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 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 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 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 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 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 Online Probabilistic Risk Assessment of Complex Marine Systems written by Tarannom Parhizkar and published by Springer Nature. This book was released on 2021-11-26 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a new approach to dynamic and online risk assessment of automated and autonomous marine systems, taking into account different environmental and operational conditions. The book presents lessons learnt from dynamic positioning incidents and accidents, and discusses the challenges of risk assessment of complex systems. The book begins by introducing dynamic and online risk assessment, before presenting automated and autonomous marine systems, as well as numerous dynamic positioning incidents. It then discusses human interactions with technology and explores how to quantify human error. Dynamic probabilistic risk assessment and online risk assessment are both considered fully, including case studies with the application of assisting operators in decision making in emergency situations. Finally, areas for future research are suggested. This practical volume offers tools and methodologies to help operators make better decisions and improve the safety of automated and autonomous marine systems. It provides a guideline for researchers and practitioners to perform dynamic probabilistic and online risk assessment, which also should be applicable to other complex systems outside the marine and maritime domain, such as nuclear power plants, chemical processes, autonomous transport systems, and space shuttles.

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 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.

Download or read book A Guided Simulation Methodology for Dynamic Probabilistic Risk Assessment of Complex Systems written by Yunwei Hu and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 Dynamic Probabilistic Risk Assessment Using Event Sequence Diagrams written by S. Swaminathan and published by . This book was released on 1999 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Advanced Concepts In Nuclear Energy Risk Assessment And Management written by Tunc Aldemir and published by World Scientific. This book was released on 2018-04-25 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 30 years, numerous concerns have been raised in the literature regarding the capability of static modeling approaches such as the event-tree (ET)/fault-tree (FT) methodology to adequately account for the impact of process/hardware/software/firmware/human interactions on nuclear power plant safety assessment, and methodologies to augment the ET/FT approach have been proposed. Often referred to as dynamic probabilistic risk/safety assessment (DPRA/DPSA) methodologies, which use a time-dependent phenomenological model of system evolution along with a model of its stochastic behavior to model for possible dependencies among failure events. The book contains a collection of papers that describe at existing plant level applicable DPRA/DPSA tools, as well as techniques that can be used to augment the ET/FT approach when needed.

Download or read book Safety and Reliability Theory and Applications written by Marko Cepin and published by CRC Press. This book was released on 2017-06-14 with total page 6847 pages. Available in PDF, EPUB and Kindle. Book excerpt: Safety and Reliability – Theory and Applications contains the contributions presented at the 27th European Safety and Reliability Conference (ESREL 2017, Portorož, Slovenia, June 18-22, 2017). The book covers a wide range of topics, including: • Accident and Incident modelling • Economic Analysis in Risk Management • Foundational Issues in Risk Assessment and Management • Human Factors and Human Reliability • Maintenance Modeling and Applications • Mathematical Methods in Reliability and Safety • Prognostics and System Health Management • Resilience Engineering • Risk Assessment • Risk Management • Simulation for Safety and Reliability Analysis • Structural Reliability • System Reliability, and • Uncertainty Analysis. Selected special sessions include contributions on: the Marie Skłodowska-Curie innovative training network in structural safety; risk approaches in insurance and fi nance sectors; dynamic reliability and probabilistic safety assessment; Bayesian and statistical methods, reliability data and testing; oganizational factors and safety culture; software reliability and safety; probabilistic methods applied to power systems; socio-technical-economic systems; advanced safety assessment methodologies: extended Probabilistic Safety Assessment; reliability; availability; maintainability and safety in railways: theory & practice; big data risk analysis and management, and model-based reliability and safety engineering. Safety and Reliability – Theory and Applications will be of interest to professionals and academics working in a wide range of industrial and governmental sectors including: Aeronautics and Aerospace, Automotive Engineering, Civil Engineering, Electrical and Electronic Engineering, Energy Production and Distribution, Environmental Engineering, Information Technology and Telecommunications, Critical Infrastructures, Insurance and Finance, Manufacturing, Marine Industry, Mechanical Engineering, Natural Hazards, Nuclear Engineering, Offshore Oil and Gas, Security and Protection, Transportation, and Policy Making.