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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 OSTI2015

Detection Of Special Nuclear Materials Tagged Neutrons

In order to detect Special Nuclear Materials (SNM) in unattended luggage or cargo containers in the field of homeland security, fissions are induced by 14 MeV neutrons produced by an associated particle DT neutron generator, and prompt fission particles correlated with tagged neutron are detected by plastic scintillators. SMN produce high multiplicity events due to induced fissions, whereas nonnuclear materials produce low multiplicity events due to cross-talk, (n,2n) or (n,n'γ) reactions. The data acquisition electronics is made of compact FPGA boards. The coincidence window is triggered by the alpha particle detection, allowing to tag the emission date and direction of the 14 MeV interrogating neutron. The first part of the paper presents experiment vs. calculation comparisons to validate MCNP-PoliMi simulations and the post-processing tools developed with the data analysis framework ROOT. Measurements have been performed using different targets (iron, lead, graphite), first with small plastic scintillators (10 x 10 x 10 cm{sup 3}) and then with large detectors (10 x 10 x 100 cm{sup 3}) to demonstrate that nuclear materials can be differentiated from nonnuclear dense materials (iron, lead) in iron and wood matrixes. A special attention is paid on SNM detection in abandoned luggage. In the second part of the paper, the performances of a cargo container inspection system are studied by numerical simulation, following previous work reported in. Detectors dimensions and shielding against the neutron generator background are optimized for container inspection. Events not correlated to an alpha particle (uncorrelated background), counting statistics, time and energy resolutions of the data acquisition system are all taken into account in a realistic numerical model. The impact of the container matrix (iron, ceramic, wood) has been investigated by studying the system capability to detect a few kilograms of SNM in different positions in the cargo container, within 10 min acquisitions. (authors)

36 MATERIALS SCIENCE↗
NASA NTRS2009

Determining Surface Roughness in Urban Areas Using Lidar Data

An automated procedure has been developed to derive relevant factors, which can increase the ability to produce objective, repeatable methods for determining aerodynamic surface roughness. Aerodynamic surface roughness is used for many applications, like atmospheric dispersive models and wind-damage models. For this technique, existing lidar data was used that was originally collected for terrain analysis, and demonstrated that surface roughness values can be automatically derived, and then subsequently utilized in disaster-management and homeland security models. The developed lidar-processing algorithm effectively distinguishes buildings from trees and characterizes their size, density, orientation, and spacing (see figure); all of these variables are parameters that are required to calculate the estimated surface roughness for a specified area. By using this algorithm, aerodynamic surface roughness values in urban areas can then be extracted automatically. The user can also adjust the algorithm for local conditions and lidar characteristics, like summer/winter vegetation and dense/sparse lidar point spacing. Additionally, the user can also survey variations in surface roughness that occurs due to wind direction; for example, during a hurricane, when wind direction can change dramatically, this variable can be extremely significant. In its current state, the algorithm calculates an estimated surface roughness for a square kilometer area; techniques using the lidar data to calculate the surface roughness for a point, whereby only roughness elements that are upstream from the point of interest are used and the wind direction is a vital concern, are being investigated. This technological advancement will improve the reliability and accuracy of models that use and incorporate surface roughness.

Holland, Donald↗
DOE OSTI2019

DATA-DRIVEN ALGORITHMS FOR DISTRIBUTION SYSTEM OPERATION AND CONTROL

Transactive energy systems are an alternative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources(DERs) and resulting much required scalability in managing active distribution system. To meet the reliability, sustainability and security requirement, ecient management and control of DERs including distributed generators, energy storage systems, and demand response is necessary. DERs can provide services such as reactive power compensation, voltage control, energy balancing, peak load reduction. These edge resources can also be used for system restoration after the extreme events within regulatory bound and enabling resiliency of the electric grid. While there are numbers of related work focused on solving these problems, availability of additional data resources has not been utilized to its potential to enable the reliable, sustainable and resilient electric grid. In order to address these challenges, data-driven algorithms have been proposed in this dissertation, including a) Volt/Var control, b) activity aware demand response and c) estimation of behind the meter distributed energy resources. Results indicate satisfactory performance of the developed algorithms.

ZHANG, YUE↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

AgMIP Training in Multiple Crop Models and Tools

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

farm crops↗
DOE OSTI2017

Design and impact assessment of watershed investments: An approach based on ecosystem services and boundary work

Watershed investments, whose main aim is to secure water for cities, represent a promising opportunity for large-scale sustainability transitions in the near future. If properly designed, they promote activities in the watershed that enhance ecosystem services while protecting nature and biodiversity, as well as achieving other societal goals. In this paper, we build on the concepts of ecosystem services and boundary work, to develop and test an operative approach for designing and assessing the impact of watershed investments. The approach is structured to facilitate negotiations among stakeholders. Its strategic component includes setting the agenda; defining investment scenarios; and assessing the performance of watershed investments as well as planning for a follow-up. Its technical component concerns data processing; tailoring spatially explicit ecosystem service models; hence their application to design a set of “investment portfolios”, generate future land use scenarios, and model impacts on selected ecosystem services. A case study illustrates how the technical component can be developed in a data scarce context in sub-Saharan Africa in a way that is functional to support the steps of the strategic component. The case study addresses soil erosion and water scarcity-related challenges affecting Asmara, a medium-sized city in Eritrea, and considers urban water security and rural poverty alleviation as two illustrative objectives, within a ten-year planning horizon. The case study results consist in spatially explicit data (investment portfolio, land use scenario, impact on ecosystem services), which were aggregated to quantitatively assess the performance of different watershed investments scenarios, in terms of changes in soil erosion control. By addressing stakeholders' concerns of credibility, saliency, and legitimacy, the approach is expected to facilitate negotiation of objectives, definition of scenarios, and assessment of alternative watershed investments, ultimately, to contribute to implementing an adaptive watershed management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

High-Resolution Gamma Spectrometry Data Set for High Burnup Pressurized Water Reactor Used Fuel

Over the past decade, considerable resources have been applied to investigate verification of used nuclear fuel assemblies for international safeguards. The opportunity to expand the available data used to validate these verification techniques was presented in 2016 by the High Burnup Spent Fuel Data Project, which made 25 high-burnup pressurized water reactor used fuel rods available for measurement in the Irradiated Fuels Examination Laboratory at Oak Ridge National Laboratory. With the support of the National Nuclear Security Administration Office of Nonproliferation and Arms Control, a 2017 campaign measured the 25 used fuel rods with a high-resolution gamma spectrometer. A planar high-purity germanium detector was used to provide high-efficiency across an energy range from 60 keV to 3 MeV. Gamma spectra of individual fuel rods were collected in 4.5 mm steps along the entire length of each rod. In addition, spectra were collected on select rods in rotational increments of 30 degrees to provide data on the distribution of fission products along the rod perimeter. The spectra are stored on a controlled-access website for use by others. This paper describes the measurement geometry, used fuel rod characteristics, example spectra, and preliminary analyses performed during the measurement campaign.

Smith, Susan↗
DOE OSTI2019

Renewable Energy Data, Analysis, and Decisions Viewed through a Case Study in Bangladesh: Preprint

Many developing countries around the world have signaled their intention to transition their energy sectors to rely on cleaner sources of electricity generation for a variety of reasons that may include complying with their Nationally Determined Contributions (in support of the Paris Climate Agreement), increasing energy security, or reducing air pollution. Renewable energy resources are increasingly a cost competitive option for new electricity generation; however, nations must consider renewable energy potential if they wish to increase these technologies in their electricity generation mix. Yet, goal setting, policymaking, grid modeling, and investment decisions that will enable renewable energy development all depend on the existence and quality of renewable energy resource data. This paper aims to summarize the relationship between renewable energy data, analysis, and decision making for developing countries seeking to transition their energy sector and to consider Bangladesh as a case study of a country that has worked with development organizations and the National Renewable Energy Laboratory to develop a national inventory of renewable energy resource data to enable critical decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

The SDSS-IV extended baryon oscillation spectroscopic survey: Luminous red galaxy target selection

Here, we describe the algorithm used to select the luminous red galaxy (LRG) sample for the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV) using photometric data from both the SDSS and the Wide-field Infrared Survey Explorer. LRG targets are required to meet a set of color selection criteria and have z-band and i-band MODEL magnitudes z < 19.95 and 19.9 < i < 21.8, respectively. Our algorithm selects roughly 50 LRG targets per square degree, the great majority of which lie in the redshift range 0.6 < z < 1.0 (median redshift 0.71). We also demonstrate that our methods are highly effective at eliminating stellar contamination and lower-redshift galaxies. We perform a number of tests using spectroscopic data from SDSS-III/BOSS ancillary programs to determine the redshift reliability of our target selection and its ability to meet the science requirements of eBOSS. The SDSS spectra are of high enough signal-to-noise ratio that at least ~89% of the target sample yields secure redshift measurements. Finally, we present tests of the uniformity and homogeneity of the sample, demonstrating that it should be clean enough for studies of the large-scale structure of the universe at higher redshifts than SDSS-III/BOSS LRGs reached.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

An agent-based model of the observed distribution of wealth in the United States

Pareto cautiously asserted that the wealth and income distributions which bear his name are universal, basing his argument on observations of this distribution in many different types of economies. In this paper, we present an agent based model (and a scalable approximation of it) in a closely related spirit. The central feature of this model is that wealth enables an individual to secure more wealth. Specifically, the important and novel feature of this model is its ability to simultaneously produce both the Pareto distribution observed in empirical data for the top 10% of the population and the exponential distribution observed for the lower 90%. We show that the model produces these distributions of wealth when initialized with an equitable distribution. Then, using historical data, we initialize the model with US wealth shares in 1988 and show that the model tracks wealth share changes from 1988 to 2012. Simulations to 2088 project that the top 0.01% of the population will possess more than 70% of the total wealth in the economy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A NEW ANALYSIS OF THE TWO CLASSICAL ZZ CETI WHITE DWARFS GD 165 AND ROSS 548. I. PHOTOMETRY AND SPECTROSCOPY

We present the first of a two-part seismic analysis of the two bright hot ZZ Ceti stars GD 165 and Ross 548. In this first part, we report the results of frequency extraction exercises based on time-series data sets of exceptional quality. We uncovered up to 13 independent pulsation modes in GD 165, regrouped into six main frequency multiplets. These include 9 secure (signal-to-noise ratio, S/N > 4) detections and 4 possible ones (4 ≥ S/N ≥ 3). Likewise, we isolated 11 independent modes in Ross 548 (9 secure and 2 possible detections), also regrouped into 6 multiplets. The multiplet structure is likely caused by rotational splitting. We also provide updated estimates of the time-averaged atmospheric properties of these two pulsators in the light of recent developments on the front of atmospheric modeling for DA white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Multi-Fidelity Gaussian Process for Distribution System Voltage Probabilistic Estimation with PVs

The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.

distribution system estimation↗
DOE OSTI2021

Cyber-Power Co-Simulation for End-to-End Synchrophasor Network Analysis and Applications

The resiliency, reliability and security of the next generation cyber-power smart grid depend upon efficiently leveraging advanced communication and computing technologies. Also, developing real-time data-driven applications is critical to enable wide-area monitoring and control of the cyber-power grid given high-resolution data from Phasor Measurement Units (PMUs). North American Synchrophasor Initiative Network (NASPlnet) provides guidance for PMU data exchanges. With the advancement in networking and grid operation, it is necessary to evaluate the performance of different data flow architectures suggested by NASPInet and analyze the impact on applications. Therefore, we need a cyber-power co-simulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents an end-to-end automated and user-driven cyber-power co-simulation using NS3 to model communication networks, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application utilizing PMU data in an IEEE 39 bus test system is presented using this cosimulation testbed.

Mustafa, Hussain M.↗
DOE OSTI2023

The Cost and Benefit of Enhancing Cybersecurity for Hybrid AC/DC Grids

As critical interfaces of AC grids and DC grids inside a hybrid AC/DC grid, the voltage-sourced-converter (VSC) has been demonstrated to be vulnerable to false data injection (FDI) cyber-attacks. As a result, the cyber-attack-induced AC grid frequency deviations and DC grid voltage deviations threaten the secure operation. Here, to enhance cybersecurity in a not only feasible but also cost-effective manner, this paper proposes a cost-benefit-based cyber-defense strategy for a hybrid AC/DC grid. First, this paper establishes a spatial-temporal dual cyber-attack evaluation model, in which the cyber-attack-induced frequency and voltage deviations are modelled in both a spatially and temporally dual manner. Then, the proposed cost-benefit-based cyber-defense strategy is modelled as a VSC commitment problem to achieve the trade-off between maximizing the cyber-defense benefits and minimizing the cyber-defense costs. The VSC commitment problem is then mathematically convexified into a mixed-integer second-order cone programming (MISOCP) problem, which could be efficiently solved in an event-triggered manner against unfolding cyber-attack events. Simulation results on a test hybrid AC/DC grid verified the feasibility and the cost-effectiveness of the proposed cyber-defense strategy.

cost-benefit analysis↗
DOE OSTI2017

Phase I Project: Fiber Optic Distributed Acoustic Sensing for Periodic Hydraulic Tests

The extraction of heat from hot rock requires circulation of fluid through fracture networks. Because the geometry and connectivity of these fractures determines the efficiency of fluid circulation, many tools are used to characterize fractures before and after development of the reservoir. Under this project, a new tool was developed that allows hydraulic connectivity between geothermal boreholes to be identified. Nanostrain in rock fractures is measured using fiber optic distributed acoustic sensing (DAS). This strain is measured in one borehole in response to periodic pressure pulses induced in another borehole. The strain in the fractures represents hydraulic connectivity between wells. DAS is typically used at frequencies of Hz to kHz, but strain at mHz frequencies were measured for this project. The tool was demonstrated in the laboratory and in the field. In the laboratory, strain in fiber optic cables was measured in response to compression due to oscillating fluid pressure. DAS recorded strains as small as 10 picometer/m in response to 1 cm of water level change. At a fractured crystalline rock field site, strain was measured in boreholes. Fiber-optic cable was mechanically coupled borehole walls using pressured flexible liners. In one borehole 30 m from the oscillating pumping source, pressure and strain were measured simultaneously. The DAS system measured fracture displacement at frequencies of less than 1 mHz (18 min periods) and amplitudes of less than 1 nm, in response to fluid pressure changes of less 20 Pa (2 mm of water). The attenuation and phase shift of the monitored strain signal is indicative of the permeability and storage (compliance) of the fracture network that connects the two wells. The strain response as a function of oscillation frequency is characteristic of the hydraulic structure of the formation. This is the first application of DAS to the measurement of low frequency strain in boreholes. It has enormous potential for monitoring geothermal reservoirs for purposes of understanding reservoir compliance and for assuring security of injection fluids. Periodic pressure pulses can be induced by oscillating injection or pumping during operation so the system could provide real time reservoir data. DAS cable may already be installed at a site as it is becoming increasingly used for seismic observation. Simulations conducted for this project indicate that strain should be propagated through borehole cements, so observations can be made outside of well casing. In uncased holes, the cable would need to be mechanically coupled to the borehole wall to provide measurements. One option would be to install fiber into cemented and abandoned boreholes to extend their utility.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

2015 Annual Site Environmental Report for Sandia National Laboratories New Mexico

Sandia National Laboratories, New Mexico, is a government-owned/contractor-operated facility. Sandia Corporation (Sandia), a wholly owned subsidiary of Lockheed Martin Corporation, manages and operates the Laboratories for the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA). The DOE/NNSA Sandia Field Office administers the contract and oversees contractor operations at the site. This Annual Site Environmental Report (ASER) summarizes data and the compliance status of Sandia's sustainability, environmental protection, and monitoring programs during calendar year 2015. Major environmental programs include air quality, water quality, groundwater protection, terrestrial surveillance, waste management, pollution prevention, environmental restoration, oil and chemical spill prevention, and implementation of the National Environmental Policy Act.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

BigData Express: Toward Predictable, Schedulable, and High-Performance Data Transfer [Slides]

BigData Express: a schedulable, predictable, and high-performance data transfer service. A peer-to-peer, scalable, and extensible data transfer model; A visually appealing, easy-to-use web portal; A high-performance data transfer engine; On-demand provisioning of end-to-end network paths with guaranteed QoS; Robust and flexible error handling; CILogon-based security (certificate version); A rich set of REST APIs to support scientific workflows

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗