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At least 577 records · Page 32

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

Here, we implement a variance-based distance metric (D n ) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.

58 GEOSCIENCES↗

DOE OSTI2024

Hexagonal Lattices of HIV Capsid Proteins Explored by Simulations Based on a Thermodynamically Consistent Model

HIV capsid proteins (CAs) may self-assemble into a variety of shapes under in vivo and in vitro conditions. Here, we employed simulations based on a residue-level coarse-grained (CG) model with full conformational flexibility to investigate hexagonal lattices, which are the underlying structural pattern for CA aggregations. Facilitated by enhanced sampling simulations to rigorously calculate CA dimerization and polymerization affinities, we calibrated our model to reproduce the experimentally measured affinities. Here using the calibrated model, we performed unbiased simulations on several large systems consisting of 1512 CA subunits, allowing reversible binding and unbinding of the CAs in a thermodynamically consistent manner. In one simulation, a preassembled hexagonal CA sheet developed spontaneous curvatures reminiscent of those observed in experiments, and the edges of the sheet exhibited local curvatures larger than those of the interior. In other simulations starting with randomly distributed CAs at different concentrations, existing CA assemblies grew by binding free capsomeres to the edges and by merging with other assemblies. At high CA concentrations, rapid establishment of predominant aggregates was followed by much slower adjustments toward more regular hexagonal lattices, with increasing numbers of intact CA hexamers and pentamers being formed. Our approach of adapting a general CG model to specific systems by using experimental binding data represents a practical and effective strategy for simulating and elucidating intricate protein aggregations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Automated Generation of Graph-based Cyber Threat Intel

With the advancement of AI technology and tools, specifically in the cybersecurity domain, both cyber defenders and threat actors are continuously adapting the use of these capabilities to expedite their operations. With this phenomenon, threat intelligence that is up to date, refreshable, and has relevant context to a specific threat becomes more and more important as it enables cybersecurity professionals to gain insight into relevant data and relationships to guide their operations. This project enables users to frequently aggregate threat intelligence from various sources, such as vendor vulnerability advisories affecting critical infrastructure, malware reports, and adversary writeups into a centralized, standardized database. The project utilizes the Structured Threat Intelligence eXpression (STIX) for a standardized, shareable threat intelligence data format and Neo4j as a graph database solution to store STIX nodes and relationships. Initial results of the project include datasets of over 8,000 nodes and 20,000 relationships extracted from over 500 data sources that have been released within the past month.

Threat Intelligence↗

DOE OSTI2016

The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?

The PALS Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave, surface air temperature and relative humidity. These results are explored here in greater detail and possible causes are investigated. We examine whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. We demonstrate that energy conservation in the observational data is not responsible for these results. We also show that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. In conclusion, we present evidence suggesting that the nature of this partitioning problem is likely shared among all contributing LSMs. While we do not find a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER, we do exclude multiple possible explanations and provide guidance on where future research should focus.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

16 TIDAL AND WAVE POWER↗

DOE OSTI2016

Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies

This two-part paper considers the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using real-world weather data and price data, and is compared with other approaches in terms of aggregated power response. Simulation results indicate that our coordination framework can effectively improve the efficiency of the power grid operations and reduce power congestion at key times.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Small-angle X-Ray analysis of macromolecular structure: the structure of protein NS2 (NEP) in solution

A complex structural analysis of nuclear export protein NS2 (NEP) of influenza virus A has been performed using bioinformatics predictive methods and small-angle X-ray scattering data. The behavior of NEP molecules in a solution (their aggregation, oligomerization, and dissociation, depending on the buffer composition) has been investigated. It was shown that stable associates are formed even in a conventional aqueous salt solution at physiological рН value. For the first time we have managed to get NEP dimers in solution, to analyze their structure, and to compare the models obtained using the method of the molecular tectonics with the spatial protein structure predicted by us using the bioinformatics methods. The results of the study provide a new insight into the structural features of nuclear export protein NS2 (NEP) of the influenza virus A, which is very important for viral infection development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

NEUTRON-IRRADIATION–INDUCED DAMAGE ASSESSMENT IN CONCRETE USING COMBINED PHASE CHARACTERIZATION AND NONLINEAR FAST FOURIER TRANSFORM SIMULATION

Understanding irradiation effects in concrete cavities of light water reactors is critical to the long-term operation (60+ years) of US nuclear power plants. Susceptibility of concrete to neutron irradiation greatly varies as a function of its constituents: coarse aggregates, sand, and hardened cement paste (hcp). In particular, higher irradiation–susceptibility of aggregates was found to be a direct function of radiation-induced volumetric expansion (RIVE), or the propensity of aggregates to swell as a function of mineral contents, structures and textures. Irradiation-induced amorphization is accompanied by significant swelling, especially in silicates. For example, the maximum volumetric expansions of quartz and feldspars have been shown to be as large as ≈ 18% and ≈ 8%, respectively, while the change of density in calcite remains rather low (≈ 0.3–0.5%). High swelling is associated with a high number of covalent bonds (Si–O) and a high degree of polymerization of [SiO4] tetrahedrons. Differential RIVEs between rock-forming minerals cause high stresses and may potentially develop cracking within the aggregates at the interface between aggregates and the hcp and within the hcp. Modeling neutron-irradiation effects in concrete requires addressing the problem at a scale that captures both the details of the aggregates’ mineralogy and texture, which is <~ 100 μm, and the representative size of concrete, which is ~ 10 cm. This requires high-resolution, large mapping capabilities. To this end, the US Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program developed an original tool suite called Microstructure Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) in which Nonlinear 2D-fast Fourier transform (FFT) techniques are employed to model mechanical or irradiation-induced cracking of high-resolution complex microstructures derived from combined mapping techniques, including micro x-ray fluorescence (m-XRF), energy-dispersive x-ray spectroscopy (EDS), electron backscatter diffraction (EBSD), ellipsometry, and scanning electron microscopy (SEM). Irradiated mineral properties are derived from the Irradiated Minerals, Aggregate and Concrete (IMAC) database, which includes available relevant literature data. Rigorous coupled damage-creep simulations are obtained through sequential linear analysis. Illustrations of actual accelerated irradiation-damage propagation in a complex microstructures are presented herein.

Le Pape, Yann↗

DOE OSTI2019

PV Cyber Security Research (Final Report)

Extensive deployment of interoperable distributed energy resources (DER) on power systems is increasing the power system cyber security attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid support functions which can drastically influence power quality, voltage, and bulk system frequency. This project was split into two phases. The first provided a survey and roadmap of the cybersecurity for the solar industry. The second investigated multiple PV cybersecurity research and development (R&D) concepts identified in the first phase. In the first year, the team created a roadmap for improving cybersecurity for distributed solar energy resources. This roadmap was intended to provide direction for the nation over the next five years and focused on the intersection of industry and government and recommends activities in four related areas: stakeholder engagement, cyber security research and development, standards development, and industry best practices. At the same time, the team produced a primer for DER vendors, aggregators, and grid operators to establish a common taxonomy and describe basic principles of cyber security, encryption, communication protocols, DER cyber security recommendations and requirements, and device-, aggregator-, and utility-level security best practices to ensure data confidentiality, integrity, and availability. This material was motivated by the need to assist the broader PV industry with cybersecurity resilience and describe the state-of-the-art for securing DER communications. Lastly, an adversary-based assessment of multiple PV devices was completed at the Distributed Energy Technologies Laboratory at Sandia National Laboratories to determine the status of industry cybersecurity practices. The team found multiple deficiencies in the security features of the assessed devices. In the second year, a set of recommendations was created for DER communication protocols— especially with respect to the state-of-the-art requirements in IEEE 2030.5. Additionally, several cybersecurity R&D technologies related to communications-enabled photovoltaic systems were studied to harden DER communication networks. Specifically, the team investigated (a) using software defined networking to create a moving target defense system for DER communications, and (b) engineering controls that prevent misprogramming or adversary action on DER devices/networks by disallowing setpoints that will generate unstable power system operations.

14 SOLAR ENERGY↗

DOE OSTI2017

Cyber Security Primer for DER Vendors Aggregators and Grid Operators

This report provides an introduction to cyber security for distributed energy resources (DER) - such as photovoltaic (PV) inverters and energy storage systems (ESS). This material is motivated by the need to assist DER vendors, aggregators, grid operators, and broader PV industry with cyber security resilience and describe the state-of-the-art for securing DER communications. The report outlines basic principles of cyber security, encryption, communication protocols, DER cyber security recommendations and requirements, and device-, aggregator-, and utility-level security best practices to ensure data confidentiality, integrity, and availability. Example cyber security attacks, including eavesdropping, masquerading, man-in-the-middle, replay attacks, and denial-of-service are also described. A survey of communication protocols and cyber security recommendations used by the DER and power system industry are included to elucidate the cyber security standards landscape. Lastly, a roadmap is presented to harden end-to-end communications for DER with research and industry engagement.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

SIMULATED HUMAN ERROR PROBABILITY AND ITS APPLICATION TO DYNAMIC HUMAN FAILURE EVENTS

Abstract Objectives: Human reliability analysis (HRA) methods typically analyze human failure events (HFEs) at the overall task level. For dynamic HRA, it is important to model human activities at the subtask level. There exists a disconnect between dynamic subtask level and static task level that presents issues when modeling dynamic scenarios. For example, the SPAR-H method is typically used to calculate the human error probability (HEP) at the task level. As demonstrated in this paper, quantification in SPAR-H does not translate to the subtask level. Methods: Two different discrete distributions were generated for each SPAR-H Performance Shaping Factor (PSF) to define the frequency of PSF levels. The first distribution was a uniform, or uninformed distribution that assumed the frequency of each PSF level was equally likely. The second non-continuous distribution took the frequency of PSF level as identified from an assessment of the HERA database. These two different approaches were created to identify the resulting distribution of the HEP. The resulting HEP that appears closer to the known distribution, a log-normal centered on 1E-3, is the more desirable. Each approach then has median, average and maximum HFE calculations applied. To calculate these three values, three events, A, B and C are generated from the PSF level frequencies comprised of subtasks. The median HFE selects the median PSF level from each PSF and calculates HEP. The average HFE takes the mean PSF level, and the maximum takes the maximum PSF level. The same data set of subtask HEPs yields starkly different HEPs when aggregated to the HFE level in SPAR-H. Results: Assuming that each PSF level in each HFE is equally likely creates an unrealistic distribution of the HEP that is centered at 1. Next the observed frequency of PSF levels was applied with the resulting HEP behaving log-normally with a majority of the values under 2.5% HEP. The median, average and maximum HFE calculations did yield different answers for the HFE. The HFE maximum grossly over estimates the HFE, while the HFE distribution occurs less than HFE median, and greater than HFE average. Conclusions: Dynamic task modeling can be perused through the framework of SPAR-H. Identification of distributions associated with each PSF needs to be defined, and may change depending upon the scenario. However it is very unlikely that each PSF level is equally likely as the resulting HEP distribution is strongly centered at 100%, which is unrealistic. Other distributions may need to be identified for PSFs, to facilitate the transition to dynamic task modeling. Additionally discrete distributions need to be exchanged for continuous so that simulations for the HFE can further advance. This paper provides a method to explore dynamic subtask to task translation and provides examples of the process using the SPAR-H method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Effects of ferumoxytol on quantitative PET measurements in simultaneous PET/MR whole-body imaging: a pilot study in a baboon model

Background: Simultaneous PET/MR imaging depends on MR-derived attenuation maps (mu-maps) for accurate attenuation correction of PET data. Currently, these maps are derived from gradient-echo-based MR sequences, which are sensitive to susceptibility changes. Iron oxide magnetic nanoparticles have been used in the measurement of blood volume, tumor microvasculature, tumor-associated macrophages, and characterizing lymph nodes. Our aim in this study was to assess whether the susceptibility effects associated with iron oxide nanoparticles can potentially affect measured 18 F-FDG PET standardized uptake values (SUV) through effects on MR-derived attenuation maps. Methods: The study protocol was approved by the Institutional Animal Care and Use Committee. Using a Siemens Biograph mMR PET/MR scanner, we evaluated the effects of increasing concentrations of ferumoxytol and ferumoxytol aggregates on MR-derived mu-maps using an agarose phantom. In addition, we performed a baboon experiment evaluating the effects of a single i.v. ferumoxytol dose (10 mg/kg) on the liver, spleen, and pancreas 18 F-FDG SUV at baseline (ferumoxytol-naïve), within the first hour and at 1, 3, 5, and 11 weeks. Results: Phantom experiments showed mu-map artifacts starting at ferumoxytol aggregate concentrations of 10 to 20 mg/kg. The in vivo baboon data demonstrated a 53% decrease of observed 18 F-FDG SUV compared to baseline within the first hour in the liver, persisting at least 11 weeks. Conclusions: A single ferumoxytol dose can affect measured SUV for at least 3 months, which should be taken into account when administrating ferumoxytol in patients needing sequential PET/MR scans.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Cover crops and poultry litter impact on soil structural stability in dryland soybean production in southeastern United States

Abstract This study explored the efficacy of soil aggregate indices in quantifying soil structural development, utilizing 5‐year field experiment data from the Southeastern United States. The experiment utilized a split‐plot design with cover crops (native vegetation as control, cereal rye (Secale cerealeL.), winter wheat (Triticum aestivum), hairy vetch (Vicia villosa), and mustard (Brassica rapa) plus cereal rye as the main factor and fertilizer source (no fertilizer as control, inorganic fertilizer with phosphorus, potassium, and elemental sulfur, and poultry litter) as the secondary factor. Aggregate size fractions were determined using the wet‐sieving method, and aggregate stability index (ASI), mean weight diameter (MWD), geometric mean diameter (GMD), and fractal dimension (FD) were calculated to assess soil structural stability. Main effects results indicated that cereal rye (55.11%) and poultry litter (50.97%) exhibited the highest ASI values. The highest MWD, GMD, and FD were observed under mustard plus cereal rye (1.187 mm), cereal rye (0.462 mm), and hairy vetch (2.573), respectively. Principal component analysis revealed that cover crops significantly improved soil aggregate structure and stability, overcoming limitations of sole fertilization practices. Regression analysis suggested that ASI, MWD, and GWD positively correlated with soil organic carbon, whereas FD negatively correlated with MWD, GMD, and ASI. Principal component analysis exhibited that FD decreased with increasing soil organic carbon, ASI, MWD, and GMD, demonstrating that lower FD values indicate enhanced soil aggregation and structure. Assessed indices, FD included, effectively gauged soil structural stability. These metrics should be prioritized in managerial decisions to support soil productivity and health in agricultural systems.

Agriculture↗

DOE OSTI2017

U.S. Solar Market Value Report: Further Evidence that Solar Adds Value to Real Estate

Solar photovoltaic systems provide cost savings to the property owner in terms of avoided electricity costs that accrue over the system lifetime. From an investment standpoint, the equipment and the value of the energy generated can potentially increase the underlying property value. This first-of-a-kind study presents real market data collected from real estate appraisers using the PV Value® tool to develop a market value for solar as part of a property sale or refinance. Aggregated results at the state level are discussed for California, Arizona and Massachusetts, using 2015 and 2016 data where appraisers used the income capitalization approach to develop a market value for solar. Additional data collection using future transaction data could reveal market-specific trends and insights at the zip code, city and metropolitan statistical area (MSA) levels.

14 SOLAR ENERGY↗

DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗

DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗

DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗