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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 1,351 records · Page 75

All sources NASA NTRS DOE OSTI

DOE OSTI2024

3D Quantification of Pore Networks and Anthropogenic Carbon Mineralization in Stacked Basalt Reservoirs

Basalt formations are promising candidates for the geologic storage of anthropogenic CO 2 due to their storage capacity, porosity, permeability, and reactive geochemical trapping ability. The Wallula Basalt Carbon Storage Pilot Project demonstrated that supercritical CO 2 injected into >800 m deep Columbia River Basalt Group stacked reservoir flow tops mineralizes to ankerite-siderite-aragonite on month-year timescales, with 60% of the 977 metric tonnes of CO 2 converted within two years. The potential impacts of mineral precipitation and consequent changes on rock porosity, pore structure, pore size, and pore size distributions have likely been underestimated hitherto. Herein, we address these knowledge gaps using X-ray Micro Computed Tomography (XMT) to evaluate the pore network architecture of sidewall cores recovered two years after CO 2 injection. In the present study, we performed a detailed quantitative analysis of the CO 2 -reacted basalt cores by XMT imaging. Reconstructed 3D images were analyzed to study the distribution and volumetric details of porosity and carbonate nodules in the cores along with the various other phases, providing insight into paragenesis and carbonate growth mechanisms, including mineralogic/chemical zonation. Finally, these findings are being used to parametrize multiphase reactive transport models to predict the fate and transport of subsurface CO 2 , enabling scale-up to commercial-scale geologic carbon storage in basalts and other reactive mafic-ultramafic formations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Attention-Augmented Parametric Kernel Graph Neural Network (APKGNN) for Node Classification

We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relationships between connected nodes of a graph, the effect of attention on edge features mapped to relationships has not yet been analyzed through learning representation. This study derives such an attention vector by first calculating node features corresponding to endpoints of an edge and then aggregating these with extracted local intrinsic patches of a given graph to generate augmented local patch vectors. This process uses a parametric kernel based on Gaussian mixture models (GMMs) to embed local neighborhoods of the graph in local patches. The patch vectors then convolve with the above node features to produce an updated node representation. We show that this new learning representation (APKGNN) achieves higher node classification accuracy on tasks - both standard benchmarks (Cora, PubMed, Citeseer) and new experimental short text corpora where nodes correspond to text documents and words. This implementation of the GNN convolution layer outperforms state-of-the-art (SOTA) algorithms, achieving higher training, validation, and test accuracy by a significant margin on three standard benchmark data sets under both SOTA experimental settings and those for new testbeds.

Bose, Avishek↗

DOE OSTI2024

Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

The fifth-generation mobile communication (5G) technology offers the opportunities to enhance the grid real-time monitoring. The 5G-enabled phasor measurement units (PMUs) features flexible positioning and cost-effective long-term maintenance, without constraints of fixing wire. This paper is the first to demonstrate the applicability of 5G in PMU communication, and the experiment was carried out at Verizon non-standalone testbed at Pacific Northwest National Laboratory (PNNL) Advanced Wireless Communication lab. The performance of 5G-enabled PMU communication setup is reviewed and discussed in this paper, and the paper presents a real-time dynamic linear model (DML) based synchrophasor data anomaly detection application. Last but not least, the practicability of implementing 5G for wide-area protection strategies is explored and discussed by analyzing the experimental results.

5G, Synchrophasor data, machine learning, anomaly ↗

DOE OSTI2023

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis: Aggregation and Interpretation of Multi-Report Analysis Results

Industry-wide operating experience is a critical source of raw data for reliability and risk model parameter estimations for nuclear power plants. A large portion of operating experience data are failure events stored as reports that contain unstructured data, such as narratives. In current practice, a failure report is usually reviewed and manually coded by analysts. The coding is based on extracting several event characteristics such as system name, component type, sub-part type, failure mode, and failure cause. Event narratives are mostly used to help understand events and extract their characteristics. In this line of research, we aim to maximize the usage of event narratives by leveraging natural language processing (NLP) methods to automatically convert an event narrative to a causal graph. This research has promise to improve physical understanding of failure initiation and propagation and to facilitate use of non-failure data (e.g., near-misses and degradations) to complement the limited data pool of failures. In our previous work, we developed an NLP tool and applied it to analyze a number of licensee event reports submitted by U.S. nuclear power plants to the Nuclear Regulatory Commission. In this paper, we will report our recent research progress in aggregating the results of multiple reports, developing network model(s), and drawing statistical insights.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS1974

Lunar electrical conductivity, permeability,and temperature from Apollo magnetometer experiments

Magnetometers were deployed at four Apollo sites on the moon to measure remanent and induced lunar magnetic fields. Measurements from this network of instruments were used to calculate the electrical conductivity, temperature, magnetic permeability, and iron abundance of the lunar interior. Global lunar fields due to eddy currents, induced in the lunar interior by magnetic transients, were analyzed to calculate and electrical conductivity profile for the moon, and those profiles were used to calculate the lunar temperature for an assumed lunar material of olivine. Simultaneous measurements by magnetometers on the lunar surface and in orbit around the moon were use to construct a whole-moon hysteresis curve, from which the global lunar magnetic permeability is determined. Total iron abundance (sum of iron in the ferromagnetic and paramagnetic states) was calculated for two assumed compositional models of the lunar interior. Other lunar models with an iron core and with a shallow iron-rich layer also discussed in light of the measured global lunar permeability. Simultaneous magnetic field and solar plasma pressure measurements show that the remanent fields at the Apollo 12 and 16 sites interact with, and are compressed by, the solar wind. Velocities and thicknesses of the earth's magnetopause and bow shock were also estimated from simultaneous magnetometer measurements.

Dyal, P.↗

NASA NTRS1983

Auroral kilometric radiation/aurora correlation

Auroral kilometric radiation (AKR) observations from the ISIS 1 topside sounder receiver were compared with visual auroral observations from the network of Alaskan all-sky camera stations. The goal was to relate AKR source region encounters to specific auroral forms on the same magnetic field line. Thirty-eight simultaneous data sets were identified and analyzed. In general, intense AKR was associated with bright auroral arcs and conditions of weak or no AKR corresponded to times when either no aurora or only a faint arc or weak diffuse aurora were observed. Five cases, when both intense AKR and bright visual aurora were present, were analyzed in detail. Complete electron density N sub e contours, from the satellite altitude down to the F region ionization peak, were obtained along N-S traversals of the AKR source region. In addition, the ISIS 1 orbital tracks were projected down the magnetic field lines to the auroral altitude and compared to auroral features on a map derived from the all sky camera images. Density cavities (regions where N sub e 100/cu cm) were encountered on each of these passes.

Benson, R. F.↗

NASA NTRS1984

Auroral kilometric radiation/aurora correlation

Benson, R. F.↗

NASA NTRS1992

Brightness temperature and attenuation diversity statistics at 20.6 and 31.65 GHz for the Colorado Research Network

A limited network of four dual-channel microwave radiometers, with frequencies of 20.6 and 31.65 GHz, was operated in the front range of eastern Colorado from 1985 to 1988. Data, from November 1987 through October 1988 are analyzed to determine both single-station and joint-station brightness temperature and attenuation statistics. Only zenith observations were made. The spatial separations of the stations varied from 50 km to 190 km. Before the statistics were developed, the data were screened by rigorous quality control methods. One such method, that of 20.6 vs. 31.65 GHz scatter plots, is analyzed in detail, and comparisons are made of measured vs calculated data. At 20.6 and 31.65 GHz, vertical attenuations of 5 and 8 dB are exceeded 0.01 percent of the time. For these four stations and at the same 0.01 percent level, diversity gains from 6 to 8 dB are possible with the 50 to 190 km separations.

Westwater, Ed R.↗

NASA NTRS1998

The Ulysses Supplement to the BATSE 4B Catalog of Cosmic Gamma-Ray Bursts

We present Interplanetary Network Localization information for 150 gamma-ray bursts observed by the Burst and Transient Source Experiment(BATSE) between the end of the 3rd BATSE catalog and the end of the 4th BATSE catalog obtained by analyzing the arrival times of these bursts at the Ulysses and Compton Gamma Ray Observatory (CGRO) spacecraft. For any given burst observed by these two spacecraft, arrival time analysis (triangulation) results in an annulus of possible arrival directions whose width varies between 7 arcseconds and 2.3 degrees, depending on the intensity and time history of the burst, and the distance of the Ulysses spacecraft from Earth. This annulus generally intersects the BATSE error circle, resulting in an average reduction of the error box area by a factor of 25.

Hurley, K.↗

NASA NTRS1961

Study of Systems Using Inertia Wheels for Precise Attitude Control of a Satellite

Systems using inertia wheels are evaluated in this report to determine their suitability for precise attitude control of a satellite and to select superior system configurations. Various possible inertia wheel system configurations are first discussed in a general manner. Three of these systems which appear more promising than the others are analyzed in detail, using the Orbiting Astronomical Observatory as an example. The three systems differ from each other only by the method of damping, which is provided by either a rate gyro, an error-rate network, or a tachometer in series with a high-pass filter. An analytical investigation which consists of a generalized linear analysis, a nonlinear analysis using the switching-time method, and an analog computer study shows that all three systems are theoretically capable of producing adequate response and also of maintaining the required pointing accuracy for the Orbiting Astronomical Observatory of plus or minus 0.1 second of arc. Practical considerations and an experimental investigation show, however, that the system which uses an error-rate network to provide damping is superior to the other two systems. The system which uses a rate gyro is shown to be inferior because the threshold level causes a significant amount of limit-cycle operation, and the system which uses a tachometer with a filter is shown to be inferior because a device with the required dynamic range of operation does not appear to be available. The experimental laboratory apparatus used to investigate the dynamic performance of the systems is described, and experimental results are included to show that under laboratory conditions with relatively large extraneous disturbances, a dynamic tracking error of less than plus or minus 0.5 second of arc was obtained.

White, John S.↗

NASA NTRS2011

Reducing Traffic Congestions by Introducing CACC-Vehicles on a Multi-Lane Highway Using Agent-Based Approach

Traffic congestion is an ongoing problem of great interest to researchers from different areas in academia. With the emerging technology for inter-vehicle communication, vehicles have the ability to exchange information with predecessors by wireless communication. In this paper, we present an agent-based model of traffic congestion and examine the impact of having CACC (Cooperative Adaptive Cruise Control) embedded vehicle(s) on a highway system consisting of 4 traffic lanes without overtaking. In our model, CACC vehicles adapt their acceleration/deceleration according to vehicle-to-vehicle inter-communication. We analyze the average speed of the cars, the shockwaves, and the evolution of traffic congestion throughout the lifecycle of the model. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce the oscillatory behavior (stop and go) resulting from the acceleration/deceleration of the vehicles.

Arnaout, Georges M.↗

NASA NTRS2020

Simulating the Cascading Effects of an Extreme Agricultural Production Shock: Global Implications of a Contemporary US Dust Bowl Event

Higher temperatures expected by midcentury increase the risk of shocks to crop production, while the interconnected nature of the current global food system functions to spread the impact of localized production shocks throughout the world. In this study, we analyze the global potential impact of a present-day event of equivalent magnitude to the US Dust Bowl, modeling the ways in which a sudden decline in US wheat production could cascade through the global network of agricultural trade. We use observations of country-level production, reserves, and trade data in a Food Shock Cascade model to explore trade adjustments and country-level inventory changes in response to a major, multiyear production decline. We find that a 4-year decline in wheat production of the same proportional magnitude as occurred during the Dust Bowl greatly reduces both wheat supply and reserves in the United States and propagates through the global trade network. By year 4 of the event, US wheat exports fall from 90.5 trillion kcal before the drought to 48 trillion to 52 trillion kcal, and the United States exhausts 94% of its reserves. As a result of reduced US exports, other countries meet their needs by leveraging their own reserves, leading to a 31% decline in wheat reserves globally. These findings demonstrate that an extreme production decline would lead to substantial supply shortfalls in both the United States and in other countries, where impacts outside the United States strongly depend on a country's reserves and on its relative position in the global trade network.

Heslin, Alison↗

DOE OSTI2024

From IMPEL to Impact: Lessons Learned in Accelerating Innovative Building Technologies

The built environment is a complex ecosystem of social institutions and physical infrastructures. Innovation and entrepreneurship in the building industry are critical levers for market transformation toward equitable climate action. However, climate tech innovation for the built environment is not moving fast enough for global needs, and it lacks fundamental diversity, leading to inequitable outcomes. IMPEL (Incubating Market-propelled Entrepreneurial-mindset at the Labs and Beyond) - a U.S. Department of Energy incubator–addresses these critical issues. Over five years, IMPEL has enabled 250 innovators, including 55% women and diverse founders, to accelerate their buildings and clean energy technologies towards market and climate impact. IMPEL provides access to strategic mentoring and coaching, carbon tools training, testbeds, and powerful public-private pipelines, including industry demonstrations, non-dilutive grants, and venture capital networks. The IMPEL innovation ecosystem has accelerated the pace of innovation and market adoption of building decarbonization technologies. In this paper, leverage the IMPEL stakeholder ecosystem - from innovators to investors and product industry to policymakers - to analyze the critical barriers to decarbonization still encountered in the building industry. We study the IMPEL approach and highlight lessons learned that benefit young businesses pursuing innovative building and building-edge energy technologies to develop new ideas and products. Finally, we propose a ‘market forming’ framework to improve the quality and efficiency of the entrepreneurial ecosystem in the building industry. This framework could scale vetted technologies and the participation of diverse founders to de-risk the climate tech

Singh, Reshma↗

DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗

DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1998

Neutral Networks for Subpixel Classification of Multispectral Images

In this work, the implementation and use of AVHRR (Advanced Very High Resolution Radiometer) images for subpixel analysis is studied. The work consists of two parts, the first is making training data, the second is using the training data to design a neural net subpixel analyzer. Most work on subpixel analysis has been done with images with more spectral bands. AVHRR images were chosen because of their easy acquisition, and because the five spectral bands allow investigation into the development of training data. The first step in subpixel analysis is the development of training data. This consists of image to be classified, and the classification of each pixel. In order to do the classification, a high spatial resolution image is typically needed in order to manually create a classified image. It is difficult to have both the image of interest, and a high spatial resolution image of the same area taken at the same time. Thus it was studied whether a subsampled image taken from the image of interest could serve as the training data. Statistical work has been done showing the unusefulness of this approach. In the second part of the analysis, a feedforward neural net was trained and used to classify the AVHRR images. Results of these tests, comparing the neural net with typical statistical based schemes are shown.

Figueroa, Ricardo R.↗

DOE OSTI2024

Cybersecurity Assessment in DER-rich Distribution Operations: Criticality Levels and Impact Analysis

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control op- erations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. This paper utilizes real-time feeder models, which are instrumental in developing cybersecurity testbeds tailored for hardware-in-loop (HIL) systems. These models enable users to simulate cyber attacks in a real-world environment and analyze the power distribution operations during vulnerabilities. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.

Maharjan, Manisha↗

DOE OSTI2020

Sequentially coupled flow and geomechanical simulation with a discrete fracture model for analyzing fracturing fluid recovery and distribution in fractured ultra-low permeability gas reservoirs

More accurate characterization and prediction of the in-situ distribution of fracturing fluid in fractured reservoirs are needed for enhancing well productivity. In this study, an implicit-sequentially coupled flow/geomechanics simulator incorporating an efficient discrete fracture model is developed to model fluid distribution and recovery performance of ultra-low permeability gas reservoirs. The finite-volume and finite-element methods are used for space discretization of the flow and geomechanics equations, respectively, while the backward Euler method is employed for time discretization. The flow and geomechanics equations are solved sequentially based on fixed-stress splitting. An efficient discrete-fracture model is used to explicitly model the fractured system. Flexible unstructured gridding is employed to model arbitrarily-oriented fractures. The interrelations among pore volume, permeability and geomechanical conditions are considered dynamically using two-way coupled flow and geomechanics computations. The geometry of fracture (networks) due to hydraulic fracturing has significant impacts on the fracturing fluid recovery efficiency and ensuing fluid distribution. Under the same injection volume, the fracturing fluid recovery is higher when the fracture geometry is planar. Fluid recovery is relatively lower whenever natural fractures are activated during fracturing treatments; flowback time is also shortened when complex fracture network with enlarged fracture interface is present. Fracturing fluid in hydraulic fractures may leak off into the natural fractures and subsequently imbibes into the surrounding matrix due to capillarity effects. The fracturing fluid recovery and in-situ fluid distribution are sensitive to the shut-in duration and fracture closure behavior. This study analyzes the coupled flow-geomechanical responses of fractured gas reservoirs during the post-fracturing periods. Understanding the fate of the fracturing fluid can provide insights on, to some extent, the stimulated fracture volume, size of the water invasion zone, and efficiency of the fracturing design. The simulation predictions can also provide more accurate initial reservoir conditions (e.g. distributions of different phases and pressure) for long-term well performance estimation.

42 ENGINEERING↗