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At least 415 records · Page 23

Parallel Algorithms and Software for Nuclear, Energy, and Environmental Applications Part I: Multiphysics Algorithms

There is a growing trend within energy and environmental simulation to consider tightly coupled solutions to multiphysics problems. This can be seen in nuclear reactor analysis where analysts are interested in coupled flow, heat transfer and neutronics, and in nuclear fuel performance simulation where analysts are interested in thermomechanics with contact coupled to species transport and chemistry. In energy and environmental applications, energy extraction involves geomechanics, flow through porous media and fractured formations, adding heat transport for enhanced oil recovery and geothermal applications, and adding reactive transport in the case of applications modeling the underground flow of contaminants. These more ambitious simulations usually motivate some level of parallel computing. Many of the physics coupling efforts to date utilize simple code coupling or first-order operator splitting, often referred to as loose coupling. While these approaches can produce answers, they usually leave questions of accuracy and stability unanswered. Additionally, the different physics often reside on distinct meshes and data are coupled via simple interpolation, again leaving open questions of stability and accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Anomaly Detection in Multiple Scale for Insider Threat Analysis

We propose a method to quantify malicious insider activity with statistical and graph-based analysis aided with semantic scoring rules. Different types of personal activities or interactions are monitored to form a set of directed weighted graphs. The semantic scoring rules assign higher scores for the events more significant and suspicious. Then we build personal activity profiles in the form of score tables. Profiles are created in multiple scales where the low level profiles are aggregated toward more stable higherlevel profiles within the subject or object hierarchy. Further, the profiles are created in different time scales such as day, week, or month. During operation, the insider s current activity profile is compared to the historical profiles to produce an anomaly score. For each subject with a high anomaly score, a subgraph of connected subjects is extracted to look for any related score movement. Finally the subjects are ranked by their anomaly scores to help the analysts focus on high-scored subjects. The threat-ranking component supports the interaction between the User Dashboard and the Insider Threat Knowledge Base portal. The portal includes a repository for historical results, i.e., adjudicated cases containing all of the information first presented to the user and including any additional insights to help the analysts. In this paper we show the framework of the proposed system and the operational algorithms.

99 GENERAL AND MISCELLANEOUS↗

Computational Human Performance Modeling For Alarm System Design

The introduction of new technologies like adaptive automation systems and advanced alarms processing and presentation techniques in nuclear power plants is already having an impact on the safety and effectiveness of plant operations and also the role of the control room operator. This impact is expected to escalate dramatically as more and more nuclear power utilities embark on upgrade projects in order to extend the lifetime of their plants. One of the most visible impacts in control rooms will be the need to replace aging alarm systems. Because most of these alarm systems use obsolete technologies, the methods, techniques and tools that were used to design the previous generation of alarm system designs are no longer effective and need to be updated. The same applies to the need to analyze and redefine operators’ alarm handling tasks. In the past, methods for analyzing human tasks and workload have relied on crude, paper-based methods that often lacked traceability. New approaches are needed to allow analysts to model and represent the new concepts of alarm operation and human-system interaction. State-of-the-art task simulation tools are now available that offer a cost-effective and efficient method for examining the effect of operator performance in different conditions and operational scenarios. A discrete event simulation system was used by human factors researchers at the Idaho National Laboratory to develop a generic alarm handling model to examine the effect of operator performance with simulated modern alarm system. It allowed analysts to evaluate alarm generation patterns as well as critical task times and human workload predicted by the system.

99 GENERAL AND MISCELLANEOUS↗

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

Big Data↗

Data-Intensive Visual Analysis for Cyber Security

Protecting communications networks against attacks where the aim is to steal information, disrupt order, or harm critical infrastructure can require the collection and analysis of staggering amounts of data. The ability to detect and respond to threats quickly is a paramount concern across sectors, and especially for critical government, utility and financial networks. Yet detecting emerging or incipient threats in immense volumes of network traffic requires new computational and analytic approaches. Network security increasingly requires cooperation between human analysts able to spot suspicious events through means such as data visualization and automated systems that process streaming network data in near real-time to triage events so that human analysts are best able to focus their work.

Pike, William A.↗

Human Reliability Analysis for Small Modular Reactors

Because no human reliability analysis (HRA) method was specifically developed for small modular reactors (SMRs), the application of any current HRA method to SMRs represents tradeoffs. A first- generation HRA method like THERP provides clearly defined activity types, but these activity types do not map to the human-system interface or concept of operations confronting SMR operators. A second- generation HRA method like ATHEANA is flexible enough to be used for SMR applications, but there is currently insufficient guidance for the analyst, requiring considerably more first-of-a-kind analyses and extensive SMR expertise in order to complete a quality HRA. Although no current HRA method is optimized to SMRs, it is possible to use existing HRA methods to identify errors, incorporate them as human failure events in the probabilistic risk assessment (PRA), and quantify them. In this paper, we provided preliminary guidance to assist the human reliability analyst and reviewer in understanding how to apply current HRA methods to the domain of SMRs. While it is possible to perform a satisfactory HRA using existing HRA methods, ultimately it is desirable to formally incorporate SMR considerations into the methods. This may require the development of new HRA methods. More practicably, existing methods need to be adapted to incorporate SMRs. Such adaptations may take the form of guidance on the complex mapping between conventional light water reactors and small modular reactors. While many behaviors and activities are shared between current plants and SMRs, the methods must adapt if they are to perform a valid and accurate analysis of plant personnel performance in SMRs.

99 GENERAL AND MISCELLANEOUS↗

Comparison of Photovoltaic Models in the System Advisor Model: Preprint

The System Advisor Model (SAM) is free software developed by the National Renewable Energy Laboratory (NREL) for predicting the performance of renewable energy systems and analyzing the financial feasibility of residential, commercial, and utility-scale grid-connected projects. SAM offers several options for predicting the performance of photovoltaic (PV) systems. The model requires that the analyst choose from three PV system models, and depending on that choice, possibly choose from three module and two inverter component models. To obtain meaningful results from SAM, the analyst must be aware of the differences between the model options and their applicability to different modeling scenarios. This paper presents an overview the different PV model options and presents a comparison of results for a 200-kW system using different model options.

14 SOLAR ENERGY↗

'Big Data' Collaboration: Exploring, Recording and Sharing Enterprise Knowledge

As data sources and data size proliferate, knowledge discovery from "Big Data" is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly-related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis, inference and decision-making lifecycle. We motivate the implementation of an enterprise data-discovery and knowledge recorder tool, called SEEKER based on real world case-study. We demonstrate SEEKER capturing schema and data-element relationships, tracking the data elements of value based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as digital record of institutional domain knowledge and a documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by avoiding analytic silos, expediting the process of multi-source data integration and intelligently documenting discoveries from fellow analysts.

Big Data↗

A Visual Analytics Approach to Structured Data Analysis to Enhance Nonproliferation and Arms Control Verification Activities

Analysis activities for Nonproliferation and Arms Control verification require the use of many types of data. Tabular structured data, such as Excel spreadsheets and relational databases, have traditionally been used for data mining activities, where specific queries are issued against data to look for matching results. The application of visual analytics tools to structured data enables further exploration of datasets to promote discovery of previously unknown results. This paper discusses the application of a specific visual analytics tool to datasets related to the field of Arms Control and Nonproliferation to promote the use of visual analytics more broadly in this domain. Visual analytics focuses on analytical reasoning facilitated by interactive visual interfaces (Wong and Thomas 2004). It promotes exploratory analysis of data, and complements data mining technologies where known patterns can be mined for. Also with a human in the loop, they can bring in domain knowledge and subject matter expertise. Visual analytics has not widely been applied to this domain. In this paper, we will focus on one type of data: structured data, and show the results of applying a specific visual analytics tool to answer questions in the Arms Control and Nonproliferation domain. We chose to use the T.Rex tool, a visual analytics tool developed at PNNL, which uses a variety of visual exploration patterns to discover relationships in structured datasets, including a facet view, graph view, matrix view, and timeline view. The facet view enables discovery of relationships between categorical information, such as countries and locations. The graph tool visualizes node-link relationship patterns, such as the flow of materials being shipped between parties. The matrix visualization shows highly correlated categories of information. The timeline view shows temporal patterns in data. In this paper, we will use T.Rex with two different datasets to demonstrate how interactive exploration of the data can aid an analyst with arms control and nonproliferation verification activities. Using a dataset from PIERS (PIERS 2014), we will show how container shipment imports and exports can aid an analyst in understanding the shipping patterns between two countries. We will also use T.Rex to examine a collection of research publications from the IAEA International Nuclear Information System (IAEA 2014) to discover collaborations of concern. We hope this paper will encourage the use of visual analytics structured data analytics in the field of nonproliferation and arms control verification. Our paper outlines some of the challenges that exist before broad adoption of these kinds of tools can occur and offers next steps to overcome these challenges.

arms control↗

Augmenting Traditional Static Analysis With Commonly Available Metadata

Developers and security analysts have been using static analysis for a long time to analyze programs for defects and vulnerabilities with some success. Generally a static analysis tool is run on the source code for a given program, flagging areas of code that need to be further inspected by a human analyst. These areas may be obvious bugs like potential bu er over flows, information leakage flaws, or the use of uninitialized variables. These tools tend to work fairly well - every year they find many important bugs. These tools are more impressive considering the fact that they only examine the source code, which may be very complex. Now consider the amount of data available that these tools do not analyze. There are many pieces of information that would prove invaluable for finding bugs in code, things such as a history of bug reports, a history of all changes to the code, information about committers, etc. By leveraging all this additional data, it is possible to nd more bugs with less user interaction, as well as track useful metrics such as number and type of defects injected by committer. This dissertation provides a method for leveraging development metadata to find bugs that would otherwise be difficult to find using standard static analysis tools. We showcase two case studies that demonstrate the ability to find 0day vulnerabilities in large and small software projects by finding new vulnerabilities in the cpython and Roundup open source projects.

97 MATHEMATICS AND COMPUTING↗

PNNL Strategic Goods Testbed: A Data Library for Illicit Nuclear Trafficking

Pacific Northwest National Laboratory (PNNL) has put significant effort into nonproliferation activities as an institution, both in terms of the classical nuclear material focused approach and in the examination of other strategic goods necessary to implement a nuclear program. To assist in these efforts, several projects in the Analysis in Motion (AIM) and Signature Discovery (SDI) Initiatives at PNNL are developing machine learning methodology for human-computer interaction in real time environments to assist analysts in this domain. All of these technical projects require access to data – whether it is in terms of detector data, shipping records, financial information, company relations, or other communications. The first question that mathematical and computational researchers come up with when asked to build analyst assist or automated tools is “What does the data look like? ” They become frustrated when basic questions like this can not be easily answered and this can have the effect of pushing researchers away from the nuclear trafficking domain, especially in strategic commodity and export control areas where data sets can not easily be generated through standard experimental techniques. For small projects that are building a proof of concept for their methodology, obtaining this data can be arduous and expensive. To relieve the burden of data collection from these projects and grow a lab-wide capability, the Strategic Goods Testbed Team has taken over data collection and placed subscriptions and access to flat data files in a centralized location so that all projects can benefit from these items. We have collected shipping data in the form of PIERS records, judicial information about export control cases, NAC data on the nuclear fuel industry, and financial data from Dun and Bradstreet and our data sets are continuing to expand. With a single access agreement, researchers in data-mining and other fields can utilize all of the records that have been downloaded, make requests through subscription services, and interact with other researchers through our interface. Our testbed team provides more than a simply static repository by working with researchers to refine their data needs and insure data quality as well as quantity. We are currently working with laboratory and initiative specific management to examine effective ways for continuing data growth and sustainability.

data capability↗

Annual Technology Baseline (Including Supporting Data); NREL (National Renewable Energy Laboratory)

Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), National Renewable Energy Laboratory provides an organized and centralized dataset that was reviewed by internal and external experts. It uses the best information from the Department of Energy laboratory's renewable energy analysts and Energy Information Administration information for conventional technologies. The ATB will be updated annually in order to provide an up-to-date repository of current and future cost and performance data. Going forward, we plan to revise and refine the values using best available information. The ATB includes both a presentation with notes (PDF) and an associated Excel Workbook. The ATB includes the following electricity generation technologies: land-based wind; offshore wind; utility-scale solar PV; concentrating solar power; geothermal power; hydropower plants (upgrades to existing facilities, powering non-powered dams, and new stream-reach development); conventional coal; coal with carbon capture and sequestration; integrated gasification combined cycle coal; natural gas combustion turbines; natural gas combined cycle; conventional biopower. Nuclear laboratory's renewable energy analysts and Energy Information Administration information for conventional technologies. The ATB will be updated annually in order to provide an up-to-date repository of current and future cost and performance data. Going forward, we plan to revise and refine the values using best available information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

LMJ Points Plus v2.6

Short summary of the software's functionality: • built-in scan feature to acquire optical image of the surface to be analyzed • click-and-point selection of points of interest on the surface • supporting standalone autosampler/HPLC/MS operation: creating independent batch files after points of interests are selected for LEAPShell (autosampler control software from Leap Technologies) and Analyst® (mass spectrometry (MS) software from AB Sciex) • supporting integrated autosampler/HPLC/MS operation: creating one batch file for all instruments controlled by Analyst® (mass spectrometry software from AB Sciex) after points of interests are selected •creating heatmaps of analytes of interests from collected MS files in a hand-off fashion

Kertesz, Vilmos↗

Geographic Visualization of Power-Grid Dynamics

The visualization enables the simulation analyst to see changes in the frequency through time and space. With this technology, the analyst has a bird's eye view of the frequency at loads and generators as the simulated power system responds to the loss of a generator, spikes in load, and other contingencies. The significance of a contingency to the operation of an electrical power system depends critically on how the resulting tansients evolve in time and space. Consequently, these dynamic events can only be understood when seen in their proper geographic context. this understanding is indispensable to engineers working on the next generation of distributed sensing and control systems for the smart grid. By making possible a natural and intuitive presentation of dynamic behavior, our new visualization technology is a situational-awareness tool for power-system engineers.

Sukumar, Sreenivas R.↗

2016 Annual Technology Baseline (ATB)

Consistent cost and performance data for various electricity generation technologies can be difficult to find and may change frequently for certain technologies. With the Annual Technology Baseline (ATB), National Renewable Energy Laboratory provides an organized and centralized dataset that was reviewed by internal and external experts. It uses the best information from the Department of Energy laboratory's renewable energy analysts and Energy Information Administration information for conventional technologies. The ATB will be updated annually in order to provide an up-to-date repository of current and future cost and performance data. Going forward, we plan to revise and refine the values using best available information. The ATB includes both a presentation with notes (PDF) and an associated Excel Workbook. The ATB includes the following electricity generation technologies: land-based wind; offshore wind; utility-scale solar PV; concentrating solar power; geothermal power; hydropower plants (upgrades to existing facilities, powering non-powered dams, and new stream-reach development); conventional coal; coal with carbon capture and sequestration; integrated gasification combined cycle coal; natural gas combustion turbines; natural gas combined cycle; conventional biopower. Nuclear laboratory's renewable energy analysts and Energy Information Administration information for conventional technologies. The ATB will be updated annually in order to provide an up-to-date repository of current and future cost and performance data. Going forward, we plan to revise and refine the values using best available information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NIRP Core Software Suite v. 1.0

The NIRP Core Software Suite is a core set of code that supports multiple applications. It includes miscellaneous base code for data objects, mathematic equations, and user interface components; and the framework includes several fully-developed software applications that exist as stand-alone tools to compliment other applications. The stand-alone tools are described below. Analyst Manager: An application to manage contact information for people (analysts) that use the software products. This information is often included in generated reports and may be used to identify the owners of calculations. Radionuclide Viewer: An application for viewing the DCFPAK radiological data. Compliments the Mixture Manager tool. Mixture Manager: An application to create and manage radionuclides mixtures that are commonly used in other applications. High Explosive Manager: An application to manage explosives and their properties. Chart Viewer: An application to view charts of data (e.g. meteorology charts). Other applications may use this framework to create charts specific to their data needs.

Whitener, Dustin Heath↗

Big Data, Big Solutions

Data—lots of data—generated in seconds and piling up on the internet, streaming and stored in countless databases. Big data is important for commerce, society and our nation’s security. Yet the volume, velocity, variety and veracity of data is simply too great for any single analyst to make sense of alone. It requires advanced, data-intensive computing. Simply put, data-intensive computing is the use of sophisticated computers to sort through mounds of information and present analysts with solutions in the form of graphics, scenarios, formulas, new hypotheses and more. This scientific capability is foundational to PNNL’s energy, environment and security missions. Senior Scientist and Division Director Bill Pike and his team are developing analytic tools that are used to solve important national challenges, including cyber systems defense, power grid control systems, intelligence analysis, climate change and scientific exploration.

97 MATHEMATICS AND COMPUTING↗

Preliminary Concept of Operations for the Spent Fuel Management System--WM2017

The Nuclear Fuels Storage and Transportation Planning Project (NFST) within the U.S. Department of Energy s Office of Nuclear Energy is tasked with identifying, planning, and conducting activities to lay the groundwork for developing interim storage and transportation capabilities in support of an integrated waste management system. The system will provide interim storage for commercial spent nuclear fuel (SNF) from reactor sites and deliver it to a repository. The system will also include multiple subsystems, potentially including; one or more interim storage facilities (ISF); one or more repositories; facilities to package and/or repackage SNF; and transportation systems. The project team is analyzing options for an integrated waste management system. To support analysis, the project team has developed a Concept of Operations document that describes both the potential integrated system and inter-dependencies between system components. The goal of this work is to aid systems analysts in the development of consistent models across the project, which involves multiple investigators. The Concept of Operations document will be updated periodically as new developments emerge. At a high level, SNF is expected to travel from reactors to a repository. SNF is first unloaded from reactors and placed in spent fuel pools for wet storage at utility sites. After the SNF has cooled enough to satisfy loading limits, it is placed in a container at reactor sites for storage and/or transportation. After transportation requirements are met, the SNF is transported to an ISF to store the SNF until a repository is developed or directly to a repository if available. While the high level operation of the system is straightforward, analysts must evaluate numerous alternative options. Alternative options include the number of ISFs (if any), ISF design, the stage at which SNF repackaging occurs (if any), repackaging technology, the types of containers used, repository design, component sizing, and timing of events. These alternative options arise due to technological, economic, or policy considerations. As new developments regularly emerge, the operational concepts will be periodically updated. This paper gives an overview of the different potential alternatives identified in the Concept of Operations document at a conceptual level.

Cumberland, Riley M↗