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At least 757 records · Page 42

Verification of MCNP6.2 for Nuclear Criticality Safety Applications

Several suites of verification/validation benchmark problems were run in early 2017 to verify that the new production release of MCNP6.2 performs correctly for nuclear criticality safety applications (NCS). MCNP6.2 results for several NCS validation suites were compared to the results from MCNP6.1 [1] and MCNP6.1.1 [2]. MCNP6.1 is the production version of MCNP® released in 2013, and MCNP6.1.1 is the update released in 2014. MCNP6.2 includes all of the standard features for NCS calculations that have been available for the past 15 years, along with new features for sensitivity-uncertainty based methods for NCS validation [3]. Results from the benchmark suites were compared with results from previous verification testing [4-8]. Criticality safety analysts should consider testing MCNP6.2 on their particular problems and validation suites. No further development of MCNP5 is planned. MCNP6.1 is now 4 years old, and MCNP6.1.1 is now 3 years old. In general, released versions of MCNP are supported only for about 5 years, due to resource limitations. All future MCNP improvements, bug fixes, user support, and new capabilities are targeted only to MCNP6.2 and beyond.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Vadose zone flow convergence test suite

Performance Assessment (PA) simulations for engineered disposal systems at the Savannah River Site involve highly contrasting materials and moisture conditions at and near saturation. These conditions cause severe convergence difficulties that typically result in unacceptable convergence or long simulation times or excessive analyst effort. Adequate convergence is usually achieved in a trial-anderror manner by applying under-relaxation to the Saturation or Pressure variable, in a series of everdecreasing RELAxation values. SRNL would like a more efficient scheme implemented inside PORFLOW to achieve flow convergence in a more reliable and efficient manner. To this end, a suite of test problems that illustrate these convergence problems is provided to facilitate diagnosis and development of an improved convergence strategy. The attached files are being transmitted to you describing the test problem and proposed resolution.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

SIERRA Code Coupling Module: Arpeggio User Manual Version 4.44

The SNL Sierra Mechanics code suite is designed to enable simulation of complex multiphysics scenarios. The code suite is composed of several specialized applications which can operate either in standalone mode or coupled with each other. Arpeggio is a supported utility that enables loose coupling of the various Sierra Mechanics applications by providing access to Framework services that facilitate the coupling. More importantly Arpeggio orchestrates the execution of applications that participate in the coupling. This document describes the various components of Arpeggio and their operability. The intent of the document is to provide a fast path for analysts interested in coupled applications via simple examples of its usage.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

GAMSOR: Gamma Source Preparation and DIF3D Flux Solution

Nuclear reactors that rely upon the fission reaction have two modes of thermal energy deposition in the reactor system: neutron absorption and gamma absorption. The gamma rays are typically generated by neutron capture reactions or during the fission process which means the primary driver of energy production is of course the neutron interaction. In conventional reactor physics methods, the gamma heating component is ignored such that the gamma absorption is forced to occur at the gamma emission site. For experimental reactor systems like EBR-II and FFTF, the placement of structural pins and assemblies internal to the core leads to problems with power heating predictions because there is no fission power source internal to the assembly to dictate a spatial distribution of the power. As part of the EBR-II support work in the 1980s, the GAMSOR code was developed to assist analysts in calculating the gamma heating. The GAMSOR code is a modified version of DIF3D and actually functions within a sequence of DIF3D calculations. The gamma flux in a conventional fission reactor system does not perturb the neutron flux and thus the gamma flux calculation can be cast as a fixed source problem given a solution to the steady state neutron flux equation. This leads to a sequence of DIF3D calculations, called the GAMSOR sequence, which involves solving the neutron flux, then the gamma flux, and then combining the results to do a summary edit. In this manuscript, we go over the GAMSOR code and detail how it is put together and functions. We also discuss how to setup the GAMSOR sequence and input for each DIF3D calculation in the GAMSOR sequence.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Developing the User Experience for a Next Generation Nuclear Fuel Cycle Simulator (NGFCS)

This project made substantial progress on its original aim for providing a modern user experience for nuclear fuel cycle analysis while also creating a robust and functional next- generation fuel cycle simulator. The Cyclus kernel experienced a dramatic clari cation of its interfaces and data model, becoming a full- edged agent-based framework, with strong support for third party developers of novel archetypes. The most important contribution of this project to the the development of Cyclus was the introduction of tools to facilitate archetype development. These include automated code generation of routine archetype components, metadata annotations to provide re ection and rich description of each data member's purpose, and mechanisms for input validation and output of complex data. A comprehensive social science investigation of decision makers' interests in nuclear fuel cycles, and speci cally their interests in nuclear fuel cycle simulators (NFCSs) as tools for understanding nuclear fuel cycle options, was conducted. This included document review and analysis, stakeholder interviews, and a survey of decision makers. This information was used to study the role of visualization formats and features in communicating information about nuclear fuel cycles. A exible and user-friendly tool was developed for building Cyclus analysis models, featuring a drag-and-drop interface and automatic input form generation for novel archetypes. Cycic allows users to design fuel cycles from arbitrary collections of facilities for the rst time, with mechanisms that contribute to consistency within that fuel cycle. Interacting with some of the metadata capabilities introduced in the above-mentioned tools to support archetype development, Cycic also automates the generation of user input forms for novel archetypes with little to no special knowledge required by the archetype developers. Translation of the fundamental metrics of Cyclus into more interesting quantities is accomplished in the Cymetric python package. This package is speci cally designed to support the introduction of new metrics by building upon existing metrics. This concept allows for multiple dependencies and encourages building complex metrics out of incremental transformations to those prior metrics. New archetype developers can contribute their own archetype-speci c metric using the same capability. A simple demonstration of this capability focused on generating time-dependent cash ows for reactor deployment that could then be analyzed in di erent ways. Cyclist, a dedicated application for exploration of Cyclus results, was developed. It's primary capabilities at this stage are best-suited to experienced fuel cycle analysts, but it provides a basic platform for simpler visualizations for other audiences. An important part of its interface is the ability to uidly examine di erent slices of what is fundamentally a ve-dimensional sparse data set. A drag-and-drop interface simpli es the process of selecting which data is displayed in the plot as well as which dimensions are being used for

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

MASTODON Theory Manual

Multi-hazard Analysis for STOchastic time-DOmaiN phenomena (MASTODON) is a finite element application that aims at analyzing the response of 3-D soil-structure systems to natural and man-made hazards such as earthquakes, floods and fire. MASTODON currently focuses on the simulation of seismic events and has the capability to perform extensive ‘source-to-site’ simulations including earthquake fault rupture, nonlinear wave propagation and nonlinear soil-structure interaction (NLSSI) analysis. MASTODON is being developed to be a dynamic probabilistic risk assessment framework that enables analysts to not only perform deterministic analyses, but also easily perform probabilistic or stochastic simulations for the purpose of risk assessment.

42 ENGINEERING↗

Characterizing Density and Complexity of Imported Cargos

X-ray inspection systems are used to detect radiological and nuclear threats in imported cargo. In order to better understand performance of these systems, system imaging capabilities and the characteristics of imported cargo need to be determined. This project involved calculation of the modulation transfer function as a metric of system imaging performance and a study of the density and inhomogeneity of imported cargos, which have been shown to correlate with human analysts, threat detection performance.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Regulatory Incentives and Disincentives for Utility Investments in Grid Modernization

Electric power is America's most capital-intensive industry, with more than $100 billion invested each year in energy infrastructure. Investment needs are likely to grow as electric utilities make power systems more reliable and resilient, deploy advanced digital technologies, and facilitate new services to meet some consumers' expectations for greater choice and control. But do current regulatory approaches provide the appropriate incentives for grid modernization investments? This report presents three perspectives: -Financial analyst Steve Kihm begins by explaining that any major investor-owned electric utility that wants to raise capital today can do so at a reasonable cost. The question is whether utility managers want to raise capital for grid modernization. Specifically, they look for investments that create the most value for their existing shareholders. In cases where grid modernization investments are not the best choice in terms of shareholder value, Kihm describes shareholder incentive mechanisms that regulators could consider to encourage such investments when they are in the public interest. -From an institutional perspective, Dr. Janice Beecher finds that the traditional rate-base/rate of return regulatory model provides powerful incentives for utilities to pursue investments, cost control, efficiency and even innovation, and it is well suited to the policy objectives of grid modernization. Prudence of grid modernization investments (fair returns) depends on careful evaluation of the specific asset, and any special incentives (bonus returns) should be used only if they promote economic efficiency consistent with the core goals of economic regulation. According to Beecher, realizing the promises of grid modernization depends on effective implementation of the traditional regulatory model and ratemaking tools to serve the public interest. -Conversely, former commissioner and clean energy consultant Ron Lehr says that rapid electric industry changes require a better alignment of utility investment incentives with changes challenging the electricity sector, emerging grid modernization options and benefits, and public policies. For example, investor-owned utilities typically have an incentive to make capital investments, but rarely to employ expense-based solutions, since utilities do not earn profits on expenses. Further, Lehr cites a variety of factors that stand in the way of creating well targeted and well aligned utility incentives, including litigated regulatory processes. These may be a poor choice for finding the right balance among competing interests, establishing rules of prospective application, justifying demonstrations of new technologies and approaches to meeting emerging consumer demands, and keeping pace with rapid change.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Modeling human comprehension of data visualizations

This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need for cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. We then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.

97 MATHEMATICS AND COMPUTING↗

Tracking the Sun 10: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States

Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing trends in the installed price of grid-connected, residential and non-residential systems solar photovoltaic (PV) systems in the United States. The present report, the tenth edition in the series, focuses on systems installed through year-end 2016, with preliminary data for the first half of 2017. The report provides an overview of both long-term and more-recent trends, highlighting key drivers for installed price declines over different time horizons. The report also extensively characterizes the widespread variability in system pricing, comparing installed prices across states, market segments, installers, and various system and technology characteristics. The trends described in this report derive from project-level data collected by state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data for this report were compiled and cleaned for more than 1.1 million individual PV systems, though the analysis in the report is based on a subset of that sample, consisting of roughly 630,000 systems with available installed price data. The full underlying dataset of project-level data (excluding any confidential information) is available in a public data file, for use by other researchers and analysts.

14 SOLAR ENERGY↗

Building Foundations for Nuclear Security Enterprise Analysis Utilizing Nuclear Weapon Data

The Nuclear Security Enterprise, managed by the National Nuclear Security Administration - a semiautonomous agency within the Department of Energy - has been associated with numerous assessments with respect to the estimating, management capabilities, and practices pertaining to nuclear weapon modernization efforts. This report identifies challenges in estimating and analyzing the Nuclear Security Enterprise through an analysis of analogous timeframe conditions utilizing two types of nuclear weapon data - (1) a measure of effort and (2) a function of time. The analysis of analogous timeframe conditions that utilizes only two types of nuclear weapon data yields four summary observations that estimators and analysts of the Nuclear Security Enterprise will find useful.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

CASL Dakota Capabilities Summary

The Dakota software project serves the mission of Sandia National Laboratories and supports a worldwide user community by delivering state-of-the-art research and robust, usable software for optimization and uncertainty quantification. These capabilities enable advanced exploration and riskinformed prediction with a wide range of computational science and engineering models. Dakota is the verification and validation (V&V) / uncertainty quantification (UQ) software delivery vehicle for CASL, allowing analysts across focus areas to apply these capabilities to myriad nuclear engineering analyses.

97 MATHEMATICS AND COMPUTING↗

Temporal Cyber Attack Detection.

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms require large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.

97 MATHEMATICS AND COMPUTING↗

Using Machine Learning to Predict MCNP Bias

For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental k eff ) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Sociocultural Behavior Influence Modelling & Assessment: Current Work and Research Frontiers.

A common problem associated with the effort to better assess potential behaviors of various individuals within different countries is the shear difficulty in comprehending the dynamic nature of populations, particularly over time and considering feedback effects. This paper discusses a theory-based analytical capability designed to enable analysts to better assess the influence of events on individuals interacting within a country or region. These events can include changes in policy, man-made or natural disasters, migration, war, or other changes in environmental/economic conditions. In addition, this paper describes potential extensions of this type of research to enable more timely and accurate assessments.

99 GENERAL AND MISCELLANEOUS↗

Integral Full Core Multi-Physics PWR Benchmark with Measured Data

In recent years, the importance of modeling and simulation has been highlighted extensively in the DOE research portfolio with concrete examples in nuclear engineering with the CASL and NEAMS programs. These research efforts and similar efforts worldwide aim at the development of high-fidelity multi-physics analysis tools for the simulation of current and next-generation nuclear power reactors. Like all analysis tools, verification and validation is essential to guarantee proper functioning of the software and methods employed. The current approach relies mainly on the validation of single physic phenomena (e.g. critical experiment, flow loops, etc.) and there is a lack of relevant multiphysics benchmark measurements that are necessary to validate high-fidelity methods being developed today. This work introduces a new multi-cycle full-core Pressurized Water Reactor (PWR) depletion benchmark based on two operational cycles of a commercial nuclear power plant that provides a detailed description of fuel assemblies, burnable absorbers, in-core fission detectors, core loading and re-loading patterns. This benchmark enables analysts to develop extremely detailed reactor core models that can be used for testing and validation of coupled neutron transport, thermal-hydraulics, and fuel isotopic depletion. The benchmark also provides measured reactor data for Hot Zero Power (HZP) physics tests, boron letdown curves, and three-dimensional in-core flux maps from 58 instrumented assemblies. The benchmark description is now available online and has been used by many groups. However, much work remains to be done on the quantification of uncertainties and modeling sensitivities. This work aims to address these deficiencies and make this benchmark a true non-proprietary international benchmark for the validation of high-fidelity tools. This report details the BEAVRS uncertainty quantification for the first two cycle of operations and serves as the final report of the project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Exemplar for simulation challenges: Large-deformation micromechanics of Sylgard 184/glass microballoon syntactic foams.

Sylgard® 184/Glass Microballoon (GMB) potting material is currently used in many NW systems. Analysts need a macroscale constitutive model that can predict material behavior under complex loading and damage evolution. To address this need, ongoing modeling and experimental efforts have focused on study of damage evolution in these materials. Micromechanical finite element simulations that resolve individual GMB and matrix components promote discovery and better understanding of the material behavior. With these simulations, we can study the role of the GMB volume fraction, time-dependent damage, behavior under confined vs. unconfined compression, and the effects of partial damage. These simulations are challenging and push the boundaries of capability even with the high performance computing tools available at Sandia. We summarize the major challenges and the current state of this modeling effort, as an exemplar of micromechanical modeling needs that can motivate advances in future computing efforts.

36 MATERIALS SCIENCE↗

Estimating the Value of Improved Distributed Photovoltaic Adoption Forecasts for Utility Resource Planning

Misforecasting the adoption of customer-owned distributed photovoltaics (DPV) can have operational and financial implications for utilities; forecasting capabilities can be improved, but generally at a cost. This paper informs this decision-space by using a suite of models to explore the capacity expansion and operation of the Western Interconnection over a 15-year period across a wide range of DPV growth rates and misforecast severities. The system costs under a misforecast are compared against the costs under a perfect forecast, to quantify the costs of misforecasting. Using a simplified probabilistic method applied to these modeling results, an analyst can make a first-order estimate of the financial benefit of improving a utility’s forecasting capabilities, and thus be better informed about whether to make such an investment. For example, under our base assumptions, a utility with 10 TWh per year of retail electric sales who initially estimates that DPV growth could range from 2% to 7.5% of total generation over the next 15 years could expect total present-value savings of approximately $4 million if they could reduce the severity of misforecasting to within ±25%. Utility resource planners can compare those savings against the costs needed to achieve that level of precision, to guide their decision on whether to make an investment in tools or resources.

14 SOLAR ENERGY↗