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  1. RAVEN Quality Assurance Activities

    SciTech Connect (OSTI)

    Cogliati, Joshua Joseph

    2015-09-01

    This report discusses the quality assurance activities needed to raise the Quality Level of Risk Analysis in a Virtual Environment (RAVEN) from Quality Level 3 to Quality Level 2. This report also describes the general RAVEN quality assurance activities. For improving the quality, reviews of code changes have been instituted, more parts of testing have been automated, and improved packaging has been created. For upgrading the quality level, requirements have been created and the workflow has been improved.

  2. RAVEN User Manual

    SciTech Connect (OSTI)

    Mandelli, Diego; Rabiti, Cristian; Cogliati, Joshua Joseph; Kinoshita, Robert Arthur; Alfonsi, Andrea; Sen, Ramazan Sonat

    2015-10-01

    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

  3. Software infrastructure progress in the RAVEN code

    SciTech Connect (OSTI)

    Cogliati, Joshua J.; Rabiti, Cristian; Permann, Cody J.

    2015-03-01

    The milestones have been achieved. RAVEN has been migrated to Gitlab which adds new abilities for code review and management. Standalone RAVEN framework packages have been created for OSX and two Linux distributions.

  4. RAVEN, a New Software for Dynamic Risk Analysis (Conference)...

    Office of Scientific and Technical Information (OSTI)

    RAVEN, a New Software for Dynamic Risk Analysis Citation Details In-Document Search Title: RAVEN, a New Software for Dynamic Risk Analysis RAVEN is a generic software driver to ...

  5. Raven Biofuels International Corporation | Open Energy Information

    Open Energy Info (EERE)

    Biofuels International Corporation Jump to: navigation, search Name: Raven Biofuels International Corporation Place: Paramus, New Jersey Zip: 07652-1236 Sector: Biofuels Product:...

  6. Deployment and Overview of RAVEN capabilities for (Technical...

    Office of Scientific and Technical Information (OSTI)

    Deployment and Overview of RAVEN capabilities for Citation Details In-Document Search Title: Deployment and Overview of RAVEN capabilities for Since the Beginning of 2012 Idaho ...

  7. RAVEN and Dynamic Probabilistic Risk Assessment: Software overview...

    Office of Scientific and Technical Information (OSTI)

    RAVEN and Dynamic Probabilistic Risk Assessment: Software overview Citation Details In-Document Search Title: RAVEN and Dynamic Probabilistic Risk Assessment: Software overview ...

  8. Adaptive Dynamic Event Tree in RAVEN code

    SciTech Connect (OSTI)

    Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Cogliati, Joshua Joseph; Kinoshita, Robert Arthur

    2014-11-01

    RAVEN is a software tool that is focused on performing statistical analysis of stochastic dynamic systems. 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 applications (system codes). Among the several capabilities currently present in RAVEN, there are five different sampling strategies: Monte Carlo, Latin Hyper Cube, Grid, Adaptive and Dynamic Event Tree (DET) sampling methodologies. The scope of this paper is to present a new sampling approach, currently under definition and implementation: an evolution of the DET me

  9. Dynamic Event Tree Analysis Through RAVEN

    SciTech Connect (OSTI)

    A. Alfonsi; C. Rabiti; D. Mandelli; J. Cogliati; R. A. Kinoshita; A. Naviglio

    2013-09-01

    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.

  10. Performing Probabilistic Risk Assessment Through RAVEN

    SciTech Connect (OSTI)

    A. Alfonsi; C. Rabiti; D. Mandelli; J. Cogliati; R. Kinoshita

    2013-06-01

    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

  11. Analysis of the Space Propulsion System Problem Using RAVEN ...

    Office of Scientific and Technical Information (OSTI)

    RAVEN (Reactor Analysis and Virtual control ENviroment) is a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities. ...

  12. RAVEN, a New Software for Dynamic Risk Analysis

    SciTech Connect (OSTI)

    Cristian Rabiti; Andrea Alfonsi; Joshua Cogliati; Diego Mandelli; Robert Kinoshita

    2014-06-01

    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.

  13. DAKOTA reliability methods applied to RAVEN/RELAP-7.

    SciTech Connect (OSTI)

    Swiler, Laura Painton; Mandelli, Diego; Rabiti, Cristian; Alfonsi, Andrea

    2013-09-01

    This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR).

  14. Developing and Implementing the Data Mining Algorithms in RAVEN

    SciTech Connect (OSTI)

    Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea; Rabiti, Cristian

    2015-09-01

    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 discover 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.

  15. RAVEN and Dynamic Probabilistic Risk Assessment: Software overview

    SciTech Connect (OSTI)

    Andrea Alfonsi; Cristian Rabiti; Diego Mandelli; Joshua Cogliati; Robert Kinoshita; Antonio Naviglio

    2014-09-01

    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.

  16. Implementation of Stochastic Polynomials Approach in the RAVEN Code

    SciTech Connect (OSTI)

    Cristian Rabiti; Paul Talbot; Andrea Alfonsi; Diego Mandelli; Joshua Cogliati

    2013-10-01

    RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, has been tasked to provide the necessary software and algorithms to enable the application of the conceptual framework developed by the Risk Informed Safety Margin Characterization (RISMC) [1] path. RISMC is one of the paths defined under the Light Water Reactor Sustainability (LWRS) DOE program.

  17. RAVEN. Dynamic Event Tree Approach Level III Milestone

    SciTech Connect (OSTI)

    Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Cogliati, Joshua; Kinoshita, Robert

    2014-07-01

    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.

  18. RAVEN: Dynamic Event Tree Approach Level III Milestone

    SciTech Connect (OSTI)

    Andrea Alfonsi; Cristian Rabiti; Diego Mandelli; Joshua Cogliati; Robert Kinoshita

    2013-07-01

    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.

  19. REACTOR ANALYSIS AND VIRTUAL CONTROL ENVIRONMENT (RAVEN) FY12 REPORT

    SciTech Connect (OSTI)

    Cristian Rabiti; Andrea Alfonsi; Joshua Cogliati; Diego Mandelli; Robert Kinoshita

    2012-09-01

    RAVEN is a complex software tool that will have tasks spanning from being the RELAP-7 user interface, to using RELAP-7 to perform Risk Informed Safety Characterization (RISMC), and to controlling RELAP-7 calculation execution. The goal of this document is to: 1. Highlight the functional requirements of the different tasks of RAVEN 2. Identify shared functions that could be aggregate in modules so to obtain a minimal software redundancy and maximize software utilization. RAVEN is in fact a software framework that will allow exploiting the following functionalities: Derive and actuate the control logic required to: o Simulate the plant control system o Simulate the operator (procedure guided) actions o Perform Monte Carlo sampling of random distributed events o Perform event three based analysis Provide a GUI to: o Input a plant description to RELAP-7 (component, control variable, control parameters) o Concurrent monitoring of Control Parameters o Concurrent alteration of control parameters Provide Post Processing data mining capability based on o Dimensionality reduction o Cardinality reduction In this document it will be shown how an appropriate mathematical formulation of the control logic and probabilistic analysis leads to have most of the software infrastructure leveraged between the two main tasks. Further, this document will go through the development accomplished this year, including simulation results, and priorities for the next years development

  20. Analysis of the Space Propulsion System Problem Using RAVEN

    SciTech Connect (OSTI)

    diego mandelli; curtis smith; cristian rabiti; andrea alfonsi

    2014-06-01

    This paper presents the solution of the space propulsion problem using a PRA code currently under development at Idaho National Laboratory (INL). RAVEN (Reactor Analysis and Virtual control ENviroment) is a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities. It is designed to derive and actuate the control logic required to simulate the plant control system and operator actions (guided procedures) and to perform both Monte- Carlo sampling of random distributed events and Event Tree based analysis. In order to facilitate the input/output handling, a Graphical User Interface (GUI) and a post-processing data-mining module are available. RAVEN allows also to interface with several numerical codes such as RELAP5 and RELAP-7 and ad-hoc system simulators. For the space propulsion system problem, an ad-hoc simulator has been developed and written in python language and then interfaced to RAVEN. Such simulator fully models both deterministic (e.g., system dynamics and interactions between system components) and stochastic behaviors (i.e., failures of components/systems such as distribution lines and thrusters). Stochastic analysis is performed using random sampling based methodologies (i.e., Monte-Carlo). Such analysis is accomplished to determine both the reliability of the space propulsion system and to propagate the uncertainties associated to a specific set of parameters. As also indicated in the scope of the benchmark problem, the results generated by the stochastic analysis are used to generate risk-informed insights such as conditions under witch different strategy can be followed.

  1. Advanced probabilistic risk analysis using RAVEN and RELAP-7

    SciTech Connect (OSTI)

    Rabiti, Cristian; Alfonsi, Andrea; Mandelli, Diego; Cogliati, Joshua; Kinoshita, Robert

    2014-06-01

    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

  2. Mathematical framework for the analysis of dynamic stochastic systems with the RAVEN code

    SciTech Connect (OSTI)

    Rabiti, C.; Mandelli, D.; Alfonsi, A.; Cogliati, J.; Kinoshita, R.

    2013-07-01

    RAVEN (Reactor Analysis and Virtual control Environment) is a software code under development at Idaho National Laboratory aimed at performing probabilistic risk assessment and uncertainty quantification using RELAP-7, for which it acts also as a simulation controller. In this paper we will present the equations characterizing a dynamic stochastic system and we will then discuss the behavior of each stochastic term and how it is accounted for in the RAVEN software design. Moreover we will present preliminary results of the implementation. (authors)

  3. MATHEMATICAL FRAMEWORK FOR THE ANALYSIS OF DYNAMC STOCHASTIC SYSTEMS WITH THE RAVEN CODE

    SciTech Connect (OSTI)

    C. Rabiti; D. Mandelli; J. Cogliati; R. Kinoshita

    2013-05-01

    RAVEN (Reactor Analysis and Virtual control Environment) is a software code under development at Idaho National Laboratory aimed at performing probabilistic risk assessment and uncertainty quantification using RELAP-7, for which it acts also as a simulation controller. In this paper we will present the equations characterizing a dynamic stochastic system and we will then discuss the behavior of each stochastic term and how it is accounted for in the RAVEN software design. Moreover we will present preliminary results of the implementation.

  4. RAVEN as a tool for dynamic probabilistic risk assessment: Software overview

    SciTech Connect (OSTI)

    Alfonsi, A.; Rabiti, C.; Mandelli, D.; Cogliati, J. J.; Kinoshita, R. A.

    2013-07-01

    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 Thermal-Hydraulic 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. (authors)

  5. RAVEN AS A TOOL FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT: SOFTWARE OVERVIEW

    SciTech Connect (OSTI)

    Alfonsi Andrea; Mandelli Diego; Rabiti Cristian; Joshua Cogliati; Robert Kinoshita

    2013-05-01

    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.

  6. RAVEN: a GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

    SciTech Connect (OSTI)

    C. Rabiti; D. Mandelli; A. Alfonsi; J. Cogliati; R. Kinoshita; D. Gaston; R. Martineau; C. Curtis

    2013-06-01

    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.

  7. Initial Probabilistic Evaluation of Reactor Pressure Vessel Fracture with Grizzly and Raven

    SciTech Connect (OSTI)

    Spencer, Benjamin; Hoffman, William; Sen, Sonat; Rabiti, Cristian; Dickson, Terry; Bass, Richard

    2015-10-01

    The Grizzly code is being developed with the goal of creating a general tool that can be applied to study a variety of degradation mechanisms in nuclear power plant components. The first application of Grizzly has been to study fracture in embrittled reactor pressure vessels (RPVs). Grizzly can be used to model the thermal/mechanical response of an RPV under transient conditions that would be observed in a pressurized thermal shock (PTS) scenario. The global response of the vessel provides boundary conditions for local models of the material in the vicinity of a flaw. Fracture domain integrals are computed to obtain stress intensity factors, which can in turn be used to assess whether a fracture would initiate at a pre-existing flaw. These capabilities have been demonstrated previously. A typical RPV is likely to contain a large population of pre-existing flaws introduced during the manufacturing process. This flaw population is characterized stastistically through probability density functions of the flaw distributions. The use of probabilistic techniques is necessary to assess the likelihood of crack initiation during a transient event. This report documents initial work to perform probabilistic analysis of RPV fracture during a PTS event using a combination of the RAVEN risk analysis code and Grizzly. This work is limited in scope, considering only a single flaw with deterministic geometry, but with uncertainty introduced in the parameters that influence fracture toughness. These results are benchmarked against equivalent models run in the FAVOR code. When fully developed, the RAVEN/Grizzly methodology for modeling probabilistic fracture in RPVs will provide a general capability that can be used to consider a wider variety of vessel and flaw conditions that are difficult to consider with current tools. In addition, this will provide access to advanced probabilistic techniques provided by RAVEN, including adaptive sampling and parallelism, which can dramatically

  8. Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes: Level II Milestone

    SciTech Connect (OSTI)

    Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Cogliati, Joshua Joseph; Sen, Ramazan Sonat; Smith, Curtis Lee

    2015-07-01

    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 use 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 figures of merit 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 Figures Of Merit (FOM): for example, the failure of the system

  9. Methodology for the Incorporation of Passive Component Aging Modeling into the RAVEN/ RELAP-7 Environment

    SciTech Connect (OSTI)

    Mandelli, Diego; Rabiti, Cristian; Cogliati, Joshua; Alfonsi, Andrea; Askin Guler; Tunc Aldemir

    2014-11-01

    Passive system, structure and components (SSCs) will degrade over their operation life and this degradation may cause to reduction in the safety margins of a nuclear power plant. In traditional probabilistic risk assessment (PRA) using the event-tree/fault-tree methodology, passive SSC failure rates are generally based on generic plant failure data and the true state of a specific plant is not reflected realistically. To address aging effects of passive SSCs in the traditional PRA methodology [1] does consider physics based models that account for the operating conditions in the plant, however, [1] does not include effects of surveillance/inspection. This paper represents an overall methodology for the incorporation of aging modeling of passive components into the RAVEN/RELAP-7 environment which provides a framework for performing dynamic PRA. Dynamic PRA allows consideration of both epistemic and aleatory uncertainties (including those associated with maintenance activities) in a consistent phenomenological and probabilistic framework and is often needed when there is complex process/hardware/software/firmware/ human interaction [2]. Dynamic PRA has gained attention recently due to difficulties in the traditional PRA modeling of aging effects of passive components using physics based models and also in the modeling of digital instrumentation and control systems. RAVEN (Reactor Analysis and Virtual control Environment) [3] is a software package under development at the Idaho National Laboratory (INL) as an online control logic driver and post-processing tool. It is coupled to the plant transient code RELAP-7 (Reactor Excursion and Leak Analysis Program) also currently under development at INL [3], as well as RELAP 5 [4]. The overall methodology aims to: • Address multiple aging mechanisms involving large number of components in a computational feasible manner where sequencing of events is conditioned on the physical conditions predicted in a simulation

  10. BWR station blackout: A RISMC analysis using RAVEN and RELAP5-3D

    SciTech Connect (OSTI)

    Mandelli, D.; Smith, C.; Riley, T.; Nielsen, J.; Alfonsi, A.; Cogliati, J.; Rabiti, C.; Schroeder, J.

    2016-01-01

    The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates and improved operations. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) project aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions and accident scenarios. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess impact of power uprate of a Boiling Water Reactor system during a Station Black-Out accident scenario. We employ a system simulator code, RELAP5-3D, coupled with RAVEN which perform the stochastic analysis. Lastly, our analysis is performed by: 1) sampling values from a set of parameters from the uncertainty space of interest, 2) simulating the system behavior for that specific set of parameter values and 3) analyzing the outcomes from the set of simulation runs.

  11. BWR station blackout: A RISMC analysis using RAVEN and RELAP5-3D

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Mandelli, D.; Smith, C.; Riley, T.; Nielsen, J.; Alfonsi, A.; Cogliati, J.; Rabiti, C.; Schroeder, J.

    2016-01-01

    The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates and improved operations. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) project aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions and accident scenarios. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess impact of power uprate of a Boiling Watermore » Reactor system during a Station Black-Out accident scenario. We employ a system simulator code, RELAP5-3D, coupled with RAVEN which perform the stochastic analysis. Lastly, our analysis is performed by: 1) sampling values from a set of parameters from the uncertainty space of interest, 2) simulating the system behavior for that specific set of parameter values and 3) analyzing the outcomes from the set of simulation runs.« less

  12. BWR station blackout: A RISMC analysis using RAVEN and RELAP5-3D

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Mandelli, D.; Smith, C.; Riley, T.; Nielsen, J.; Alfonsi, A.; Cogliati, J.; Rabiti, C.; Schroeder, J.

    2016-01-01

    The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates and improved operations. In order to evaluate the impact of these factors on the safety of the plant, the Risk-Informed Safety Margin Characterization (RISMC) project aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions and accident scenarios. This paper presents a case study in order to show the capabilities of the RISMC methodology to assess impact of power uprate of a Boiling Watermore » Reactor system during a Station Black-Out accident scenario. We employ a system simulator code, RELAP5-3D, coupled with RAVEN which perform the stochastic analysis. Furthermore, our analysis is performed by: 1) sampling values from a set of parameters from the uncertainty space of interest, 2) simulating the system behavior for that specific set of parameter values and 3) analyzing the outcomes from the set of simulation runs.« less

  13. Light Water Reactor Sustainability Program Support and Modeling for the Boiling Water Reactor Station Black Out Case Study Using RELAP and RAVEN

    SciTech Connect (OSTI)

    Diego Mandelli; Curtis Smith; Thomas Riley; John Schroeder; Cristian Rabiti; Aldrea Alfonsi; Joe Nielsen; Dan Maljovec; Bie Wang; Valerio Pascucci

    2013-09-01

    The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated. In order to evaluate the impact of these two factors on the safety of the plant, the Risk Informed Safety Margin Characterization (RISMC) project aims to provide insight to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This report focuses, in particular, on the impact of power uprate on the safety of a boiled water reactor system. The case study considered is a loss of off-site power followed by the loss of diesel generators, i.e., a station black out (SBO) event. Analysis is performed by using a thermo-hydraulic code, i.e. RELAP-5, and a stochastic analysis tool currently under development at INL, i.e. RAVEN. Starting from the event tree models contained in SAPHIRE, we built the input file for RELAP-5 that models in great detail system dynamics under SBO conditions. We also interfaced RAVEN with RELAP-5 so that it would be possible to run multiple RELAP-5 simulation runs by changing specific keywords of the input file. We both employed classical statistical tools, i.e. Monte-Carlo, and more advanced machine learning based algorithms to perform uncertainty quantification in order to quantify changes in system performance and limitations as a consequence of power uprate. We also employed advanced data analysis and visualization tools that helped us to correlate simulation outcome such as maximum core temperature with a set of input uncertain parameters. Results obtained gave a detailed overview of the issues associated to power uprate for a SBO accident scenario. We were able to quantify how timing of safety related events were impacted by a higher reactor core power. Such insights can provide useful material to the decision makers to perform risk-infomed safety margins management.

  14. Raven Technology | Open Energy Information

    Open Energy Info (EERE)

    technology known as "AC-Direct," which seeks to overcome the limitations of inverters and synchronous generators for mobile, off-grid, and distributed power applications....

  15. RavenBrick LLC | Open Energy Information

    Open Energy Info (EERE)

    Denver, Colorado Zip: 80205 Region: Rockies Area Sector: Buildings Product: Efficient window and daylighting systems Website: www.ravenbrick.com Coordinates: 39.754373,...

  16. Implementation of Stochastic Polynomials Approach in the RAVEN...

    Office of Scientific and Technical Information (OSTI)

    to provide the necessary software and algorithms to enable the application of the conceptual framework developed by the Risk Informed Safety Margin Characterization (RISMC) 1 path. ...

  17. RAVEN: Dynamic Event Tree Approach Level III Milestone (Technical...

    Office of Scientific and Technical Information (OSTI)

    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. ...

  18. RAVEN as Control Logic and Probabilistic Risk Assessment Driver for RELAP-7

    SciTech Connect (OSTI)

    C. Rabiti; A. Alfonsi; D. Mandelli; J. Cogliati; R. Martineau

    2012-11-01

    The Next Generation of System Analysis Code (NGSAC) [1] aims to model and simulate the Nuclear Power Plant (NPP) thermo-hydraulic behavior with high level of accuracy. In this respect, Idaho National Laboratory (INL) is developing a NGSAC (known as RELAP-7) which will allow to model NPP responses for a set of accident scenarios (e.g., loss of off-site power).

  19. New Jersey's 5th congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    5th congressional district BGA Engineering LLC Building Performance Equipment, Inc. HERA USA Inc formerly Ergenics Inc Pure Energy Corporation PEC Raven Biofuels International...

  20. Russell County, Virginia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Virginia Castlewood, Virginia Cleveland, Virginia Honaker, Virginia Lebanon, Virginia Raven, Virginia St. Paul, Virginia Retrieved from "http:en.openei.orgw...

  1. Alamance County, North Carolina: Energy Resources | Open Energy...

    Open Energy Info (EERE)

    Burlington, North Carolina Elon, North Carolina Gibsonville, North Carolina Glen Raven, North Carolina Graham, North Carolina Green Level, North Carolina Haw River, North...

  2. Tazewell County, Virginia: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Bluefield, Virginia Cedar Bluff, Virginia Claypool Hill, Virginia Pocahontas, Virginia Raven, Virginia Richlands, Virginia Tazewell, Virginia Retrieved from "http:en.openei.org...

  3. Bergen County, New Jersey: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hycrete Public Energy Solutions Pure Energy Corporation PEC RLR Consultants LLC Raven Biofuels International Corporation Resource Energy Systems LLC Reunion Power LLC...

  4. Paramus, New Jersey: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Registered Energy Companies in Paramus, New Jersey Pure Energy Corporation PEC Raven Biofuels International Corporation References US Census Bureau Incorporated place...

  5. Phil & Monica Noll

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    the official policy or position of Los Alamos National Laboratory. Resources To learn more about Phil and Monica Noll, visit the Phil and Monica Noll's Raven Mountain Images...

  6. Brunswick, Maine: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    County, Maine.1 Registered Energy Companies in Brunswick, Maine Independence Wind LLC Raven Technology References US Census Bureau Incorporated place and minor civil division...

  7. January 2013 Most Viewed Documents for Mathematics And Computing...

    Office of Scientific and Technical Information (OSTI)

    Cybersecurity through Real-Time Distributed Control Systems Kisner, Roger A ORNL; ... M ORNL REACTOR ANALYSIS AND VIRTUAL CONTROL ENVIRONMENT (RAVEN) FY12 REPORT Cristian ...

  8. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    summary written by Raven Hanna New rock formed by deep undersea volcanoes does not stay bare long. Microbes quickly move onto these basalts to form communities in the form of...

  9. Search for: All records | SciTech Connect

    Office of Scientific and Technical Information (OSTI)

    ... Center, Morgantown, WV (United States) Mound Area Office, Miamisburg, OH (United ... (2) raven (2) relap-7 (2) relap5-3d (2) thorium oxide (2) tru (2) accuracy (1) actinides ...

  10. SU-E-T-580: Comparison of Cervical Carcinoma IMRT Plans From Four Commercial Treatment Planning Systems (TPS)

    SciTech Connect (OSTI)

    Cao, Y; Li, R; Chi, Z; Zhu, S

    2014-06-01

    Purpose: Different treatment planning systems (TPS) use different treatment optimization and leaf sequencing algorithms. This work compares cervical carcinoma IMRT plans optimized with four commercial TPSs to investigate the plan quality in terms of target conformity and delivery efficiency. Methods: Five cervical carcinoma cases were planned with the Corvus, Monaco, Pinnacle and Xio TPSs by experienced planners using appropriate optimization parameters and dose constraints to meet the clinical acceptance criteria. Plans were normalized for at least 95% of PTV to receive the prescription dose (Dp). Dose-volume histograms and isodose distributions were compared. Other quantities such as Dmin(the minimum dose received by 99% of GTV/PTV), Dmax(the maximum dose received by 1% of GTV/PTV), D100, D95, D90, V110%, V105%, V100% (the volume of GTV/PTV receiving 110%, 105%, 100% of Dp), conformity index(CI), homogeneity index (HI), the volume of receiving 40Gy and 50 Gy to rectum (V40,V50) ; the volume of receiving 30Gy and 50 Gy to bladder (V30,V50) were evaluated. Total segments and MUs were also compared. Results: While all plans meet target dose specifications and normal tissue constraints, the maximum GTVCI of Pinnacle plans was up to 0.74 and the minimum of Corvus plans was only 0.21, these four TPSs PTVCI had significant difference. The GTVHI and PTVHI of Pinnacle plans are all very low and show a very good dose distribution. Corvus plans received the higer dose of normal tissue. The Monaco plans require significantly less segments and MUs to deliver than the other plans. Conclusion: To deliver on a Varian linear-accelerator, the Pinnacle plans show a very good dose distribution. Corvus plans received the higer dose of normal tissue. The Monaco plans have faster beam delivery.

  11. Microsoft Word - S05993_CY2009 Annual Rpt.doc

    Office of Legacy Management (LM)

    4 3.2.3.3 Additional Wildlife Observations In September, a dead crow (Corvus brachyrhynchos) was observed dangling from the top of a spruce tree at the Site. Closer inspection revealed there was fishing line wrapped around the foot of the bird. Evidently the crow picked up the fishing line on its foot at one of the nearby reservoirs (since fishing is not allowed at the Site). The line was probably dangling from its foot when it landed on the branch. When it attempted to take off the line

  12. SU-E-T-608: Performance Comparison of Four Commercial Treatment Planning Systems Applied to Intensity-Modulated Radiotherapy

    SciTech Connect (OSTI)

    Cao, Y; Li, R; Chi, Z

    2014-06-01

    Purpose: To compare the performances of four commercial treatment planning systems (TPS) used for the intensity-modulated radiotherapy (IMRT). Methods: Ten patients of nasopharyngeal (4 cases), esophageal (3 cases) and cervical (3 cases) cancer were randomly selected from a 3-month IMRT plan pool at one radiotherapy center. For each patient, four IMRT plans were newly generated by using four commercial TPS (Corvus, Monaco, Pinnacle and Xio), and then verified with Matrixx (two-dimensional array/IBA Company) on Varian23EX accelerator. A pass rate (PR) calculated from the Gamma index by OminiPro IMRT 1.5 software was evaluated at four plan verification standards (1%/1mm, 2%/2mm, 3%/3mm, 4%/4mm and 5%/5mm) for each treatment plan. Overall and multiple pairwise comparisons of PRs were statistically conducted by analysis of covariance (ANOVA) F and LSD tests among four TPSs. Results: Overall significant (p>0.05) differences of PRs were found among four TPSs with F test values of 3.8 (p=0.02), 21.1(>0.01), 14.0 (>0.01), 8.3(>0.01) at standards of 1%/1mm to 4%/4mm respectively, except at 5%/5mm standard with 2.6 (p=0.06). All means (standard deviation) of PRs at 3%/3mm of 94.3 3.3 (Corvus), 98.8 0.8 (Monaco), 97.5 1.7 (Pinnacle), 98.4 1.0 (Xio) were above 90% and met clinical requirement. Multiple pairwise comparisons had not demonstrated a consistent low or high pattern on either TPS. Conclusion: Matrixx dose verification results show that the validation pass rates of Monaco and Xio plans are relatively higher than those of the other two; Pinnacle plan shows slight higher pass rate than Corvus plan; lowest pass rate was achieved by the Corvus plan among these four kinds of TPS.

  13. A flooding induced station blackout analysis for a pressurized water reactor using the RISMC toolkit

    SciTech Connect (OSTI)

    Mandelli, Diego; Prescott, Steven; Smith, Curtis; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua; Kinoshita, Robert

    2015-05-17

    In this paper we evaluate the impact of a power uprate on a pressurized water reactor (PWR) for a tsunami-induced flooding test case. This analysis is performed using the RISMC toolkit: the RELAP-7 and RAVEN codes. RELAP-7 is the new generation of system analysis codes that is responsible for simulating the thermal-hydraulic dynamics of PWR and boiling water reactor systems. RAVEN has two capabilities: to act as a controller of the RELAP-7 simulation (e.g., component/system activation) and to perform statistical analyses. In our case, the simulation of the flooding is performed by using an advanced smooth particle hydrodynamics code called NEUTRINO. The obtained results allow the user to investigate and quantify the impact of timing and sequencing of events on system safety. The impact of power uprate is determined in terms of both core damage probability and safety margins.

  14. A flooding induced station blackout analysis for a pressurized water reactor using the RISMC toolkit

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Mandelli, Diego; Prescott, Steven; Smith, Curtis; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua; Kinoshita, Robert

    2015-05-17

    In this paper we evaluate the impact of a power uprate on a pressurized water reactor (PWR) for a tsunami-induced flooding test case. This analysis is performed using the RISMC toolkit: the RELAP-7 and RAVEN codes. RELAP-7 is the new generation of system analysis codes that is responsible for simulating the thermal-hydraulic dynamics of PWR and boiling water reactor systems. RAVEN has two capabilities: to act as a controller of the RELAP-7 simulation (e.g., component/system activation) and to perform statistical analyses. In our case, the simulation of the flooding is performed by using an advanced smooth particle hydrodynamics code calledmore » NEUTRINO. The obtained results allow the user to investigate and quantify the impact of timing and sequencing of events on system safety. The impact of power uprate is determined in terms of both core damage probability and safety margins.« less

  15. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    archaeopteryx » Links Scientific Highlight SLAC Press Release BBC News » Share this Article Laboratree Ologeez SciLink LabSpaces Archaeopteryx Feathers and Bone Chemistry Fully Revealed via Synchrotron Imaging summary written by Raven Hanna Archaeopteryx, the half-reptilian, half-avian creature that lived 150 million years ago is famous as the fossil record's link between dinosaurs and birds. The discovery of the first Archaeopteryx fossil, which coincided with the publication of Charles

  16. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    10 image Outside view of the T=4 subunit arrangement. » Links Scientific Highlight Johnson Lab » Share this Article Laboratree Ologeez SciLink LabSpaces Following the pH-dependent Conformational Changes of a Maturing Viral Capsid summary written by Raven Hanna The capsid that surrounds viruses is formed from subunit proteins that interact in specific ways to form a tight shell. The processes of coming together and forming interactions are multistep and complex and are fundamental events to

  17. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Kohen Research UI News Release SSRL Macromolecular Crystallography » Share this Article Laboratree Ologeez SciLink LabSpaces Novel Mechanism for DNA Biosynthesis in Organisms with Gene thyX could Lead to Better Antibiotics summary written by Raven Hanna Before DNA is made, the subunits composing DNA must be made. The essential process of making one of these subunits, thymidine monophosphate (TMP), was thought to be similar for most living things, but scientists recently discovered that some

  18. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Hasan Research Princeton News Release » Share this Article Laboratree Ologeez SciLink LabSpaces Macroscopic Quantum Insulator State Observed summary written by Raven Hanna One of the strangest consequences of quantum mechanics is the seemingly instantaneous communication of subatomic particles over long distances. Known as quantum entanglement, pairs or groups of particles can become linked so that any changes made to one will cause the others to respond quicker than the time it takes for light

  19. Risk Analysis Virtual ENvironment

    Energy Science and Technology Software Center (OSTI)

    2014-02-10

    RAVEN has 3 major functionalities: 1. Provides a Graphical User Interface for the pre- and post-processing of the RELAP-7 input and output. 2. Provides the capability to model nuclear power plants control logic for the RELAP-7 code and dynamic control of the accident scenario evolution. This capability is based on a software structure that realizes a direct connection between the RELAP-7 solver engine (MOOSE) and a python environment where the variables describing the plant statusmore » are accessible in a scripting environment. RAVEN support the generation of the probabilistic scenario control by supplying a wide range of probability and cumulative distribution functions and their inverse functions. 3. Provides a general environment to perform probability risk analysis for RELAP-7, RELAP-5 and any generic MOOSE based applications. The probabilistic analysis is performed by sampling the input space of the coupled code parameters and it is enhanced by using modern artificial intelligence algorithms that accelerate the identification of the areas of major risk (in the input parameter space). This environment also provides a graphical visualization capability to analyze the outcomes. Among other approaches, the classical Monte Carlo and Latin Hypercube sampling algorithms are available. For the acceleration of the convergence of the sampling methodologies, Support Vector Machines, Bayesian regression, and collocation stochastic polynomials chaos are implemented. The same methodologies here described could be used to solve optimization and uncertainties propagation problems using the RAVEN framework.« less

  20. Risk Analysis Virtual ENvironment

    SciTech Connect (OSTI)

    2014-02-10

    RAVEN has 3 major functionalities: 1. Provides a Graphical User Interface for the pre- and post-processing of the RELAP-7 input and output. 2. Provides the capability to model nuclear power plants control logic for the RELAP-7 code and dynamic control of the accident scenario evolution. This capability is based on a software structure that realizes a direct connection between the RELAP-7 solver engine (MOOSE) and a python environment where the variables describing the plant status are accessible in a scripting environment. RAVEN support the generation of the probabilistic scenario control by supplying a wide range of probability and cumulative distribution functions and their inverse functions. 3. Provides a general environment to perform probability risk analysis for RELAP-7, RELAP-5 and any generic MOOSE based applications. The probabilistic analysis is performed by sampling the input space of the coupled code parameters and it is enhanced by using modern artificial intelligence algorithms that accelerate the identification of the areas of major risk (in the input parameter space). This environment also provides a graphical visualization capability to analyze the outcomes. Among other approaches, the classical Monte Carlo and Latin Hypercube sampling algorithms are available. For the acceleration of the convergence of the sampling methodologies, Support Vector Machines, Bayesian regression, and collocation stochastic polynomials chaos are implemented. The same methodologies here described could be used to solve optimization and uncertainties propagation problems using the RAVEN framework.

  1. Sandia Generated Matrix Tool (SGMT) v. 1.0

    Energy Science and Technology Software Center (OSTI)

    2010-03-24

    Provides a tool with which create and characterize a very large set of matrix-based visual analogy problems that have properties that are similar to Raven™s Progressive Matrices (RPMs). The software uses the same underlying patterns found in RPMs to generate large numbers of unique matrix problems using parameters chosen by the researcher. Specifically, the software is designed so that researchers can choose the type, direction, and number of relations in a problem and then createmore » any number of unique matrices that share the same underlying structure (e.g. changes in numerosity in a diagonal pattern) but have different surface features (e.g. shapes, colors).Raven™s Progressive Matrices (RPMs) are a widely-used test for assessing intelligence and reasoning ability. Since the test is non-verbal, it can be applied to many different populations and has been used all over the world. However, there are relatively few matrices in the sets developed by Raven, which limits their use in experiments requiring large numbers of stimuli. This tool creates a matrix set in a systematic way that allows researchers to have a great deal of control over the underlying structure, surface features, and difficulty of the matrix problems while providing a large set of novel matrices with which to conduct experiments.« less

  2. Status on the Development of a Modeling and Simulation Framework for the Economic Assessment of Nuclear Hybrid Energy Systems

    SciTech Connect (OSTI)

    Bragg-Sitton, Shannon Michelle; Rabiti, Cristian; Kinoshita, Robert Arthur; Kim, Jong Suk; Deason, Wesley Ray; Boardman, Richard Doin; Garcia, Humberto E.

    2015-09-01

    An effort to design and build a modeling and simulation framework to assess the economic viability of Nuclear Hybrid Energy Systems (NHES) was undertaken in fiscal year 2015 (FY15). The purpose of this report is to document the various tasks associated with the development of such a framework and to provide a status on its progress. Several tasks have been accomplished. First, starting from a simulation strategy, a rigorous mathematical formulation has been achieved in which the economic optimization of a Nuclear Hybrid Energy System is presented as a constrained robust (under uncertainty) optimization problem. Some possible algorithms for the solution of the optimization problem are presented. A variation of the Simultaneous Perturbation Stochastic Approximation algorithm has been implemented in RAVEN and preliminary tests have been performed. The development of the software infrastructure to support the simulation of the whole NHES has also moved forward. The coupling between RAVEN and an implementation of the Modelica language (OpenModelica) has been implemented, migrated under several operating systems and tested using an adapted model of a desalination plant. In particular, this exercise was focused on testing the coupling of the different code systems; testing parallel, computationally expensive simulations on the INL cluster; and providing a proof of concept for the possibility of using surrogate models to represent the different NHES subsystems. Another important step was the porting of the RAVEN code under the Windows™ operating system. This accomplishment makes RAVEN compatible with the development environment that is being used for dynamic simulation of NHES components. A very simplified model of a NHES on the electric market has been built in RAVEN to confirm expectations on the analysis capability of RAVEN to provide insight into system economics and to test the capability of RAVEN to identify limit surfaces even for stochastic constraints. This

  3. Dynamic Event Tree advancements and control logic improvements

    SciTech Connect (OSTI)

    Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Sen, Ramazan Sonat; Cogliati, Joshua Joseph

    2015-09-01

    The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named “Hybrid Dynamic Event Tree” (HDET) and its Adaptive variant “Adaptive Hybrid Dynamic Event Tree” (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the

  4. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    fe-s image » Links Scientific Highlight Peters Lab MSU News e! Science News Physorg.com » Share this Article Laboratree Ologeez SciLink LabSpaces The Structure of an Algal Hydrogenase Reveals the Assembly and Evolution of Complex Metalloenzymes summary written by Raven Hanna The potential for using biological enzymes to make hydrogen to use as a renewable energy source is a hot topic, but little is known about how these complex enzymes assemble and work. The [FeFe]-hydrogenase enzyme binds

  5. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    image M.F. Toney, A. Nilsson & H. Ogasawara (Image courtesy of K. Tuttle) » Links Nilsson Research SLAC Press Release » Share this Article Laboratree Ologeez SciLink LabSpaces Reducing Fuel Cell Costs by Changing the Structure and Reactivity of Platinum summary written by Raven Hanna Hydrogen fuel cells are a green alternative to fossil fuels for powering vehicles, since the byproduct of fuel cell energy production is simply water. A problem with using fuel cells is their high cost,

  6. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    H1N1 Flu » Links Scientific Highlight Wilson Lab Scripps Press Release Scripps Philanthrophy Science Magazine Vanderbilt Reporter » Share this Article Laboratree Ologeez SciLink LabSpaces Structural Basis for Senior Immunity to the Current H1N1 Flu summary written by Raven Hanna An unusual property of the last year's H1N1 "swine flu" virus pandemic is that it disproportionately affected the young. People over the age of around 65 showed much less vulnerability than to more typical

  7. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Hg Methylation » Links Scientific Highlight BL2-3 BL6-2C BL9-3 BL10-2 » Share this Article Laboratree Ologeez SciLink LabSpaces Imaging Mercury in the Rhizosphere of Wetland Plants summary written by Raven Hanna High levels of mercury in our diets can have adverse effects on our health, and fish are a major source of dietary mercury. Because of a process called biomagnification, mercury levels in fish can build up to be at a much higher concentration than in the surrounding water. The mercury

  8. 1,"Chalk Point LLC","Petroleum","NRG Chalk Point LLC",2248 2,"Calvert Cliffs Nuclear Power Plant","Nuclear","Calvert Cliffs Nuclear PP LLC",1707.8

    U.S. Energy Information Administration (EIA) Indexed Site

    Maryland" ,"Plant","Primary energy source","Operating company","Net summer capacity (MW)" 1,"Chalk Point LLC","Petroleum","NRG Chalk Point LLC",2248 2,"Calvert Cliffs Nuclear Power Plant","Nuclear","Calvert Cliffs Nuclear PP LLC",1707.8 3,"Morgantown Generating Plant","Coal","GenOn Mid-Atlantic LLC",1423 4,"Brandon Shores","Coal","Raven

  9. Science Summary

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Chang Research Scripps News Release » Share this Article Laboratree Ologeez SciLink LabSpaces Finding the Crystal Structure of P-gp: A Protein that Makes Cancer Cells Resistant to Chemotherapy summary written by Raven Hanna Medications can be rendered ineffective through cells developing multidrug resistance. This is the case in many forms of cancer cells that fail to respond to chemotherapy. The ability of these cells to avoid the effects of drugs can be due to the actions of P-glycoprotein

  10. Instrumentation of Current Technology Testing and Replicating Harsh Environments

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Abrasion Testing of Critical Components of Hydrokinetic Devices 10/17/2012 University of Alaska Anchorage 2 Project Team o Ocean Renewable Power Company (ORPC) o Jarlath McEntee o Monty Worthington o University of Alaska Anchorage (UAA) o Faculty o Thomas Ravens o Todd Petersen o Muhammad Ali o Research Assistants o Tim Kirk o Jacob Clark o Angus Bromaghin 10/17/2012 University of Alaska Anchorage 3 ORPC Technology o TideGen Power System (TGU) o Designed to generate electricity at water depths

  11. Modeling of a Flooding Induced Station Blackout for a Pressurized Water Reactor Using the RISMC Toolkit

    SciTech Connect (OSTI)

    Mandelli, Diego; Prescott, Steven R; Smith, Curtis L; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua J; Kinoshita, Robert A

    2011-07-01

    In the Risk Informed Safety Margin Characterization (RISMC) approach we want to understand not just the frequency of an event like core damage, but how close we are (or are not) to key safety-related events and how might we increase our safety margins. The RISMC Pathway uses the probabilistic margin approach to quantify impacts to reliability and safety by coupling both probabilistic (via stochastic simulation) and mechanistic (via physics models) approaches. This coupling takes place through the interchange of physical parameters and operational or accident scenarios. In this paper we apply the RISMC approach to evaluate the impact of a power uprate on a pressurized water reactor (PWR) for a tsunami-induced flooding test case. This analysis is performed using the RISMC toolkit: RELAP-7 and RAVEN codes. RELAP-7 is the new generation of system analysis codes that is responsible for simulating the thermal-hydraulic dynamics of PWR and boiling water reactor systems. RAVEN has two capabilities: to act as a controller of the RELAP-7 simulation (e.g., system activation) and to perform statistical analyses (e.g., run multiple RELAP-7 simulations where sequencing/timing of events have been changed according to a set of stochastic distributions). By using the RISMC toolkit, we can evaluate how power uprate affects the system recovery measures needed to avoid core damage after the PWR lost all available AC power by a tsunami induced flooding. The simulation of the actual flooding is performed by using a smooth particle hydrodynamics code: NEUTRINO.

  12. Comparing Simulation Results with Traditional PRA Model on a Boiling Water Reactor Station Blackout Case Study

    SciTech Connect (OSTI)

    Zhegang Ma; Diego Mandelli; Curtis Smith

    2011-07-01

    A previous study used RELAP and RAVEN to conduct a boiling water reactor station black-out (SBO) case study in a simulation based environment to show the capabilities of the risk-informed safety margin characterization methodology. This report compares the RELAP/RAVEN simulation results with traditional PRA model results. The RELAP/RAVEN simulation run results were reviewed for their input parameters and output results. The input parameters for each simulation run include various timing information such as diesel generator or offsite power recovery time, Safety Relief Valve stuck open time, High Pressure Core Injection or Reactor Core Isolation Cooling fail to run time, extended core cooling operation time, depressurization delay time, and firewater injection time. The output results include the maximum fuel clad temperature, the outcome, and the simulation end time. A traditional SBO PRA model in this report contains four event trees that are linked together with the transferring feature in SAPHIRE software. Unlike the usual Level 1 PRA quantification process in which only core damage sequences are quantified, this report quantifies all SBO sequences, whether they are core damage sequences or success (i.e., non core damage) sequences, in order to provide a full comparison with the simulation results. Three different approaches were used to solve event tree top events and quantify the SBO sequences: W process flag, default process flag without proper adjustment, and default process flag with adjustment to account for the success branch probabilities. Without post-processing, the first two approaches yield incorrect results with a total conditional probability greater than 1.0. The last approach accounts for the success branch probabilities and provides correct conditional sequence probabilities that are to be used for comparison. To better compare the results from the PRA model and the simulation runs, a simplified SBO event tree was developed with only four top

  13. Further investigation of g factors for the lead monofluoride ground state

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Skripnikov, L. V.; Petrov, A. N.; Titov, A. V.; Mawhorter, R. J.; Baum, A. L.; Sears, T. J.; Grabow, J. -U.

    2015-09-15

    We report the results of our theoretical study and analysis of earlier experimental data for the g-factor tensor components of the ground 2II1/2 state of the free PbF radical. These values obtained both within the relativistic coupled-cluster method combined with the generalized relativistic effective core potential approach and with our fit of the experimental data from [R. J. Mawhorter, B. S. Murphy, A. L. Baum, T. J. Sears, T. Yang, P. M. Rupasinghe, C. P. McRaven, N. E. Shafer-Ray, L. D. Alphei, and J.-U. Grabow, Phys. Rev. A 84, 022508 (2011); A. L. Baum, B.A. thesis, Pomona College, 2011]. Themore » obtained results agree very well with each other but contradict the previous fit performed in the cited works. Our final prediction for g factors is G∥=0.081(5),G⊥=–0.27(1).« less

  14. Risk-Informed Safety Margin Characterization Methods Development Work

    SciTech Connect (OSTI)

    Smith, Curtis L; Ma, Zhegang; Tom Riley; Mandelli, Diego; Nielsen, Joseph W; Alfonsi, Andrea; Rabiti, Cristian

    2014-09-01

    This report summarizes the research activity developed during the Fiscal year 2014 within the Risk Informed Safety Margin and Characterization (RISMC) pathway within the Light Water Reactor Sustainability (LWRS) campaign. This research activity is complementary to the one presented in the INL/EXT-??? report which shows advances Probabilistic Risk Assessment Analysis using RAVEN and RELAP-7 in conjunction to novel flooding simulation tools. Here we present several analyses that prove the values of the RISMC approach in order to assess risk associated to nuclear power plants (NPPs). We focus on simulation based PRA which, in contrast to classical PRA, heavily employs system simulator codes. Firstly we compare, these two types of analyses, classical and RISMC, for a Boiling water reactor (BWR) station black out (SBO) initiating event. Secondly we present an extended BWR SBO analysis using RAVEN and RELAP-5 which address the comments and suggestions received about he original analysis presented in INL/EXT-???. This time we focus more on the stochastic analysis such probability of core damage and on the determination of the most risk-relevant factors. We also show some preliminary results regarding the comparison between RELAP5-3D and the new code RELAP-7 for a simplified Pressurized Water Reactors system. Lastly we present some conceptual ideas regarding the possibility to extended the RISMC capabilities from an off-line tool (i.e., as PRA analysis tool) to an online-tool. In this new configuration, RISMC capabilities can be used to assist and inform reactor operator during real accident scenarios.

  15. Light Water Reactor Sustainability Program: Analysis of Pressurized Water Reactor Station Blackout caused by external flooding using the RISMC toolkit

    SciTech Connect (OSTI)

    Mandelli, Diego; Smith, Curtis; Prescott, Steven; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua; Kinoshita, Robert

    2014-08-01

    The existing fleet of nuclear power plants is in the process of extending its lifetime and increasing the power generated from these plants via power uprates. In order to evaluate the impacts of these two factors on the safety of the plant, the Risk Informed Safety Margin Characterization project aims to provide insights to decision makers through a series of simulations of the plant dynamics for different initial conditions (e.g., probabilistic analysis and uncertainty quantification). This paper focuses on the impacts of power uprate on the safety margin of a boiling water reactor for a flooding induced station black-out event. Analysis is performed by using a combination of thermal-hydraulic codes and a stochastic analysis tool currently under development at the Idaho National Laboratory, i.e. RAVEN. We employed both classical statistical tools, i.e. Monte-Carlo, and more advanced machine learning based algorithms to perform uncertainty quantification in order to quantify changes in system performance and limitations as a consequence of power uprate. Results obtained give a detailed investigation of the issues associated with a plant power uprate including the effects of station black-out accident scenarios. We were able to quantify how the timing of specific events was impacted by a higher nominal reactor core power. Such safety insights can provide useful information to the decision makers to perform risk informed margins management.

  16. Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Maljovec, D.; Liu, S.; Wang, B.; Mandelli, D.; Bremer, P. -T.; Pascucci, V.; Smith, C.

    2015-07-14

    Here, dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated,more » where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.« less

  17. Further investigation of g factors for the lead monofluoride ground state

    SciTech Connect (OSTI)

    Skripnikov, L. V.; Petrov, A. N.; Titov, A. V.; Mawhorter, R. J.; Baum, A. L.; Sears, T. J.; Grabow, J. -U.

    2015-09-15

    We report the results of our theoretical study and analysis of earlier experimental data for the g-factor tensor components of the ground 2II1/2 state of the free PbF radical. These values obtained both within the relativistic coupled-cluster method combined with the generalized relativistic effective core potential approach and with our fit of the experimental data from [R. J. Mawhorter, B. S. Murphy, A. L. Baum, T. J. Sears, T. Yang, P. M. Rupasinghe, C. P. McRaven, N. E. Shafer-Ray, L. D. Alphei, and J.-U. Grabow, Phys. Rev. A 84, 022508 (2011); A. L. Baum, B.A. thesis, Pomona College, 2011]. The obtained results agree very well with each other but contradict the previous fit performed in the cited works. Our final prediction for g factors is G=0.081(5),G=–0.27(1).

  18. Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios

    SciTech Connect (OSTI)

    Joe, Jeffrey Clark; Boring, Ronald Laurids; Herberger, Sarah Elizabeth Marie; Mandelli, Diego; Smith, Curtis Lee

    2015-09-01

    The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the overall objective to help sustain the existing commercial nuclear power plants (NPPs). To accomplish this program objective, there are multiple LWRS “pathways,” or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin and Characterization (RISMC) pathway. Initial efforts under this pathway to combine probabilistic and plant multi-physics models to quantify safety margins and support business decisions also included HRA, but in a somewhat simplified manner. HRA experts at Idaho National Laboratory (INL) have been collaborating with other experts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using RAVEN as a controller, seamlessly integrate virtual operator models (HUNTER) with 1) the dynamic computational MOOSE runtime environment that includes a full-scope plant model, and 2) the RISMC framework PRA models already in use. The HUNTER computational HRA approach is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a significant departure from existing static and even dynamic HRA methods. This report is divided into five chapters that cover the development of an external flooding event test case and associated statistical modeling considerations.

  19. Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study

    SciTech Connect (OSTI)

    Maljovec, D.; Liu, S.; Wang, B.; Mandelli, D.; Bremer, P. -T.; Pascucci, V.; Smith, C.

    2015-07-14

    Here, dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP and MELCOR) with simulation controller codes (e.g., RAVEN and ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic and operating procedures) and stochastic (e.g., component failures and parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other for enhanced structural understanding of the data.

  20. Improved Sampling Algorithms in the Risk-Informed Safety Margin Characterization Toolkit

    SciTech Connect (OSTI)

    Mandelli, Diego; Smith, Curtis Lee; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua Joseph

    2015-09-01

    The RISMC approach is developing advanced set of methodologies and algorithms in order to perform Probabilistic Risk Analyses (PRAs). In contrast to classical PRA methods, which are based on Event-Tree and Fault-Tree methods, the RISMC approach largely employs system simulator codes applied to stochastic analysis tools. The basic idea is to randomly perturb (by employing sampling algorithms) timing and sequencing of events and internal parameters of the system codes (i.e., uncertain parameters) in order to estimate stochastic parameters such as core damage probability. This approach applied to complex systems such as nuclear power plants requires to perform a series of computationally expensive simulation runs given a large set of uncertain parameters. These types of analysis are affected by two issues. Firstly, the space of the possible solutions (a.k.a., the issue space or the response surface) can be sampled only very sparsely, and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Secondly, large amounts of data are generated and tools to generate knowledge from such data sets are not yet available. This report focuses on the first issue and in particular employs novel methods that optimize the information generated by the sampling process by sampling unexplored and risk-significant regions of the issue space: adaptive (smart) sampling algorithms. They infer system response from surrogate models constructed from existing samples and predict the most relevant location of the next sample. It is therefore possible to understand features of the issue space with a small number of carefully selected samples. In this report, we will present how it is possible to perform adaptive sampling using the RISMC toolkit and highlight the advantages compared to more classical sampling approaches such Monte-Carlo. We will employ RAVEN to perform such statistical analyses using both analytical cases but also another RISMC code: RELAP-7.

  1. Reduced Order Model Implementation in the Risk-Informed Safety Margin Characterization Toolkit

    SciTech Connect (OSTI)

    Mandelli, Diego; Smith, Curtis L.; Alfonsi, Andrea; Rabiti, Cristian; Cogliati, Joshua J.; Talbot, Paul W.; Rinaldi, Ivan; Maljovec, Dan; Wang, Bei; Pascucci, Valerio; Zhao, Haihua

    2015-09-01

    The RISMC project aims to develop new advanced simulation-based tools to perform Probabilistic Risk Analysis (PRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermo-hydraulic behavior of the reactor primary and secondary systems but also external events temporal evolution and components/system ageing. Thus, this is not only a multi-physics problem but also a multi-scale problem (both spatial, µm-mm-m, and temporal, ms-s-minutes-years). As part of the RISMC PRA approach, a large amount of computationally expensive simulation runs are required. An important aspect is that even though computational power is regularly growing, the overall computational cost of a RISMC analysis may be not viable for certain cases. A solution that is being evaluated is the use of reduce order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RICM analysis computational cost by decreasing the number of simulations runs to perform and employ surrogate models instead of the actual simulation codes. This report focuses on the use of reduced order modeling techniques that can be applied to any RISMC analysis to generate, analyze and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (µs instead of hours/days). We apply reduced order and surrogate modeling techniques to several RISMC types of analyses using RAVEN and RELAP-7 and show the advantages that can be gained.

  2. Effects of Transcranial Direct Current Stimulation (tDCS) on Human Memory.

    SciTech Connect (OSTI)

    Matzen, Laura E.; Trumbo, Michael Christopher Stefan

    2014-10-01

    Training a person in a new knowledge base or skill set is extremely time consuming and costly, particularly in highly specialized domains such as the military and the intelligence community. Recent research in cognitive neuroscience has suggested that a technique called transcranial direct current stimulation (tDCS) has the potential to revolutionize training by enabling learners to acquire new skills faster, more efficiently, and more robustly (Bullard et al., 2011). In this project, we tested the effects of tDCS on two types of memory performance that are critical for learning new skills: associative memory and working memory. Associative memory is memory for the relationship between two items or events. It forms the foundation of all episodic memories, so enhancing associative memory could provide substantial benefits to the speed and robustness of learning new information. We tested the effects of tDCS on associative memory, using a real-world associative memory task: remembering the links between faces and names. Working memory refers to the amount of information that can be held in mind and processed at one time, and it forms the basis for all higher-level cognitive processing. We investigated the degree of transfer between various working memory tasks (the N-back task as a measure of verbal working memory, the rotation-span task as a measure of visuospatial working memory, and Raven's progressive matrices as a measure of fluid intelligence) in order to determine if tDCS-induced facilitation of performance is task-specific or general.

  3. Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case

    SciTech Connect (OSTI)

    D. Mandelli; C. Smith; T. Riley; J. Nielsen; J. Schroeder; C. Rabiti; A. Alfonsi; Cogliati; R. Kinoshita; V. Pasucci; B. Wang; D. Maljovec

    2014-06-01

    Dynamic Probabilistic Risk Assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). While system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by: 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the Risk Informed Safety Margin Characterization (RISMC) Boiling Water Reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.

  4. CHRPR Operations Manual

    SciTech Connect (OSTI)

    Windsor, Bradford T.; Woodring, Mitchell L.; Myjak, Mitchell J.

    2012-08-21

    1.0 Overview The TSA systems VM-250AGN portal monitor is a set of two pillars made to detect nuclear material in a vehicle. Each pillar contains two polyvinyl toluene (PVT) plastic gamma ray detectors and four 3He neutron detectors, as well as a power supply and electronics to process the output from these detectors. Pacific Northwest National Laboratory has designed and built a continuous high-resolution PVT readout (CHRPR) for the TSA portal to allow spectral readout from the gamma and neutron detectors. The CHRPR helps differentiate between different types of radioactive material through increased spectroscopic capability and associated developments. The TSA VM-250AGN continually monitors the natural neutron and gamma ray background which occurs around the pillars. When the system is installed, the two pillars are placed on either side of a roadway, and a vehicle presence sensor records the passage of cars between them. When radiation measurements exceed a preset alarm threshold, the system alarms to let the user know that a radioactive material is present. Time-stamped measurements are continually sent to a computer, where they can be recorded via a Windows terminal or the TSA RAVEN software. For each pillar in the original TSA model, output from each detector is amplified and shaped by a single channel analyzer, the SCA-775. Information from both SCA-775s are passed to the SC-770 in the master pillar. This is the detector interface module and main data processor. It counts electrical pulses and uses program software to output total readings to the computer, as well as trigger any appropriate alarms. The CHRPR allows a parallel approach to recording radiation readings from the TSA system. After installing the CHRPR system, all TSA power and signal connections are unchanged. The CHRPR captures electrical pulses containing detector and occupancy sensor information from the SCA-775 on either side. These pulses are converted to a signal with a time width

  5. Medium Truck Duty Cycle Data from Real-World Driving Environments: Final Report

    SciTech Connect (OSTI)

    Lascurain, Mary Beth; Franzese, Oscar; Capps, Gary J; Siekmann, Adam; Thomas, Neil; LaClair, Tim J; Barker, Alan M; Knee, Helmut E

    2012-11-01

    ), including speed, acceleration, and spatial location information at a rate of 5 Hz for the Part 1 FOT. For the Part 2 FOT, this information was obtained from DAS-based GPS instrumentation. The Air-Weigh LoadMaxx, a self-weighing system that determines the vehicle s gross weight by means of pressure transducers, was used to collect vehicle payload information for the combination, urban transit, and towing and recovery vehicles. A cellular modem, the Raven X EVDO V4221, facilitated the communication between the eDAQ-lite (the data collection engine of the system) and the user. The modem functioned as a wireless gateway, allowing data retrievals and system checks to be performed remotely. Also, in partnership with FMCSA, two additional safety sensors were installed on the combination vehicles: the MGM e-Stroke brake monitoring system and the Tire SafeGuard tire pressure monitoring system. All of these sensors posted data to the J1939 data bus, enabling the signals to be read withou...

  6. Medium Truck Duty Cycle Data from Real-World Driving Environments: Project Interim Report

    SciTech Connect (OSTI)

    Franzese, Oscar; Lascurain, Mary Beth; Capps, Gary J

    2011-01-01

    1939 data bus, was used to collect vehicle payload information. A cellular modem, the Raven X EVDO V4221, facilitated the communication between the eDAQ-lite (the data collection engine of the system) and the user, via the internet. The modem functioned as a wireless gateway, allowing data retrievals and system checks to be performed remotely. Also, and in partnership with FMCSA, two additional safety sensors were installed on the combination vehicles: the MGM e-Stroke brake monitoring system and the Tire SafeGuard tire pressure monitoring system. Both of these systems posted data to the J1939 data bus, enabling these signals to be read without any additional DAS interface hardware. Seventy-three signals from the different deployed sensors and available vehicle systems were collected. Because of the differences in vehicle data buses (i.e., J1939 and J1708 data buses), not all desired signals were available for both types of vehicles. The s...