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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

DISTRIBUTION TRANSFORMER ASSET MONITORING ON THE GRID EDGE USING SMART SENSOR DATA

As new loads such as rooftop photovoltaics, electric vehicles and other distributed energy resources become commonplace on the distribution grid, the stress on already aging assets begins to escalate. This increased loading and changing dynamics can exacerbate failure rates. While traditional monitoring efforts focus on transmission and generation assets, utilities are now beginning to pay close attention to distribution assets in order to increase reliability indices and reduce cost from unexpected outages. This research develops low-cost and scalable methods to monitoring the health of a critical distribution grid asset: the service transformer. Existing methods in literature are either invasive and thus difficult to implement or require the device to be tested offline in an expensive lab setting. Data from the ubiquitous smart meter as well as a novel Bluetooth based transformer monitor are leveraged to automatically notify the utility of deteriorating or damaged transformers. Voltage, temperature, and vibration are some of the signals measured and analyzed by the proposed algorithms to predict transformer failures. Furthermore, these algorithms are designed to keep the implementation and processing costs low by taking advantage of edge computing where possible.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1993

Update of GRASP/Ada reverse engineering tools for Ada

The GRASP/Ada project (Graphical Representations of Algorithms, Structures, and Processes for Ada) successfully created and prototyped a new algorithmic level graphical representation for Ada software, the Control Structure Diagram (CSD). The primary impetus for creation of the CSD was to improve the comprehension efficiency of Ada software and, as a result, improve reliability and reduce costs. The emphasis was on the automatic generation of the CSD from Ada PDL or source code to support reverse engineering and maintenance. The CSD has the potential to replace traditional pretty printed Ada source code. In Phase 1 of the GRASP/Ada project, the CSD graphical constructs were created and applied manually to several small Ada programs. A prototype CSD generator (Version 1) was designed and implemented using FLEX and BISON running under VMS on a VAX 11-780. In Phase 2, the prototype was improved and ported to the Sun 4 platform under UNIX. A user interface was designed and partially implemented using the HP widget toolkit and the X Windows System. In Phase 3, the user interface was extensively reworked using the Athena widget toolkit and X Windows. The prototype was applied successfully to numerous Ada programs ranging in size from several hundred to several thousand lines of source code. Following Phase 3,e two update phases were completed. Update'92 focused on the initial analysis of evaluation data collected from software engineering students at Auburn University and the addition of significant enhancements to the user interface. Update'93 (the current update) focused on the statistical analysis of the data collected in the previous update and preparation of Version 3.4 of the prototype for limited distribution to facilitate further evaluation. The current prototype provides the capability for the user to generate CSD's from Ada PDL or source code in a reverse engineering as well as forward engineering mode with a level of flexibility suitable for practical application. An overview of the GRASP/Ada project with an emphasis on the current update is provided.

Cross, James H., II↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗
DOE OSTI2020

Error-mitigated data-driven circuit learning on noisy quantum hardware

Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1997

Flood Management Enhancement Using Remotely Sensed Data

SENTAR, Inc., entered into a cooperative agreement with NASA Goddard Space Flight Center (GSFC) in December 1994. The intent of the NASA Cooperative Agreement was to stimulate broad public use, via the Internet, of the very large remote sensing databases maintained by NASA and other agencies, thus stimulating U.S. economic growth, improving the quality of life, and contributing to the implementation of a National Information Infrastructure. SENTAR headed a team of collaborating organizations in meeting the goals of this project. SENTAR's teammates were the NASA Marshall Space Flight Center (MSFC) Global Hydrology and Climate Center (GHCC), the U.S. Army Space and Strategic Defense Command (USASSDC), and the Alabama Emergency Management Agency (EMA). For this cooperative agreement, SENTAR and its teammates accessed remotely sensed data in the Distributed Active Archive Centers, and other available sources, for use in enhancing the present capabilities for flood disaster management by the Alabama EMA. The project developed a prototype software system for addressing prediction, warning, and damage assessment for floods, though it currently focuses on assessment. The objectives of the prototype system were to demonstrate the added value of remote sensing data for emergency management operations during floods and the ability of the Internet to provide the primary communications medium for the system. To help achieve these objectives, SENTAR developed an integrated interface for the emergency operations staff to simplify acquiring and manipulating source data and data products for use in generating new data products. The prototype system establishes a systems infrastructure designed to expand to include future flood-related data and models or to include other disasters with their associated remote sensing data requirements and distributed data sources. This report covers the specific work performed during the seventh, and final, milestone period of the project, which began on 1 October 1996 and ended on 31 January 1997. In addition, it provides a summary of the entire project.

Romanowski, Gregory J.↗
DOE OSTI2024

Continuous soil temperature measurements from 2019-10-4 to 2020-10-4, Teller road Mile 27, Seward Peninsula, Alaska

The dataset contains depth-resolved soil temperature measured at 45 discrete locations in a watershed located along the Nome-Teller road at Mile 27 in Seward Peninsula, Alaska. The dataset was generated to understand the local heterogeneity of soil thermal dynamics and their controls in a discontinuous permafrost region. At each location, temperatures were measured by a distributed temperature profiling probe designed based on Dafflon et al (2022). The dataset includes a description of the probe locations in the "Probe_locations.csv" file and the 45 data files (Soil_temperatures_*.csv). Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in NGA513_flmd.csv.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 OSTI2020

Particlization in fluid dynamical simulations of heavy-ion collisions: The IS3D module

The IS3D particlization module simulates the emission of hadrons from heavy-ion collisions via Monte-Carlo sampling of the Cooper–Frye formula which converts fluid dynamical information into local phase-space distributions for hadrons. The code package includes multiple choices for the non-equilibrium correction to these distribution functions: the 14-moment approximation, first-order Chapman–Enskog expansion, and two types of modified equilibrium distributions. This makes it possible to explore to what extent heavy-ion experimental data are sensitive to different choices for δf n , presently the main source of theoretical uncertainty in the particlization stage. Here, we validate our particle sampler with a high degree of precision by generating several million hadron emission events from a longitudinally boost-invariant hypersurface and comparing the event-averaged particle spectra and space–time distributions to the Cooper–Frye formula.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Impacts of Experimentally Obtained Harmonic Spectrums of Residential Appliances on Distribution Feeder: Preprint

This paper focuses on the harmonic load flow study for a set of residential loads and the impacts of the harmonic contents on the distribution feeder. Owing to the increased use of the power electronic based appliances and energy efficient home equipment like the lighting loads, the percentage of non-linear loads has increased in the distribution system which can impact the system performance, loss, and stability. Thus a comprehensive knowledge of their power quality and harmonic analysis is essential to improve the load models, voltage stability assessment, determining possible interactions at harmonic frequencies, protection planning, and the effect of the system impedance. For this study, the household appliances include the lighting, power electronics, resistive, and motor loads with a nominal supply voltage of 120 V single-phase or 240 V split-phase at a nominal frequency of 60 Hz. The harmonic spectrum as obtained from the experimental data is used to solve the harmonic load flow for a detailed secondary network including the distribution transformer extracted from a real distribution feeder's model. Additionally, the impact of the voltage harmonic on a three phase test load is presented through experimental results. The harmonic data of the transformer terminal voltage as obtained from the former load flow study is scaled to generate the source voltage for the three phase configuration of the grid simulator to make the test setup very similar to a real scenario.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1994

High Performance FORTRAN

High performance FORTRAN is a set of extensions for FORTRAN 90 designed to allow specification of data parallel algorithms. The programmer annotates the program with distribution directives to specify the desired layout of data. The underlying programming model provides a global name space and a single thread of control. Explicitly parallel constructs allow the expression of fairly controlled forms of parallelism in particular data parallelism. Thus the code is specified in a high level portable manner with no explicit tasking or communication statements. The goal is to allow architecture specific compilers to generate efficient code for a wide variety of architectures including SIMD, MIMD shared and distributed memory machines.

Mehrotra, Piyush↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

NASA Langley FUN3D Analyses in Support of the 1st AIAA Stability and Control Prediction Workshop

This work summarizes the results of FUN3D analyses conducted for the 1st AIAA Stability and Control Workshop on behalf of participants from the NASA Langley Research Center. The workshop was created to establish best practices for the prediction of stability and control derivatives using computational fluid dynamics and assess the limitations of these methods when those best practices are applied. The inaugural workshop considered the ONERA version of the NASA/Boeing Common Research Model, which includes the wing, body, horizontal tail, and a vertical tail designed by ONERA. Wind tunnel data at small sideslip angles remain unpublished and served as ‘blind’ data for computational comparisons. The present research generated workshop test case data using the NASA FUN3D code, which is a parallelized, unstructured, node-based, finite-volume discretization, Reynolds-averaged Navier-Stokes flow solver. Steady- state numerical simulations were conducted for workshop test cases investigating the following: grid convergence, Mach number effect on static stability, wind tunnel sting increments, static stability-derivative calculations, and a sideslip angle sweep. Results were generated for two series of unstructured, mixed-element grids, one set provided by the workshop and another set created using the HeldenMesh grid generation software. The results provided include total- and component-level breakdowns of the force and moment coefficients, in addition to sectional pressure distributions for the wing and tail components for comparisons to wind tunnel data.

CFD↗
DOE OSTI2019

Centrality and pseudorapidity dependence of the transverse energy density in p Pb collisions at s N N = 5.02 TeV

The almost hermetic coverage of the CMS detector is used to measure the distribution of transverse energy, E T , over 13.2 units of pseudorapidity, η, for p Pb collisions at a center-of-mass energy per nucleon pair of s NN = 5.02 TeV. The huge angular acceptance exploits the fact that the CASTOR calorimeter at -6.6 < η < -5.2 is effectively present on both sides of the colliding system because of a switch in the proton-going and lead-going beam directions. This wide acceptance enables the study of correlations between well-separated angular regions and makes the measurement a particularly powerful test of event generators. For minimum bias p Pb collisions the maximum value of dE T / d η is 22GeV, which implies an E T per participant nucleon pair comparable to that of peripheral PbPb collisions at s NN =2.76TeV. The increase of dE T / d η with centrality is much stronger for the lead-going side than for the proton-going side. The η dependence of dE T / d η is sensitive to the η range in which the centrality variable is defined. Several modern generators are compared to these results but none is able to capture all aspects of the η and centrality dependence of the data and the correlations observed between different η regions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Decoding the proton’s gluonic density with lattice QCD-informed machine learning

We present a first machine learning-based decoding of the gluonic structure of the proton from lattice QCD using a variational autoencoder inverse mapper (VAIM). Harnessing the power of generative AI, we predict the parton distribution function (PDF) of the gluon given information on the reduced pseudo-Ioffe-time distributions (RpITDs) as calculated from an ensemble with lattice spacing a ≈ 0.09 fm and a pion mass of M π ≈ 310 MeV. The resulting gluon PDF is consistent with phenomenological global fits within uncertainties, particularly in the intermediate-to-high-x region where lattice data are most constraining. A subsequent correlation analysis confirms that the VAIM learns a meaningful latent representation, highlighting the potential of generative AI to bridge lattice QCD and phenomenological extractions within a unified analysis framework.

Gluon parton distribution function↗
DOE OSTI2024

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Abstract Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau–Lifshitz-Gilbert equation solver the use of pMTJs driven by spin–orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called ‘coinflips’, via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based on p -value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a ‘bucket’ to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2011

Overview of the Jet/Surface Interaction Test (JSIT1)

This material was presented at the Acoustics Technical Working Group Meeting on April22, 2011. It provides an overview of an experiment called the Jet / Surface Interaction Test which was conducted to expand the database available regarding how a planar surface interacts with a jet to shield and/or enhance the jet noise. This presentation focuses on data obtained during Phase 1 of the test, JSIT1, which was conducted using the Small Hot Jet Acoustic Rig located in the Aeroacoustics Propulsion Lab at NASA GRCduring January and February, 2011. A second phase of the test, JSIT2, is planned for 2012.There were two parts of the phase 1 test. In part 1, known as the shielding surface part of the test, a planar surface was placed between the jet and the microphones. In part 2, the reflecting surface part of the test, the surface was placed on the opposite side of the jet so that the jet noise was free to reflect off the surface toward the microphones. Phased array, pressure sensitive paint, and far field acoustic data obtained during JSIT1 are presented. The phased array data illustrate how the jet noise is blocked by the shielding surface. It also shows that the low frequency scrubbing noise generated when the surface is impacted by the jet comes predominantly from the surface trailing edge. The far field data show the trailing edge noise to be a dipole source. The pressure sensitive paint data show how the pressure distribution on the surface varies as the surface is traversed toward jet.This material was presented at the Acoustics Technical Working Group Meeting on April22, 2011. It provides an overview of an experiment called the Jet/Surface Interaction Test which was conducted to expand the database available regarding how a planar surface interacts with a jet to shield and/or enhance the jet noise. This presentation focuses on data obtained during Phase 1 of the test, JSIT1, which was conducted using the Small Hot Jet Acoustic Rig located in the Aeroacoustics Propulsion Lab at NASA GRCduring January and February, 2011. A second phase of the test, JSIT2, is planned for 2012.There were two parts of the phase 1 test. In part 1, known as the shielding surface part of the test, a planar surface was placed between the jet and the microphones. In part 2, the reflecting surface part of the test, the surface was placed on the opposite side of the jet so that the jet noise was free to reflect off the surface toward the microphones. Phased array, pressure sensitive paint, and far field acoustic data obtained during JSIT1 are presented. The phased array data illustrate how the jet noise is blocked by the shielding surface. It also shows that the low frequency scrubbing noise generated when the surface is impacted by the jet comes predominantly from the surface trailing edge. The far field data show the trailing edge noise to be a dipole source. The pressure sensitive paint data show how the pressure distribution on the surface varies as the surface is traversed toward jet.

Podboy, Gary↗