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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Morpheus Spectral Counter: A computational tool for label-free quantitative mass spectrometry using the Morpheus search engine

Label-free quantitative MS based on the Normalized Spectral Abundance Factor (NSAF) has emerged as a straightforward and robust method to determine the relative abundance of individual proteins within complex mixtures. Here, we present Morpheus Spectral Counter (MSpC) as the first computational tool that directly calculates NSAF values from output obtained from Morpheus, a fast, open-source, peptide-MS/MS matching engine compatible with high-resolution accurate-mass instruments. NSAF has distinct advantages over other MS-based quantification methods, including a higher dynamic range as compared to isobaric tags, no requirement to align and re-extract MS1 peaks, and increased speed. MSpC features an easy to use graphic user interface that additionally calculates both distributed and unique NSAF values to permit analyses of both protein families and isoforms/proteoforms. MSpC determinations of protein concentration were linear over several orders of magnitude based on the analysis of several high-mass accuracy datasets either obtained from PRIDE or generated with total cell extracts spiked with purified Arabidopsis 20S proteasomes. Lastly, the MSpC software was developed in C# and is open sourced under a permissive license with the code made available at http://dcgemperline.github.io/Morpheus_SpC/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations

Abstract Gamma distributed delay differential equations (DDEs) arise naturally in many modelling applications. However, appropriate numerical methods for generic gamma distributed DDEs have not previously been implemented. Modellers have therefore resorted to approximating the gamma distribution with an Erlang distribution and using the linear chain technique to derive an equivalent system of ordinary differential equations (ODEs). In this work, we address the lack of appropriate numerical tools for gamma distributed DDEs in two ways. First, we develop a functional continuous Runge–Kutta (FCRK) method to numerically integrate the gamma distributed DDE without resorting to Erlang approximation. We prove the fourth-order convergence of the FCRK method and perform numerical tests to demonstrate the accuracy of the new numerical method. Nevertheless, FCRK methods for infinite delay DDEs are not widely available in existing scientific software packages. As an alternative approach to solving gamma distributed DDEs, we also derive a hypoexponential approximation of the gamma distributed DDE. This hypoexponential approach is a more accurate approximation of the true gamma distributed DDE than the common Erlang approximation but, like the Erlang approximation, can be formulated as a system of ODEs and solved numerically using standard ODE software. Using our FCRK method to provide reference solutions, we show that the common Erlang approximation may produce solutions that are qualitatively different from the underlying gamma distributed DDE. However, the proposed hypoexponential approximations do not have this limitation. Finally, we apply our hypoexponential approximations to perform statistical inference on synthetic epidemiological data to illustrate the utility of the hypoexponential approximation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WfBench: Automated Generation of Scientific Workflow Benchmarks

The prevalence of scientific workflows with high computational demands calls for their execution on various distributed computing platforms, including large-scale leadership-class high-performance computing (HPC) clusters. To handle the deployment, monitoring, and optimization of workflow executions, many workflow systems have been developed over the past decade. There is a need for workflow benchmarks that can be used to evaluate the performance of workflow systems on current and future software stacks and hardware platforms.We present a generator of realistic workflow benchmark specifications that can be translated into benchmark code to be executed with current workflow systems. Our approach generates workflow tasks with arbitrary performance characteristics (CPU, memory, and I/O usage) and with realistic task dependency structures based on those seen in production workflows. We present experimental results that show that our approach generates benchmarks that are representative of production workflows, and conduct a case study to demonstrate the use and usefulness of our generated benchmarks to evaluate the performance of workflow systems under different configuration scenarios.

Coleman, Taina↗
DOE OSTI2011

Final Report: Towards Optimal Petascale Simulations (TOPS), ER25785

Multiscale, multirate scientific and engineering applications in the SciDAC portfolio possess resolution requirements that are practically inexhaustible and demand execution on the highest-capability computers available, which will soon reach the petascale. While the variety of applications is enormous, their needs for mathematical software infrastructure are surprisingly coincident; moreover the chief bottleneck is often the solver. At their current scalability limits, many applications spend a vast majority of their operations in solvers, due to solver algorithmic complexity that is superlinear in the problem size, whereas other phases scale linearly. Furthermore, the solver may be the phase of the simulation with the poorest parallel scalability, due to intrinsic global dependencies. This project brings together the providers of some of the world's most widely distributed, freely available, scalable solver software and focuses them on relieving this bottleneck for many specific applications within SciDAC, which are representative of many others outside. Solver software directly supported under TOPS includes: hypre, PETSc, SUNDIALS, SuperLU, TAO, and Trilinos. Transparent access is also provided to other solver software through the TOPS interface. The primary goals of TOPS are the development, testing, and dissemination of solver software, especially for systems governed by PDEs. Upon discretization, these systems possess mathematical structure that must be exploited for optimal scalability; therefore, application-targeted algorithmic research is included. TOPS software development includes attention to high performance as well as interoperability among the solver components. Support for integration of TOPS solvers into SciDAC applications is also directly supported by this proposal. The role of the UCSD PI in this overall CET, is one of direct interaction between the TOPS software partners and various DOE applications scientists' specifically toward magnetohydrodynamics (MHD) simulations with the Center for Extended Magnetohydrodynamic Modeling (CEMM) SciDAC and Applied Partial Differential Equations Center (APDEC) SciDAC, and toward core-collapse supernova simulations with the previous Terascale Supernova Initiative (TSI) SciDAC and in continued work on INCITE projects headed by Doug Swesty, SUNY Stony Brook. In addition to these DOE applications scientists, the UCSD PI works to bring leading-edge DOE solver technology to applications scientists in cosmology and large-scale galactic structure formation. Unfortunately, the funding for this grant ended after only two years of its five-year duration, in August 2008, due to difficulties at DOE in transferring the grant to the PI's new faculty position at Southern Methodist University. Therefore, this report only describes two years' worth of effort.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Calculating uncertainty on K-effective with MONK10 - 14583

Criticality safety assessments require a demonstration that a particular configuration of fissile material has an adequate sub-critical margin (k-effective sufficiently below unity) to ensure that the risk of criticality under normal operation and accident conditions is acceptable. The required sub-critical margin depends upon the uncertainty in the estimated value of k-effective. The uncertainty in the calculated value of k-effective arises from a number of sources, including: manufacturing tolerances on input data to the code (affecting geometry, compositions and densities), uncertainty in the nuclear data used by the code, stochastic uncertainty resulting from Monte Carlo simulation and modelling approximations/errors, including the inevitable bugs in the software. The ANSWERS Software Service, in collaboration with industrial partners, is developing a number of techniques to better understand and quantify uncertainty on predicted values of k-effective, using MONK. The SPRUCE utility code has been developed to allow uncertainty to be estimated using sampling methods. This can include the sampling of input parameters (including dimensions, compositions and densities) from statistical distributions. It can also include sampling different nuclear data libraries. A set of nuclear data libraries has been generated for this purpose by sampling from statistical distributions that represent the uncertainties in the published nuclear data evaluated files; a set of libraries has been produced for Latin Hypercube Sampling. By varying the input data and nuclear data, separate and combined uncertainties due to manufacturing tolerances and nuclear data can be derived. By performing least squares fitting on the results it is also possible to estimate the contribution of each of the uncertain inputs and a sensitivity method in MONK can break down the nuclear data uncertainty. (authors)

42 ENGINEERING↗
DOE OSTI2016

Making the Grid "Smart" Through "Smart" Microgrids: Real-Time Power Management of Microgrids with Multiple Distributed Generation Sources Using Intelligent Control

In this Project we collaborated with two DOE National Laboratories, Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBL). Dr. Hammerstrom of PNNL initially supported our project and was on the graduate committee of one of the Ph.D. students (graduated in 2014) who was supported by this project. He is also a committee member of a current graduate student of the PI who was supported by this project in the last two years (August 2014-July 2016). The graduate student is now supported be the Electrical and Computer Engineering (ECE) Department at Montana State University (MSU). Dr. Chris Marney of LBL provided actual load data, and the software WEBOPT developed at LBL for microgrid (MG) design for our project. NEC-Labs America, a private industry, also supported our project, providing expert support and modest financial support. We also used the software “HOMER,” originally developed at the National Renewable Energy Laboratory (NREL) and the most recent version made available to us by HOMER Energy, Inc., for MG (hybrid energy system) unit sizing. We compared the findings from WebOpt and HOMER and designed appropriately sized hybrid systems for our case studies. The objective of the project was to investigate real-time power management strategies for MGs using intelligent control, considering maximum feasible energy sustainability, reliability and efficiency while, minimizing cost and undesired environmental impact (emissions). Through analytic and simulation studies, we evaluated the suitability of several heuristic and artificial-intelligence (AI)-based optimization techniques that had potential for real-time MG power management, including genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and multi-agent systems (MAS), which is based on the negotiation of smart software-based agents. We found that PSO and MAS, in particular, distributed MAS, were more efficient and better suited for our work. We investigated the following: • Intelligent load control - demand response (DR) - for frequency stabilization in islanded MGs (partially supported by PNNL). • The impact of high penetration of solar photovoltaic (PV)-generated power at the distribution level (partially supported by PNNL). • The application of AI approaches to renewable (wind, PV) power forecasting (proposed by the reviewers of our proposal). • Application of AI approaches and DR for real-time MG power management (partially supported by NEC Labs-America) • Application of DR in dealing with the variability of wind power • Real-time MG power management using DR and storage (partially supported by NEC Labs-America) • Application of DR in enhancing the performance of load-frequency controller • MAS-based whole-sale and retail power market design for smart grid A

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

GridPACK: Library Architecture and Usability Assessment

The LLNL team has carried out extensive analysis of GridPACK code, including two major code reviews (each being a 2-day event) and a vendor emulation exercise, where the team integrated GridPACK library within a third-party application. The code analysis identified a number of areas for improvement of GridPACK code architecture, but it did not find fundamental flaws in the GridPACK design. The team found the GridPACK strategy of developing a software development kit (SDK) for power systems application development to be sound and well aligned with Office of Electricity long term objectives. The SDK aims to provide power systems software vendors an easy access to high performance computing (HPC) libraries.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

DOE SciDAC's Earth System Grid Center for Enabling Technologies Final Report

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy's (DOE's) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the global-climate community, and - assuming its development continues - we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others. This ongoing effort, though daunting in scope and complexity, will greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful. Regretfully, we submit our final report at the end of project funding. To continue to serve the climate-science community, we are currently seeking additional funding. Such funding would allow us to maintain and enhance ESGF production and operation of this vital endeavor of cataloging, serving, and analyzing ultra-scale climate science data. At this time, the entire ESG-CET team would like to take this opportunity to sincerely thank our funding agencies in the DOE Scientific Discovery through Advanced Computing (SciDAC) program and the Office of Biological and Environmental Research (OBER) - as well as our national and international collaborators, stakeholders, and partners - for allowing us to work with you and serve the community these past several years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

DOE SciDAC’s Earth System Grid Center for Enabling Technologies Final Report for University of Southern California Information Sciences Institute

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy’s (DOE’s) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the globalclimate community, and—assuming its development continues—we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP Data Access Protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others—this is of course if funding continues at some level. This ongoing effort, though daunting in scope and complexity, would greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2012

Distribution Integrity Management Plan (DIMP): Los Alamos National Laboratory Natural Gas Distribution System

This document is the distribution integrity management plan (Plan) for the Los Alamos National Laboratory (LANL) Natural Gas Distribution System. This Plan meets the requirements of 49 CFR Part 192, Subpart P Distribution Integrity Management Programs (DIMP) for the LANL Natural Gas Distribution System. This Plan was developed by reviewing records and interviewing LANL personnel. The records consist of the design, construction, operation and maintenance for the LANL Natural Gas Distribution System. The records system for the LANL Natural Gas Distribution System is limited, so the majority of information is based on the judgment of LANL employees; the maintenance crew, the Corrosion Specialist and the Utilities and Infrastructure (UI) Civil Team Leader. The records used in this report are: Pipeline and Hazardous Materials Safety Administration (PHMSA) 7100.1-1, Report of Main and Service Line Inspection, Natural Gas Leak Survey, Gas Leak Response Report, Gas Leak and Repair Report, and Pipe-to-Soil Recordings. The specific elements of knowledge of the infrastructure used to evaluate each threat and prioritize risks are listed in Sections 6 and 7, Threat Evaluation and Risk Prioritization respectively. This Plan addresses additional information needed and a method for gaining that data over time through normal activities. The processes used for the initial assessment of Threat Evaluation and Risk Prioritization are the methods found in the Simple, Handy Risk-based Integrity Management Plan (SHRIMP™) software package developed by the American Pipeline and Gas Agency (APGA) Security and Integrity Foundation (SIF). SHRIMP™ uses an index model developed by the consultants and advisors of the SIF. Threat assessment is performed using questions developed by the Gas Piping Technology Company (GPTC) as modified and added to by the SHRIMP™ advisors. This Plan is required to be reviewed every 5 years to be continually refined and improved. Records for all piping system installed after the effective date of this Plan will be captured and retained in the UI records documentation system. Primary Utility Asbuilts are maintained by Utilities Mapping (UMAP) and additional records are maintained on the N drive. Engineering Change Notices (ECNs) are stored on the N drive under configuration management and kept up by Utilities and Infrastructure Division Office (UI-DO). Records include, at a minimum, the location where new piping and appurtenances are installed and the material of which they are constructed.

03 NATURAL GAS↗
DOE OSTI2012

A statistically defined anthropomorphic software breast phantom

Purpose: Digital anthropomorphic breast phantoms have emerged in the past decade because of recent advances in 3D breast x-ray imaging techniques. Computer phantoms in the literature have incorporated power-law noise to represent glandular tissue and branching structures to represent linear components such as ducts. When power-law noise is added to those phantoms in one piece, the simulated fibroglandular tissue is distributed randomly throughout the breast, resulting in dense tissue placement that may not be observed in a real breast. The authors describe a method for enhancing an existing digital anthropomorphic breast phantom by adding binarized power-law noise to a limited area of the breast. Methods: Phantoms with (0.5 mm){sup 3} voxel size were generated using software developed by Bakic et al. Between 0% and 40% of adipose compartments in each phantom were replaced with binarized power-law noise ({beta} = 3.0) ranging from 0.1 to 0.6 volumetric glandular fraction. The phantoms were compressed to 7.5 cm thickness, then blurred using a 3 Multiplication-Sign 3 boxcar kernel and up-sampled to (0.1 mm){sup 3} voxel size using trilinear interpolation. Following interpolation, the phantoms were adjusted for volumetric glandular fraction using global thresholding. Monoenergetic phantom projections were created, including quantum noise and simulated detector blur. Texture was quantified in the simulated projections using power-spectrum analysis to estimate the power-law exponent {beta} from 25.6 Multiplication-Sign 25.6 mm{sup 2} regions of interest. Results: Phantoms were generated with total volumetric glandular fraction ranging from 3% to 24%. Values for {beta} (averaged per projection view) were found to be between 2.67 and 3.73. Thus, the range of textures of the simulated breasts covers the textures observed in clinical images. Conclusions: Using these new techniques, digital anthropomorphic breast phantoms can be generated with a variety of glandular fractions and patterns. {beta} values for this new phantom are comparable with published values for breast tissue in x-ray projection modalities. The combination of conspicuous linear structures and binarized power-law noise added to a limited area of the phantom qualitatively improves its realism.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

CBRN Situational Awareness Tools for the Modern Age

CBRN Situational awareness (SA) should be a key operational requirement in the CNI area of discussion, not only for the “boots-on-the-ground” warfighter, but at a strategic level. For example, maneuver plans will need to be developed for contamination avoidance and battlefield survivability. Large datasets from instruments dispersed across the battlefield will thus need to be collected and processed into a holistic assessment of the radiological hazard. From there, hazard maps then must be distributed at the tactical level, all while continuing to effectively engage a peer adversary. This vision represents a network of sensors, software tools, data processing algorithms, communications suites, and common operational picture (COP) platforms working in concert to inform warfighters and command echelons alike of evolving nuclear and radiological hazards. This complex system-of-systems and the ultimate goal of effective engagement in a nuclear or radiological-hazard battlefield can be achieved through pursuit of improvement in collaborative SA tools.

Becker, Eric M.↗
DOE OSTI2012

The Multi-Isotope Process (MIP) Monitor Project: FY12 Progress and Accomplishments

The Multi-Isotope Process (MIP) Monitor, being developed at Pacific Northwest National Laboratory (PNNL), provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of "...(minimization of) the risks of nuclear proliferation and terrorism." The MIP Monitor measures distributions of a suite of indicator (radioactive) isotopes present within product and waste streams of a nuclear reprocessing facility. These indicator isotopes are monitored on-line by gamma spectrometry and compared, in near-real-time, to spectral patterns representing "normal" process conditions using multivariate pattern recognition software. The monitor utilizes this multivariate analysis and gamma spectroscopy of reprocessing streams to detect small changes in the gamma spectrum, which may indicate changes in process conditions. Multivariate analysis methods common in chemometrics, such as principal component analysis (PCA) and partial least squares regression (PLS), act as pattern recognition techniques, which can detect small deviations from the expected, nominal condition. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting. Development of the MIP Monitor approach continues to evaluate the efficacy of the monitor for automated, real-time or near-real-time application. This report details follow-on research and development efforts sponsored by the U.S. Department of Energy Fuel Cycle Research and Development related to the MIP Monitor for fiscal year 2012 (FY12).

fuel characterization↗
DOE OSTI2016

Staghorn: An Automated Large-Scale Distributed System Analysis Platform

Conducting experiments on large-scale distributed computing systems is becoming significantly easier with the assistance of emulation. Researchers can now create a model of a distributed computing environment and then generate a virtual, laboratory copy of the entire system composed of potentially thousands of virtual machines, switches, and software. The use of real software, running at clock rate in full virtual machines, allows experiments to produce meaningful results without necessitating a full understanding of all model components. However, the ability to inspect and modify elements within these models is bound by the limitation that such modifications must compete with the model, either running in or alongside it. This inhibits entire classes of analyses from being conducted upon these models. We developed a mechanism to snapshot an entire emulation-based model as it is running. This allows us to \freeze time" and subsequently fork execution, replay execution, modify arbitrary parts of the model, or deeply explore the model. This snapshot includes capturing packets in transit and other input/output state along with the running virtual machines. We were able to build this system in Linux using Open vSwitch and Kernel Virtual Machines on top of Sandia's emulation platform Firewheel. This primitive opens the door to numerous subsequent analyses on models, including state space exploration, debugging distributed systems, performance optimizations, improved training environments, and improved experiment repeatability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

MDSplus quality improvement project

MDSplus is a data acquisition and analysis system used worldwide predominantly in the fusion research community. Development began 29 years ago on the OpenVMS operating system. Since that time there have been many new features added and the code has been ported to many different operating systems. There have been contributions to the MDSplus development from the fusion community in the way of feature suggestions, feature implementations, documentation and porting to different operating systems. The bulk of the development and support of MDSplus, however, has been provided by a relatively small core developer group of three or four members. Given the size of the development team and the large number of users much more effort was focused on providing new features for the community than on keeping the underlying code and documentation up to date with the evolving software development standards. To ensure that MDSplus will continue to provide the needs of the community in the future, the MDSplus development team along with other members of the MDSplus user community has commenced on a major quality improvement project. The planned improvements include changes to software build scripts to better use GNU Autoconf and Automake tools, refactoring many of the source code modules using new language features available in modern compilers, using GNU MinGW-w64 to create MS Windows distributions, migrating to a more modern source code management system, improvement of source documentation as well as improvements to the www.mdsplus.org web site documentation and layout, and the addition of more comprehensive test suites to apply to MDSplus code builds prior to releasing installation kits to the community. This paper should lead to a much more robust product and establish a framework to maintain stability as more enhancements and features are added. Finally, this paper will describe these efforts that are either in progress or planned for the near future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Forecasting Marine Sediment Properties with Geospatial Machine Learning

Using a combination of geospatial machine learning prediction and sediment thermodynamic/physical modeling, we have developed a novel software workflow to create probabilistic maps of geoacoustic and geomechanical sediment properties of the global seabed. This new technique for producing reliable estimates of seafloor properties can better support Naval operations relying on sonar performance and seabed strength, can constrain models of shallow tomographic structure important for nuclear treaty compliance monitoring/detection, and can provide constraints on the distribution and inventory of shallow methane gas and gas hydrate accumulations on the continental shelves.

58 GEOSCIENCES↗
DOE OSTI2012

A COUPLED TH/NEUTRONICS/CRUD FRAMEWORK IN PREDICTION OF CIPS PHENOMENON

A coupled TH/Neutronics/CRUD framework, which is able to simulate the CRUD deposits impact on CIPS phenomenon, was described in this paper. This framework includes the coupling among three essential physics, thermal-hydraulics, CRUD and neutronics. The overall framework was implemented by using the CFD software STAR-CCM+, developing CRUD codes, and using the neutronics code DeCART. The coupling was implemented by exchanging data between softwares using intermediate exchange files. A typical 3 by 3 PWR fuel pin problem was solved under this framework. The problem was solved in a 12 months length period of time. Time-dependent solutions were provided, including CRUD deposits inventory and their distributions on fuels, boron hideout amount inside CRUD deposits, as well as power shape changing over time. The results clearly showed the power shape suppression in regions where CRUD deposits exist, which is a strong indication of CIPS phenomenon.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗