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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A change language for ontologies and knowledge graphs

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of “apply patch” and “diff” commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders—e.g. ontology edits can be specified by commands like “add synonym ‘arm’ to ‘forelimb’” or “move ‘Parkinson disease’ under ‘neurodegenerative disease’.” We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Relocating microseismicity from downhole monitoring of the Decatur CCS site using a modified double-difference algorithm

SUMMARY The injection of CO2 at the Decatur carbon capture and storage site has generated significant microseismic activity, which occurs in distinct spatial clusters up to approximately 2.2 km from the primary injection well. Accurate and precise event locations are vital for the characterization of the microseismicity to help understand the reservoir response to the CO2 injection, whilst enabling the identification of minor faults and fractures below the resolution of conventional active seismic imaging. However, microseismic monitoring of fluid injection sites, such as Decatur, is often performed using a network of borehole sensors often from a single well. While these downhole sensors have excellent detection capabilities, their poor azimuthal coverage limits the ability to precisely determine event locations. We have developed a modified double-difference relocation algorithm suitable for both 1-D and 3-D velocity models, and which incorporates differential back azimuth observations to allow the benefits of the original double-difference algorithm to be applicable to a downhole microseismic monitoring setting. Applying the modified double-difference algorithm to the microseismicity at Decatur, we have successfully relocated 4293 events. The relocation included over 59 million observations for 757 285 event pairs, split across seven geographic regions. Despite the majority of observations being recorded in only two boreholes, with an almost identical azimuthal coverage, the results have shown to be reliable with significantly reduced residuals and low uncertainties associated with the final locations. We have analysed the residuals in terms of their association with each geographic region, data type, station and individual events, to fully appreciate their influence in the inversion and the fit of the data to the final set of event locations. For each region, the relocated seismicity has become less diffuse with improved clustering, and with newly visible linear features often orientated in a NE–SW direction. These results show the potential improvements that can be made to microseismic event locations recorded by a borehole network with a limited and variable azimuthal distribution.

58 GEOSCIENCES↗
DOE OSTI2023

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

New Perspectives on Spectroscopic Factor Quenching from Reactions

The evolution of single-particle strengths as the neutron-to-proton asymmetry changes informs us of the importance of short- and long-range correlations in nuclei and has therefore been extensively studied for the last two decades. Surprisingly, the strong asymmetry dependence of these strengths and their extreme values for highly asymmetric nuclei inferred from knockout reaction measurements on a target nucleus are not consistent with what is extracted from electron-induced, transfer, and quasi-free reaction data, constituting a two-decade old puzzle. This work presents the first consistent analysis of one-nucleon transfer and one-nucleon knockout data, in which theoretical uncertainties associated with the nucleon-nucleus effective interactions considered in the reaction models are quantified using a Bayesian analysis. Here, our results demonstrate that, taking into account these uncertainties, the spectroscopic strengths of loosely bound nucleons extracted from both probes agree with each other and, although there are still discrepancies for deeply bound nucleons, the slope of the asymmetry dependence of the single-particle strengths inferred from transfer and knockout reactions are consistent within 1 σ . Both probes are consistent with a small asymmetry dependence of these strengths. The uncertainties obtained in this work represent a lower bound and are already significantly larger than the original estimates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Dynamic Matrix Completion Based State Estimation in Distribution Grids

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this paper, we propose two dynamic sparsity-based state estimation approaches for distribution systems: (1) locally weighted matrix completion (LW-MC) and (2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Finally, results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

42 ENGINEERING↗
DOE OSTI2021

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, we, in this paper, propose a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Nonparallel transcriptional divergence during parallel adaptation

Abstract How underlying mechanisms bias evolution toward predictable outcomes remains an area of active debate. In this study, we leveraged phenotypic plasticity and parallel adaptation across independent lineages of Trinidadian guppies ( Poecilia reticulata ) to assess the predictability of gene expression evolution during parallel adaptation. Trinidadian guppies have repeatedly and independently adapted to high‐ and low‐predation environments in the wild. We combined this natural experiment with a laboratory breeding design to attribute transcriptional variation to the genetic influences of population of origin and developmental plasticity in response to rearing with or without predators. We observed substantial gene expression plasticity, as well as the evolution of expression plasticity itself, across populations. Genes exhibiting expression plasticity within populations were more likely to also differ in expression between populations, with the direction of population differences more likely to be opposite those of plasticity. While we found more overlap than expected by chance in genes differentially expressed between high‐ and low‐predation populations from distinct evolutionary lineages, the majority of differentially expressed genes were not shared between lineages. Our data suggest alternative transcriptional configurations associated with shared phenotypes, highlighting a role for transcriptional flexibility in the parallel phenotypic evolution of a species known for rapid adaptation.

Fischer, Eva K.↗
DOE OSTI2022

Continuous-Time Models for Longitudinal Data

This software is used to estimate a probabilistic continuous-time model from data. The models capture nonlinear associations between variables that change over time. They can be used to infer future values of variables. These tools were used in the manuscript entitled "Continuous-Time Probabilistic Models for Longitudinal Electronic Health Records."

Kaplan, AlanD↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Polarimetric Radar Variables in Snowfall at Ka- and W-Band Frequency Bands: A Comparative Analysis

Dual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (K DP ) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band Z DR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed Z DR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.

42 ENGINEERING↗
DOE OSTI2026

Data for Magnetic Anisotropy due to Localized Structural Defects in Strained Yttrium Iron Garnet Thin Films

This dataset contains electron microscopy, magnetic, and X-ray diffraction characterization data for YIG/YSGG thin films associated with the manuscript "Magnetic anisotropy due to localized structural defects in strained yttrium iron garnet thin films". Characterizations include: scanning electron nano diffraction (SEND, or 4D-STEM), dark field transmission electron microscopy (DF-TEM), energy dispersive X-ray spectroscopy (EDS), ferrimagnetic resonance (FMR), superconducting quantum interference device (SQUID), scanning transmission electron microscopy (STEM), X-ray diffraction (XRD).

Electron microscopy↗
DOE OSTI2021

Energy efficiency of blower heater non-purge compressed air dryers

This research focuses on twin tower regenerative closed loop desiccant dryers, specifically: blower heater non-purge (BHNP) with and without cooling water pumps, compressed-air heater purge (CHP), blower heater purge (BHP) and pressure swing heaterless (PSH). The research was conducted by collecting and analysing real time current draw data on air compressors and associated dryers at eight different facilities (13 air compressors) in terms of energy, power and cost. A decision tool was developed to depict the operational characteristics (power, energy and cost) of each type of dryer if used in conjunction with the selected compressor system. Finally, this research, on an equivalent normalised basis, compared and contrasted the different types of dryers in terms of performance and cost. Here, the research concluded that of the five types of desiccant dryer types observed the most energy efficient was the BHNP (with cooling water pump), subject to the operational conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Active Layer Depth and Permafrost Temperatures at the Teller 47 Field Site, Seward Peninsula, Alaska, 2022

With extreme climate warming in the Arctic, there is increasing focus on mapping permafrost stability and improving understanding of the impacts of thawing permafrost. Summer active layer depths and temperatures at the top of the permafrost were collected at the Teller 47 field site on the Seward Peninsula, Alaska to better understand permafrost thermal state in areas of discontinuous permafrost. Measurements were taken between August 14th and August 20th, 2022. Active layer depths were measured with a 120 cm thaw probe, and temperature measurements were taken with a SpotOn Temperature probe at the top of permafrost and at a maximum depth of XYZ, when no near-surface permafrost (within 120 cm of the surface) was present. Measurements were co-located with ground surface displacement measurements, which can be found in the associated dataset, NGA254. Precise location data were collected at each observation point using a GNSS RTK GPS unit. This dataset contains a *.csv file of permafrost temperature and active layer depths and a *.kml file of measurement locations.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Progress in Genetic Algorithm Fitting of X-Ray Fluorescence Data for Absorption Spectroscopy of Liquid Surfaces

X-ray fluorescence (XRF) spectroscopy of liquid surfaces is a promising approach to elemental analysis of forensic samples. Elements in the mixture can be identified from the XRF data because chemical elements are associated with unique spectra. This report describes progress in the development of automated identification of unknown mixtures of elements from XRF spectra using a genetic algorithm (GA). The XRF spectra are measured at the Advanced Photon Source (APS) at the liquid-liquid interface during solvent extraction process. The usage of the GA is demonstrated and future implementation for related problems are then discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Adaptive multi-level checkpointing

In some examples, with respect to adaptive multi-level checkpointing, a transfer parameter associated with transfer of checkpoint data from a node-local storage to a parallel file system may be ascertained for the checkpoint data stored in the node-local storage. The transfer parameter may be compared to a specified transfer parameter threshold. A determination may be made, based on the comparison of the transfer parameter to the specified transfer parameter threshold, as to whether to transfer the checkpoint data from the node-local storage to the parallel file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Per-instruction energy debugging using instruction sampling hardware

A processor utilizes instruction based sampling to generate sampling data sampled on a per instruction basis during execution of an instruction. The sampling data indicates what processor hardware was used due to the execution of the instruction. Software receives the sampling data and generates an estimate of energy used by the instruction based on the sampling data. The sampling data may include microarchitectural events and the energy estimate utilizes a base energy amount corresponding to the instruction executed along with energy amounts corresponding to the microarchitectural events in the sampling data. The sampling data may include switching events associated with hardware blocks that switched due to execution of the instruction and the energy estimate for the instruction is based on the switching events and capacitance estimates associated with the hardware blocks.

Wei, Shijia↗
DOE OSTI2023

Modeling Heat Pipes with Non-Condensable Gases

The report showcases advancements in modeling heat pipes, considering both scenarios with and without non-condensable gases. To achieve this, two distinct modeling approaches are juxtaposed. Firstly, the effective conduction model, implemented in the MOOSE-based code Sockeye, is employed. Secondly, a first-of-a-kind two-phase Euler-Euler Computational Fluid Dynamics (CFD) model is developed using the STAR-CCM+ code. Both models undergo validation against experimental data, acknowledging the inherent uncertainties associated with each modeling assumption. Interestingly, the non-tuned CFD model surpasses the performance of the calibrated conduction model for heat pipes operating under both conditions: with and without non-condensable gases. It's worth noting, however, that the CFD models entail significantly longer runtimes compared to the conduction models. Nevertheless, the insights garnered from the CFD model shed invaluable light on the intricate operational dynamics of heat pipes. Future work involves broadening the validation scope of these models and continuing their development to enhance their utility as robust tools for heat pipe design and operational support.

42 ENGINEERING↗
DOE OSTI2021

FedFleet 2021: Federal Automotive Statistical Tool - Federal Vehicle Fleet Data Collection

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2020, discusses challenges associated with quality of the submitted data, and touches on future aspects of fleet data collection and reporting. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗