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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-energy γ -ray detection of supernova remnants in the Large Magellanic Cloud

ABSTRACT We present the results of a cluster search in the γ-ray sky images of the Large Magellanic Cloud (LMC) region by means of the Minimum Spanning Tree (MST) and DBSCAN algorithms, at energies higher than 6 and 10 GeV, using 12 yr of Fermi-LAT data. Several significant clusters were found, the majority of which associated with previously known γ-ray sources. We confirm our previous detection of the supernova remnants N 49B and N 63A and found new significant clusters associated with the SNRs N 49, N 186D, and N 44. These sources are among the brightest X-ray remnants in the LMC and corresponds to core-collapse supernovae interacting with dense H ii regions, indicating that a hadronic origin of high-energy photons is the most likely process.

Campana, R. (ORCID:0000000247945453)↗
DOE OSTI2022

Free spectator nucleons in ultracentral relativistic heavy-ion collisions as a probe of neutron skin

Besides the yield ratio of free spectator neutrons produced in ultracentral 96 Zr + 96 Zr to 96 Ru + 96 Ru collisions, we propose that the yield ratio N n /N p of free spectator neutrons to protons in a single collision system at the BNL Relativistic Heavy Ion Collider or the CERN Large Hadron Collider can be a more sensitive probe of the neutron-skin thickness Δrnp and the slope parameter L of the symmetry energy. Here, the idea is demonstrated based on the proton and neutron density distributions of colliding nuclei obtained from Skyrme-Hartree-Fock-Bogolyubov calculations, and on a Glauber model that provides information of spectator matter. The final spectator particles are produced from direct emission, clusterization by a minimum spanning tree algorithm or a Wigner function approach, and deexcitation of heavy clusters by gemini. A larger Δr np associated with a larger L value increases the isospin asymmetry of spectator matter and thus leads to a larger N n /N p , especially in ultracentral collisions where the multiplicity of free nucleons are free from the uncertainties of cluster formation and deexcitation. We have further shown that the double ratio of N n /N p in isobaric collision systems or in collisions by isotopes helps to cancel the detecting efficiency for protons. Effects from nuclear deformation and electromagnetic excitation are studied, and they are found to be subdominant compared to the expected sensitivity to Δr np .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Simulation and Postmortem Analysis of Angeles Forest Disturbance Event

Multiple unexpected solar photovoltaic (PV) plant responses in the California region following contingencies in recent years warrant postmortem analysis leveraging digital fault recorder (DFR) data for a better understanding of such events and preventing similar events in the future. Most utilities in US, in general, possess transient stability (TS) phasor-domain models of the power grid. Since traditional phasor-based transient stability planning models cannot perform such analysis, this paper presents an approach to extract a region of such models and convert it to Electromagnetic transient (EMT) models. The approach is based on the determination of the minimum impedance-weighted spanning tree between the fault location and affected PV plants in a Western Electricity Coordinating Council (WECC) planning model followed by conversion of this region into an EMT model. The accuracy of the proposed approach is validated against DFR records obtained from the 2018 Angeles Forest disturbance event.

Samanta, Sayan↗
DOE OSTI2023

Optimization of Distribution Feeder Topology: A Differential Programming Learning Approach

This paper presents a gradient based method for optimizing distribution feeder network topology under load un- certainty. We recast the optimal network reconfiguration problem as a learning problem where edge weights of a graph are learned to produce an optimized spanning tree for a distribution network. Using recent methods published on differentiable programming, we provide a data driven method for learning these weights. We test our method on 100 variations of an IEEE 15-bus test system. Our results show that our method outperforms more traditional mathematical programming-based approaches.

differentiable programming↗
DOE OSTI2020

Geometry-Driven Detection, Tracking and Visual Analysis of Viscous and Gravitational Fingers

Viscous and gravitational flow instabilities cause a displacement front to break up into finger-like fluids. The detection and evolutionary analysis of these fingering instabilities are critical in multiple scientific disciplines such as fluid mechanics and hydrogeology. However, previous detection methods of the viscous and gravitational fingers are based on density thresholding, which provides limited geometric information of the fingers. The geometric structures of fingers and their evolution are important yet little studied in the literature. In this work, we explore the geometric detection and evolution of the fingers in detail to elucidate the dynamics of the instability. Additionally, we propose a ridge voxel detection method to guide the extraction of finger cores from three-dimensional (3D) scalar fields. After skeletonizing finger cores into skeletons, we design a spanning tree based approach to capture how fingers branch spatially from the finger skeletons. Finally, we devise a novel geometric-glyph augmented tracking graph to study how the fingers and their branches grow, merge, and split over time. Feedback from earth scientists demonstrates the usefulness of our approach to performing spatio-temporal geometric analyses of fingers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Graph-Env

The Graph-Env library provides a framework for adapting graph search problems into OpenAI Gym environments for reinforcement learning. In other words, Graph-Env enables reinforcement learning algorithms to be applied to graph search problems. Graph search problems include molecule and crystal structure design problems; route planning problems, including traveling salesperson problem; puzzles and games, such as chess and go; shortest path problems; minimum spanning tree; vehicle path generation; and others.

Tripp, Charles↗
DOE OSTI2025

Clustering at Massive Scale

ClaMS provides hierarchical clustering technology for use on massive, high-dimensional datasets that require distributed memory for processing. The algorithm employed is inspired by the popular HDBSCAN algorithm but makes use of computational kernels better suited for distributed computing. ClaMS is built on scalable nearest neighbor graph construction, metric forest completion, and approximate minimum spanning tree techniques.

Stanley, ThomasA [Lawrence Livermore National Labo↗
DOE OSTI2019

Porotomo: InSAR Data from San Emidio Geothermal Field, Nevada, 1992-2010

This submission contains tarred pair directories for interferometric synthetic aperture radar (InSAR) data covering San Emidio Geothermal Field in Nevada, USA as part of the porotomo project. Data included within this submission are the following: > ENVI_T120_GDR.tgz: Tarred directory containing 39 Envisat track 120 tarred, pair subdirectories spanning from 2003-Oct-29 to 2010-Jun-09. > ENVI_T27_GDR.tgz: Tarred directory containing 32 Envisat track 27 tarred, pair subdirectories spanning from 2004-Jun-23 to 2010-Apr-28. > ERS_T27_GDR.tgz: Tarred directory containing 35 ERS track 27 tarred, pair subdirectories spanning from 1992-Jun-07 to 2000-Sep-27. > ENVI_T27_SQR_drange_utm_cut.grd: SqueeSAR-derived data product for Enivsat track 27 from Eneva, et al. (2011) (provided by TRE Altamira) in terms of unwrapped range change rate. > MSTvsSQR_GDR.tgz: Tarred directory containing Easting gradient files for each satellite and track corresponding to the MST data products and the SqueeSAR data products. > sanem_dem_utm.grd: Digital elevation model (DEM) used in the creation of the interferograms. Explanation of pair subdirectories (contained within ENVI_T120_GDR, ENVI_T27_GDR, ERS_T27_GDR): Pairs are chosen based on a minimum spanning tree (MST) algorithm with image quality (as measured by the amount of phase noise within an interferometric pair) as the weighting criterion. Pairs are formed using the InSAR processing software GMT5SAR (Sandwell et al., 2011). Pair subdirectories are named by starting and ending epochs in YYYYMMDD_YYYYMMDD format. Each tarred pair directory contains GRD files for wrapped phase data (radians) and wrapped phase data after application of a modified Goldstein filter that depends on coherence (Goldstein & Werner, 1997; Baran et al., 2003; Sandwell et al., 2011). The data are given in both latitude/longitude and Universal Transverse Mercator (Zone 11N). Raw Synthetic Aperture Radar (SAR) data from the Envisat and ERS satellite missions operated by the European Space Agency (ESA) are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Graph 500 on OpenSHMEM: Using a Practical Survey of Past Work to Motivate Novel Algorithmic Developments

Graph500 [14] is an effort to offer a standardized benchmark across large-scale distributed platforms which captures the behavior of common communicationbound graph algorithms. Graph500 differs from other large-scale benchmarking efforts (such as HPL [6] or HPGMG [7]) primarily in the irregularity of its computation and data access patterns. The core computational kernel of Graph500 is a breadth-first search (BFS) implemented on an undirected graph. The output of Graph500 is a spanning tree of the input graph, usually represented by a predecessor mapping for every node in the graph. The Graph500 benchmark defines several pre-defined input sizes for implementers to test against. This report summarizes investigation into implementing the Graph500 benchmark on OpenSHMEM, and focuses on first building a strong and practical understanding of the strengths and limitations of past work before proposing and developing novel extensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Smart distribution systems

The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Furthermore, test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Spatio–Temporal Analysis of Deformation at San Emidio Geothermal Field, Nevada, USA Between 1992 and 2010

Although subsidence has been observed at the San Emidio geothermal field in Nevada using interferometric synthetic aperture radar since the early 1990s, the spatial extent and temporal evolution of the subsidence have not heretofore been quantified. Furthermore, the weather conditions and geographic location of San Emidio negatively affect interferometric image quality, causing low correlation amongst pairs. To address this, we introduce a new method for selecting pairs in areas of low correlation and small deformation signal using a minimum spanning tree method with a measure of image quality as the weighting criterion. We validate our pair selection approach by comparing our data products to SqueeSAR TM data products from a previous study at San Emidio. We also develop a deformation model which characterizes the spatial extent of subsidence at San Emidio in terms of volume change of the reservoir. After applying this deformation model to our data set of interferometric pairs, we examine the temporal relationship of the observed deformation with production and injection operations associated with geothermal power production.

Reinisch, Elena C. (ORCID:0000000252211921)↗
DOE OSTI2021

Graphical Optimization of Spectral Shift Reconstructions for Optical Backscatter Reflectometry

Optical backscatter reflectometry (OBR) is an interferometric technique that can be used to measure local changes in temperature and mechanical strain based on spectral analyses of backscattered light from a singlemode optical fiber. The technique uses Fourier analyses to resolve spectra resulting from reflections occurring over a discrete region along the fiber. These spectra are cross-correlated with reference spectra to calculate the relative spectral shifts between measurements. The maximum of the cross-correlated spectra—termed quality—is a metric that quantifies the degree of correlation between the two measurements. Recently, this quality metric was incorporated into an adaptive algorithm to (1) selectively vary the reference measurement until the quality exceeds a predefined threshold and (2) calculate incremental spectral shifts that can be summed to determine the spectral shift relative to the initial reference. Using a graphical (network) framework, this effort demonstrated the optimal reconstruction of distributed OBR measurements for all sensing locations using a maximum spanning tree (MST). By allowing the reference to vary as a function of both time and sensing location, the MST and other adaptive algorithms could resolve spectral shifts at some locations, even if others can no longer be resolved.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Understanding Formation of Young, Distributed Low-mass Stars and Clusters in the W4 Cloud Complex

It is well known that most of the stars form in rich clusters. However, recent Spitzer observations have shown that a significant number of stars also form in the distributed mode; their origin is not well understood. In this work, we aim to investigate clustered and distributed modes of star formation in the W4 complex. To do so, we identified and characterized the young stellar population associated with the region using homogeneous infrared data sets obtained from the Two Micron All Sky Survey, GLIMPSE, MIPS, and Wide-field Infrared Survey Explorer surveys. We make stellar surface density and minimum spanning tree maps to identify young clusters, and use Spitzer images to identify irradiated structures, such as elephant-trunk-like structures (ETLSs) and pillars in the region. The surface density distribution of the young stellar objects (YSOs) reveals three new clusterings and ~50% distributed protostars in the H ii region. The clusters are of low-mass nature but significantly younger than the central cluster IC 1805. We identified ~ 38 ETLSs in the region, a majority of which consist of one or a few stars at their tips. We find that these stars are low-mass (<2 M {sub ⊙}) YSOs, located at the outskirts (>17 pc) of the cluster IC 1805 and are part of the scattered distributed population. We argued that the star formation in the ETLSs of W4 is going on possibly due to the triggering effect of the expanding W4 bubble. Although high-resolution photometric and spectroscopic data would be required to confirm the scenario, nonetheless, we discuss the implications of this scenario for our understanding of distributed low-mass star formation in cloud complexes as opposed to other mechanisms such as turbulent fragmentation and dynamical ejection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Young Stellar Objects in the Massive Star-forming Regions W51 and W43

We present the results of our investigation of the star-forming complexes W51 and W43, two of the brightest in the first Galactic quadrant. In order to determine the young stellar object (YSO) populations in W51 and W43 we used color–magnitude relations based on Spitzer mid-infrared and 2MASS/UKIDSS near-infrared data. We identified 302 Class I YSOs and 1178 Class II/transition disk candidates in W51, and 917 Class I YSOs and 5187 Class II/transition disk candidates in W43. We also identified tens of groups of YSOs in both regions using the Minimal Spanning Tree (MST) method. We found similar cluster densities in both regions, even though Spitzer was not able to probe the densest part of W43. By using the Class II/I ratios, we traced the relative ages within the regions and, based on the morphology of the clusters, we argue that several sites of star formation are independent of one another in terms of their ages and physical conditions. We used spectral energy distribution-fitting to identify the massive YSO (MYSO) candidates since they play a vital role in the star formation process, and then examined them to see if they are related to any massive star formation tracers such as UCH ii regions, masers, or dense fragments. We identified 17 MYSO candidates in W51, and 14 in W43, respectively, and found that groups of YSOs hosting MYSO candidates are positionally associated with H ii regions in W51, though we do not see any MYSO candidates associated with previously identified massive dense fragments in W43.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Vision based obstacle detection and grouping for helicopter guidance

Electro-optical sensors can be used to compute range to objects in the flight path of a helicopter. The computation is based on the optical flow/motion at different points in the image. The motion algorithms provide a sparse set of ranges to discrete features in the image sequence as a function of azimuth and elevation. For obstacle avoidance guidance and display purposes, these discrete set of ranges, varying from a few hundreds to several thousands, need to be grouped into sets which correspond to objects in the real world. This paper presents a new method for object segmentation based on clustering the sparse range information provided by motion algorithms together with the spatial relation provided by the static image. The range values are initially grouped into clusters based on depth. Subsequently, the clusters are modified by using the K-means algorithm in the inertial horizontal plane and the minimum spanning tree algorithms in the image plane. The object grouping allows interpolation within a group and enables the creation of dense range maps. Researchers in robotics have used densely scanned sequence of laser range images to build three-dimensional representation of the outside world. Thus, modeling techniques developed for dense range images can be extended to sparse range images. The paper presents object segmentation results for a sequence of flight images.

Sridhar, Banavar↗
NASA NTRS1993

Using certification trails to achieve software fault tolerance

A conceptually novel and powerful technique to achieve fault tolerance in hardware and software systems is introduced. When used for software fault tolerance, this new technique uses time and software redundancy and can be outlined as follows. In the initial phase, a program is run to solve a problem and store the result. In addition, this program leaves behind a trail of data called a certification trail. In the second phase, another program is run which solves the original problem again. This program, however, has access to the certification trail left by the first program. Because of the availability of the certification trail, the second phase can be performed by a less complex program and can execute more quickly. In the final phase, the two results are accepted as correct; otherwise an error is indicated. An essential aspect of this approach is that the second program must always generate either an error indication or a correct output even when the certification trail it receives from the first program is incorrect. The certification trail approach to fault tolerance was formalized and it was illustrated by applying it to the fundamental problem of finding a minimum spanning tree. Cases in which the second phase can be run concorrectly with the first and act as a monitor are discussed. The certification trail approach was compared to other approaches to fault tolerance. Because of space limitations we have omitted examples of our technique applied to the Huffman tree, and convex hull problems. These can be found in the full version of this paper.

Sullivan, Gregory F.↗