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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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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Field Measurement and Analysis of Frequency and RoCoF for Low-Inertia Power Systems

The frequency measurement and rate of change of frequency (RoCoF) calculations are important for active power, frequency control, and frequency-related protection in power system operations. With the rapid decrease in inertia under modern power systems, however, multiple power variations introduced by variable renewable energy bring large RoCoF and introduce oscillations in frequency measurements. To shed light on this issue, this article proposes a frequency measurement and RoCoF analysis technology, as well as the sensitivity analysis for the real low-inertia power grid. First, a real-world event in an island power grid is analyzed as an example, and an event numerical model is estimated considering four uncertainties, including event RoCoF, oscillation magnitude, oscillation frequency, and underfrequency load-shedding threshold. Then, a sensitivity analysis is performed using the Sobol indices method and quasi-Monte Carlo simulations to identify the dominant uncertainties based on the event model. Here, the dominant uncertainties are compared to provide a guideline for the frequency estimation algorithm selection under different event cases. This algorithm is compared with an enhanced zero-crossing algorithm and gives a 3.41% better RoCoF error rate. Finally, an experiment is conducted using universal grid analyzers to validate the simulation results and the theoretical analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

PV Hosting Capacity Estimation: Experiences with Scalable Framework

Hosting capacity is an indication of the amount of solar photovoltaics (PV) that can be hosted in a distribution system without additional changes to infrastructure or oper-ations. This paper presents a framework for estimating the PV hosting capacity at scale. First, we analyze computational, modeling and other key challenges of performing relevant, large-scale simulations, provided along with the experiences and lessons learned. Then, we develop two open-source Python-based software tools to conduct repeatable distribution analyses: the Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations and the Job Automation and Deployment Engine (JADE) for parallelizing jobs on high-performance computing clusters. A case study of hosting capacity estimation for the SMART-DS San Francisco (SFO) 2000+ synthetic feeders, is used to demonstrate the capability of the developed DISCO+JADE framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, which can help them better plan for the overall upgrade costs to integrate more PV in the future. The experiences are shared to aid the tool users and researchers to conduct relevant studies and research.

distributed energy resources↗
DOE OSTI2016

In-Situ Gamma-Ray Assay of the 235-F Plutonium Fuel Form Facility at the Savannah River Site - 16293

The Plutonium Fuel Form (PuFF) Facility is located in Building 235-F near the geographic center of the Savannah River Site. Between 1978 and 1984, the facility was used to produce approximately 165 kilograms of iridium-encapsulated Pu-238 spheres and pellets for use as radioisotope thermal generators, primarily for the space program. The facility is divided between two cell lines, the east cell line used to process powdered Pu-238 oxide raw material into fuel forms and the west cell line used to encapsulate the fuel forms in iridium. Scoping in-situ gamma-ray assays were performed in the PuFF Facility in 2006 and estimated that hundreds of grams of Pu-238 holdup were within the facility. Using this holdup estimate as a source term, SRS has performed a risk analysis that indicated a seismic event that induces a full-facility fire in 235-F could lead to a 28,800 rem dose to a co-located worker. Based on this risk assessment, SRS is taking steps to decontaminate the facility. One of the first steps taken has been to improve upon the quality of the in-situ gamma-ray assay data. Carts and collimators were specially designed to survey the equipment in the PuFF Facility. The cell interiors were assayed along with the furnaces and storage coolers that protrude beneath the cells. While the previous scoping work consisted of 32 measurements, the current series of measurements included nearly 400 measurements using both high-purity germanium and lanthanum bromide detectors. Data analysis for the current set of measurements was conducted with greater rigor as well. MCNP5 was used to evaluate a variety of possible physical distributions for the Pu-238 source term and estimate cross-talk between neighboring cells. Data analysis was performed using three gamma-rays emitted by Pu-238 (99.85 keV, 152.7 keV, and 766.4 keV) providing three independent estimates of the mass of Pu-238 holdup in each of the cells. This also allowed the application of matrix correction factors to adjust gamma-ray attenuation. The weighted mean of these three results was used as the best estimate of Pu-238 holdup in the PuFF facility. This work lowered the estimate of holdup in the PuFF facility by approximately 100 grams compared to the previous estimate, a 0.55's difference. The uncertainty in the Pu-238 holdup was also reduced substantially relative to the 2006 scoping assay. Further radiological measurements are planned for the near future including in-situ gamma-ray assays of wing cabinets in the east cell line maintenance area, the transfer tube between the two cell lines, and some of the furnaces and storage coolers. A germanium gamma imager, the GeGI, will also be used to map out the distribution of the Pu-238 source term in the PuFF Facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Projection-Based Constrained Fusion Performance with Link Loss and Measurement Bias

Sensor networks have been deployed in many real-world applications for tracking and monitoring of dynamic targets over large geographical areas. We consider a scenario where a number of ground and/or airborne sensors are used to jointly track a ground target with certain constraints on its motion dynamics, such as those defined by roadways. The state estimates from these sensors are sent over a lossy communications network to a remote fusion center for fusion. We propose both centralized and distributed projection-based methods to incorporate known constraints into the estimation and fusion process. In addition to presenting simple closed-form projected fusion solutions, we also show how to utilize knowledge of the constraints into the fusion process by two distance-based weighting methods. Wealso explore the effect of link loss and sensor measurement bias on tracking accuracy performance of constrained fusion using illustrative tracking examples. Performance evaluation results demonstrate the potential of adopting weighted average fusers in improving the overall tracking performance in the presenceof link loss and measurement bias.

Liu, Qiang↗
DOE OSTI2018

Method and system for analyzing gas leak based on machine learning

Embodiments of the present invention provide a system for estimating a location of a gas leak, based on machine learning from forward gas concentration data provided by an analog or scale model including a gas source. The system improves significantly over previous systems by providing high quality, physically accurate forward modeling data inexpensively. During operation, the system configures an aerosol source at a first location to emit a gaseous aerosol. The system then configures a laser source to illuminate the aerosol with a laser sheet. The system may then obtain an image of a reflection of the laser sheet from the aerosol. The system may then analyze the image to quantify a three-dimensional concentration distribution of the aerosol. The system may then estimate, based on solving an inverse problem and an observed second gas concentration, a second location of a second gas source.

03 NATURAL GAS↗
DOE OSTI2021

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. Here, the framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Multilevel Monte Carlo Predictions of First Passage Times in Three-Dimensional Discrete Fracture Networks: A Graph-Based Approach

In this paper, we present a method combining multilevel Monte Carlo (MLMC) and a graph-based primary subnetwork identification algorithm to provide estimates of the mean and variance of the distribution of first passage times in fracture media at significantly lower computational cost than standard Monte Carlo (MC) methods. Simulations of solute transport are performed using a discrete fracture network (DFN), and instead of using various grid resolutions for levels in the MLMC, which is standard practice in MLMC, we identify a hierarchy of subnetworks in the DFN based on the shortest topological paths through the network using a graph-based method. While the mean of these ensembles is of critical importance, the variance is also essential in fractured media where uncertainty is an overarching theme, and understanding variability across an ensemble is a requirement for safety assessments. The method provides good estimates of the mean and variance at two orders of magnitude lower computational cost than MC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Insights into the year-round vertical distribution of chlorophyll concentration in high-latitude Arctic Ocean: implications for primary production

Climate-induced rapid changes in the Arctic Ocean, such as decreasing sea ice extent and increasing water temperature, are altering nutrient and light availability, profoundly impacting primary producer growth. However, access to the high-latitude Arctic Ocean is limited, and satellite data are primarily available only during summer, making continuous in-situ data collection challenging. We collected year-round chlorophyll-a (Chl-a) concentration data in high-latitude regions using a mooring system and performed a comparative analysis with reanalysis data. Unlike previous satellite-based studies, which typically rely on surface measurements, we used the annual vertical distribution of Chl-a. These data were applied to the vertically generalized production model to accurately estimate annual primary production. The moored Chl-a concentration data showed that phytoplankton exhibited a typical subsurface chlorophyll maximum (SCM) layer as sea ice retreated in June. Contrary to the gradually deepening SCM distribution predicted by model-based reanalysis data, the SCM layer persisted for approximately 4 months. This indicates that light and nutrient conditions within the SCM layer remained stable, sustaining continuous phytoplankton growth. Annual primary production, reflecting this vertical distribution of Chl-a concentration, was 6.85 gC m −2 yr −1 . This exceeded satellite-based estimates by at least two-fold, highlighting the significant underestimation of primary production by satellite approaches. Estimating primary production while accounting for the vertical distribution of phytoplankton and light is essential for improving ecological models to better understand carbon cycle and food web changes in the Arctic Ocean, with important implications for climate change predictions.

Arctic Ocean↗
DOE OSTI2018

Robust Localization of an Arbitrary Distribution of Radioactive Sources for Aerial Inspection - 18094

Radiation source detection has seen various applications in the past decade, ranging from the detection of dirty bombs in public places to scanning critical nuclear facilities for leakage or flaws, and in the autonomous inspection of nuclear sites. Despite the success in detecting single point sources, or a small number of spatially separated point sources, most of the existing algorithms fail to localize sources in complex scenarios with a large number of point sources or non-trivial distributions and bulk sources. Even in simpler environments, most existing algorithms are not scalable to larger regions and/or higher dimensional spaces. For effective autonomous inspection, we not only need to estimate the positions of the sources, but also the number, distribution and intensities of each of them. In this paper, we present a novel algorithm for the robust localization of an arbitrary distribution of radiation sources using multi-layer sequential Monte Carlo methods coupled with suitable clustering algorithms. We achieve near-perfect accuracy, in terms of F1-scores (> 0.95), while allowing the algorithm to scale, both to large regions in space, and to higher dimensional spaces (5 tested). (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Effects of Reactive Power on Photovoltaic Inverter Reliability and Lifetime

For predicting the reliability and lifetime of Photovoltaic (PV) inverters, thermal cycling is considered the most important stressors in the inverter system. To realize this, a detailed electrothermal model of the PV inverter was developed along with the controllers. The model was verified by developing an in-house inverter, which was placed inside a thermal chamber to verify the losses and temperature rise within the system at various temperatures. After the verification of the model, a reduced order model of the inverter was implemented which can translate the mission profile of ambient temperature and solar irradiance into load profile of junction temperatures of the switches. The estimated junction temperature data was used to identify the reliability indices and predict the useful lifetime of the inverter system. After developing the models to predict useful lifetime of the system, the impact of reactive power on the overall reliability of the system was studied. Experiments show that lifetime and reliability of the inverter decreases as the operating power factor decreases from unity. The results are presented and discussed here.

14 SOLAR ENERGY↗
DOE OSTI2025

Different methods of estimating riverbed sediment grain size diverge at the basin scale

Introduction: The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (D50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual D50 measurements are time-consuming and ignore larger grains, while approaches to model D50 based on catchment characteristics may over-generalize and miss site-scale heterogeneity. Machine learning-enabled object detection methods like You Only Look Once (YOLO) provides an alternative that enables estimation of D50 that is faster than manual measurements and more site-specific than predictions based on catchment characteristics. Methods: To understand the potential role of object detection methods for improving understanding of D50, we compared D50 estimates made manually, predicted from catchment characteristics, and using a YOLO-enabled approach across the Yakima River Basin. Results: We found distinct differences between methods for D50 averages and variability, and relationships between D50 estimates and basin characteristics. Discussion: We discuss the advantages and limitations of object detection methods versus current methods, and explore potential future directions to combine D50 methods to better estimate spatiotemporal variation of D50, and improve incorporation into basin-scale models.

grain size distribution↗
DOE OSTI2021

A bootstrapping approach to social media quantification

Abstract This work considers the use of classifiers in a downstream aggregation task estimating class proportions, such as estimating the percentage of reviews for a movie with positive sentiment. We derive the bias and variance of the class proportion estimator when taking classification error into account to determine how to best trade off different error types when tuning a classifier for these tasks. Additionally, we propose a method for constructing confidence intervals that correctly adjusts for classification error when estimating these statistics. We conduct experiments on four document classification tasks comparing our methods to prior approaches across classifier thresholds, sample sizes, and label distributions. Prior approaches have focused on providing the most accurate point estimate while this work focuses on the creation of correct confidence intervals that appropriately account for classifier error. Compared to the prior approaches, our methods provide lower error and more accurate confidence intervals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Calibrating Bayesian generative machine learning for Bayesiamplification

Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case. Herein, we also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements

42 ENGINEERING↗
DOE OSTI2016

A Monte Carlo exploration of threefold base geometries for 4d F-theory vacua

Here, we use Monte Carlo methods to explore the set of toric threefold bases that support elliptic Calabi-Yau fourfolds for F-theory compactifications to four dimensions, and study the distribution of geometrically non-Higgsable gauge groups, matter, and quiver structure. We estimate the number of distinct threefold bases in the connected set studied to be ~ 10 48 . Moreover, the distribution of bases peaks around h 1,1 ~ 82. All bases encountered after "thermalization" have some geometric non-Higgsable structure. We also find that the number of non-Higgsable gauge group factors grows roughly linearly in h 1,1 of the threefold base. Typical bases have ~ 6 isolated gauge factors as well as several larger connected clusters of gauge factors with jointly charged matter. Approximately 76% of the bases sampled contain connected two-factor gauge group products of the form SU(3) x SU(2), which may act as the non-Abelian part of the standard model gauge group. SU(3) x SU(2) is the third most common connected two-factor product group, following SU(2) x SU(2) and G2 x SU(2), which arise more frequently.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Estimation of process steady state with autoregressive models and Bayesian inference

To improve efficiency, separations engineers will typically design process circuits containing recirculating streams, which mix one or more of the process outputs with the feed material. Doing so can improve efficiency, but will cause a delay in the system reaching steady state conditions until the recirculating load mass flows stabilize. In testing separation circuits, engineers will often test a variety of factors and complete an analysis from sample results. Knowledge of if a process is at steady state, as well as the steady state conditions of a process, is essential for a valid techno-economic analysis. However, the definition of process steady state is often poorly defined, or does not include uncertainty quantification. If the performance of a process operating under two different sets of conditions are compared, an engineer who does not test for steady state or quantify steady state conditions risks producing a faulty analysis. In this work, a Bayesian statistical method for testing if all streams are at steady state is further motivated and then derived. Then after testing for steady state, the same model is used with a prior distribution that enforces a steady state assumption to estimate steady state conditions. Further, these methods were validated in a solvent extraction pilot plant where steady state conditions for all outflows were inferred with uncertainty quantification. Analysis is completed with functions available to the reader as part of the BayesMassBal (V 1.1.0) software package written in R.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

The Poisson tensor completion parametric estimator

We introduce the Poisson tensor completion (PTC) estimator that exploits inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram for samples of a multivariate distribution. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial non-homogeneous Poisson process. The Poisson tensor decomposition leads to a completion of the mean measure over all bins—including those containing few to no samples—and leads to our proposed estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values obviating the need for additional constraints to ensure non-negativity. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

97 MATHEMATICS AND COMPUTING↗