download or read book Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes written by and published by . This book was released on 2015 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: The RAVEN code is becoming a comprehensive tool to perform Probabilistic Risk Assessment (PRA); Uncertainty Quantification (UQ) and Propagation; and Verification and Validation (V & V). The RAVEN code is being developed to support the Risk-Informed Safety Margin Characterization (RISMC) pathway by developing an advanced set of methodologies and algorithms for used in advanced risk analysis. The RISMC approach uses system simulator codes applied to stochastic analysis tools. The fundamental idea behind this coupling approach to perturb (by employing sampling strategies) timing and sequencing of events, internal parameters of the system codes (i.e., uncertain parameters of the physics model) and initial conditions to estimate values ranges and associated probabilities of figure of merits of interest for engineering and safety (e.g. core damage probability, etc.). This approach applied to complex systems such as nuclear power plants requires performing a series of computationally expensive simulation runs. The large computational burden is caused by the large set of (uncertain) parameters characterizing those systems. Consequently exploring the uncertain/ parametric domain, with a good level of confidence, is generally not affordable, considering the limited computational resources that are currently available. In addition, the recent tendency to develop newer tools, characterized by higher accuracy and larger computational resources (if compared with the presently used legacy codes, that have been developed decades ago), has made this issue even more compelling. In order to overcome to these limitations, the strategy for the exploration of the uncertain/parametric space needs to use at best the computational resources focusing the computational effort in those regions of the uncertain/parametric space that are "interesting" (e.g., risk-significant regions of the input space) with respect the targeted Figure Of Merits (FOM): for example, the failure of the system, subject of the analysis. These methodologies are named, in the RAVEN environment, adaptive sampling strategies. These methodologies infer system responses from surrogate models constructed from already existing samples (produced using high fidelity simulations) and suggest the most relevant location (coordinate in the input space) of the next sampling point to be explored in the uncertain/parametric domain. When using those methodologies, it is possible to understand features of the system response with a small number of carefully selected samples. This report focuses on the development and improvement of the limit surface search. The limit surface is an important concept in system reliability analysis. Without going into the details, which will be covered later in the report, the limit surface could be briefly described as an hyper-surface in the system uncertainty/parametric space separating the regions leading to a prescribed system outcome. For example, if the uncertainty/parametric space is the one generated by the reactor power level and the duration of the batteries, the system is a nuclear power plant and the system outcome discriminating variable is the clad failure in a station blackout scenario, then the limit surface separates the combinations of reactor power level and battery duration that lead to clad failure form the one the does not.

Download or read book Risk Analysis in Engineering written by Mohammad Modarres and published by CRC Press. This book was released on 2016-04-27 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the author's 20 years of teaching, Risk Analysis in Engineering: Techniques, Tools, and Trends presents an engineering approach to probabilistic risk analysis (PRA). It emphasizes methods for comprehensive PRA studies, including techniques for risk management. The author assumes little or no prior knowledge of risk analysis on the p

Download or read book Risk Assessment written by Lee T. Ostrom and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guides the reader through a risk assessment and shows them the proper tools to be used at the various steps in the process This brand new edition of one of the most authoritative books on risk assessment adds ten new chapters to its pages to keep readers up to date with the changes in the types of risk that individuals, businesses, and governments are being exposed to today. It leads readers through a risk assessment and shows them the proper tools to be used at various steps in the process. The book also provides readers with a toolbox of techniques that can be used to aid them in analyzing conceptual designs, completed designs, procedures, and operational risk. Risk Assessment: Tools, Techniques, and Their Applications, Second Edition includes expanded case studies and real life examples; coverage on risk assessment software like SAPPHIRE and RAVEN; and end-of-chapter questions for students. Chapters progress from the concept of risk, through the simple risk assessment techniques, and into the more complex techniques. In addition to discussing the techniques, this book presents them in a form that the readers can readily adapt to their particular situation. Each chapter, where applicable, presents the technique discussed in that chapter and demonstrates how it is used. Expands on case studies and real world examples, so that the reader can see complete examples that demonstrate how each of the techniques can be used in analyzing a range of scenarios Includes 10 new chapters, including Bayesian and Monte Carlo Analyses; Hazard and Operability (HAZOP) Analysis; Threat Assessment Techniques; Cyber Risk Assessment; High Risk Technologies; Enterprise Risk Management Techniques Adds end-of-chapter questions for students, and provides a solutions manual for academic adopters Acts as a practical toolkit that can accompany the practitioner as they perform a risk assessment and allows the reader to identify the right assessment for their situation Presents risk assessment techniques in a form that the readers can readily adapt to their particular situation Risk Assessment: Tools, Techniques, and Their Applications, Second Edition is an important book for professionals that make risk-based decisions for their companies in various industries, including the insurance industry, loss control, forensics, all domains of safety, engineering and technical fields, management science, and decision analysis. It is also an excellent standalone textbook for a risk assessment or a risk management course.

Download or read book Risk Assessment written by Lee T. Ostrom and published by John Wiley & Sons. This book was released on 2019-07-30 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guides the reader through a risk assessment and shows them the proper tools to be used at the various steps in the process This brand new edition of one of the most authoritative books on risk assessment adds ten new chapters to its pages to keep readers up to date with the changes in the types of risk that individuals, businesses, and governments are being exposed to today. It leads readers through a risk assessment and shows them the proper tools to be used at various steps in the process. The book also provides readers with a toolbox of techniques that can be used to aid them in analyzing conceptual designs, completed designs, procedures, and operational risk. Risk Assessment: Tools, Techniques, and Their Applications, Second Edition includes expanded case studies and real life examples; coverage on risk assessment software like SAPPHIRE and RAVEN; and end-of-chapter questions for students. Chapters progress from the concept of risk, through the simple risk assessment techniques, and into the more complex techniques. In addition to discussing the techniques, this book presents them in a form that the readers can readily adapt to their particular situation. Each chapter, where applicable, presents the technique discussed in that chapter and demonstrates how it is used. Expands on case studies and real world examples, so that the reader can see complete examples that demonstrate how each of the techniques can be used in analyzing a range of scenarios Includes 10 new chapters, including Bayesian and Monte Carlo Analyses; Hazard and Operability (HAZOP) Analysis; Threat Assessment Techniques; Cyber Risk Assessment; High Risk Technologies; Enterprise Risk Management Techniques Adds end-of-chapter questions for students, and provides a solutions manual for academic adopters Acts as a practical toolkit that can accompany the practitioner as they perform a risk assessment and allows the reader to identify the right assessment for their situation Presents risk assessment techniques in a form that the readers can readily adapt to their particular situation Risk Assessment: Tools, Techniques, and Their Applications, Second Edition is an important book for professionals that make risk-based decisions for their companies in various industries, including the insurance industry, loss control, forensics, all domains of safety, engineering and technical fields, management science, and decision analysis. It is also an excellent standalone textbook for a risk assessment or a risk management course.

Download or read book Developing and Implementing the Data Mining Algorithms in RAVEN written by and published by . This book was released on 2015 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discovers knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.

Download or read book Dynamic Event Tree Analysis Through RAVEN written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also know as Dynamic Probabilistic Risk Assessment (D-PRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used D-PRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). In the past two years, the Idaho National Laboratory (INL) has developed its own tool to perform Dynamic PRA: RAVEN (Reactor Analysis and Virtual control ENvironment). RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other application including the ones based on the MOOSE framework, developed by INL as well. RAVEN performs two main tasks: 1) control logic driver for the new Thermo-Hydraulic code RELAP-7 and 2) post-processing tool. In the first task, RAVEN acts as a deterministic controller in which the set of control logic laws (user defined) monitors the RELAP-7 simulation and controls the activation of specific systems. Moreover, RAVEN also models stochastic events, such as components failures, and performs uncertainty quantification. Such stochastic modeling is employed by using both MC and DET algorithms. In the second task, RAVEN processes the large amount of data generated by RELAP-7 using data-mining based algorithms. This paper focuses on the first task and shows how it is possible to perform the analysis of dynamic stochastic systems using the newly developed RAVEN DET capability. As an example, the Dynamic PRA analysis, using Dynamic Event Tree, of a simplified pressurized water reactor for a Station Black-Out scenario is presented.

Download or read book RAVEN User Manual written by and published by . This book was released on 2015 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

Download or read book Probabilistic Risk Assessment written by and published by . This book was released on 2000 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains references to documents in the NASA Scientific and Technical (STI) Database.

Download or read book Safety and Reliability Methodology and Applications written by Tomasz Nowakowski and published by CRC Press. This book was released on 2014-09-01 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the last fifty years the performance requirements for technical objects and systems were supplemented with: customer expectations (quality), abilities to prevent the loss of the object properties in operation time (reliability and maintainability), protection against the effects of undesirable events (safety and security) and the ability to

Download or read book Risk informed Methods and Applications in Nuclear and Energy Engineering written by Curtis Smith and published by Academic Press. This book was released on 2023-11-16 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk-informed Methods and Applications in Nuclear and Energy Engineering: Modelling, Experimentation, and Validation presents a comprehensive view of the latest technical approaches and experimental capabilities in nuclear energy engineering. Based on Idaho National Laboratory's popular summer school series, this book compiles a collection of entries on the cutting-edge research and knowledge presented by proponents and developers of current and future nuclear systems, focusing on the connection between modelling and experimental approaches. Included in this book are key topics such as probabilistic concepts for risk analysis, the survey of legacy reliability and risk analysis tools, and newly developed tools supporting dynamic probabilistic risk-assessment. This book is an insightful and inspiring compilation of work from top nuclear experts from INL. Industry professionals, researchers and academics working in nuclear engineering, safety, operations and training will gain a board picture of the current state-of-practice and be able to apply that to their own risk-assessment studies. - Based on Idaho National Laboratory's summer school series, this book is a collection of entries from proponents and developers of current and future nuclear systems - Provides an up-to-date view of current technical approaches and experimental capabilities in nuclear energy engineering, covering modeling and validation, and focusing on risk-informed methods and applications - Equips the reader with an understanding of various case studies and experimental validations to enable them to carry out a risk-assessment study

Download or read book RAVEN a New Software for Dynamic Risk Analysis written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: RAVEN is a generic software driver to perform parametric and probabilistic analysis of code simulating complex systems. Initially developed to provide dynamic risk analysis capabilities to the RELAP-7 code [1] is currently being generalized with the addition of Application Programming Interfaces (APIs). These interfaces are used to extend RAVEN capabilities to any software as long as all the parameters that need to be perturbed are accessible by inputs files or directly via python interfaces. RAVEN is capable to investigate the system response probing the input space using Monte Carlo, grid strategies, or Latin Hyper Cube schemes, but its strength is its focus toward system feature discovery like limit surfaces separating regions of the input space leading to system failure using dynamic supervised learning techniques. The paper will present an overview of the software capabilities and their implementation schemes followed by same application examples.

Download or read book RAVEN written by and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Increases in computational power and pressure for more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and computational power address the back end of this challenge, the front end is still handled by engineers that need to extract meaningful information from the large amount of data and build these complex models. Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of software development. The above-described issues would have negatively impacted deployment of the new high fidelity plant simulator RELAP-7 (Reactor Excursion and Leak Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the plant controller for RELAP-7 will help mitigate future RELAP-7 software engineering risks. In order to accomplish this task, Reactor Analysis and Virtual Control Environment (RAVEN) has been designed to provide an easy to use Graphical User Interface (GUI) for building plant models and to leverage artificial intelligence algorithms in order to reduce computational time, improve results, and help the user to identify the behavioral pattern of the Nuclear Power Plants (NPPs). In this paper we will present the GUI implementation and its current capability status. We will also introduce the support vector machine algorithms and show our evaluation of their potentiality in increasing the accuracy and reducing the computational costs of PRA analysis. In this evaluation we will refer to preliminary studies performed under the Risk Informed Safety Margins Characterization (RISMC) project of the Light Water Reactors Sustainability (LWRS) campaign [3]. RISMC simulation needs and algorithm testing are currently used as a guidance to prioritize RAVEN developments relevant to PRA.