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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Ceiling Fires Studied to Simulate Low-Gravity Fires

A unique new way to study low-gravity flames in normal gravity has been developed. To study flame structure and extinction characteristics in low-stretch environments, a normal gravity low-stretch diffusion flame was generated using a cylindrical PMMA sample of varying large radii, as shown in the photograph. These experiments have demonstrated that low-gravity flame characteristics can be generated in normal gravity through the proper use of scaling. On the basis of this work, it is feasible to apply this concept toward the development of an Earth-bound method of evaluating material flammability in various gravitational environments from normal gravity to microgravity, including the effects of partial gravity low-stretch rates such as those found on the Moon (1/6g) or Mars (1/3g). During these experiments, the surface regression rates for PMMA were measured for the first time over the full range of flammability in air, from blowoff at high stretch, to quenching at low stretch, as plotted in the graph. The solid line drawn through the central portion of the data (3<a<100 sec-1) has a slope of unity, which indicates regression is proportional to stretch. The figure coordinates assume that the values of stretch are equivalent, whether derived from forced stretch or from buoyant stretch. The excellent correlation of the regression-rate data over the two-order-of-magnitude variation of stretch shows the reasonableness of this assumption.

Olson, Sandra L.↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2026

Query Relaxation for LLM-Generated SPARQL Queries over Building Knowledge Graphs

When Knowledge Graph (KG) queries fail to match a pattern in a KG, they return no results. Identifying the statements causing these failures is tedious, especially for LLM-generated queries, which tend to be longer and more complex than queries written by hand. Query relaxation addresses this by systematically loosening query constraints until results are recovered. To evaluate the effectiveness of query relaxation against LLM generated queries, we propose a two-stage relaxation method combining triple deletion and path relaxation and test it against 1,823 failed queries for building KGs.

Paul, Lazlo↗
NASA NTRS1989

Solving sparse triangular linear systems on parallel computers

This paper describes and compares three parallel algorithms for solving sparse triangular systems of equations. These methods involve some preprocessing overhead and are primarily of interest in solving many systems with the same coefficient matrix. The first approach is to use a fixed blocksize and form the inverse of the diagonal blocks. The second approach is to use a variable blocksize and reorder the unknowns so that the diagonal blocks are diagonal matrices. The latter technique is called level scheduling because of how it is represented in the adjacency graph, and both row-wise and jagged diagonal storage for the off-diagonal blocks are considered. These techniques are analyzed for general parallel computers and experiments are presented for the eight-processor Alliant FX/8.

Anderson, Edward↗
NASA NTRS1990

Four experimental demonstrations of active vibration control for flexible structures

Laboratory experiments designed to test prototype active-vibration-control systems under development for future flexible space structures are described, summarizing previously reported results. The control-synthesis technique employed for all four experiments was the maximum-entropy optimal-projection (MEOP) method (Bernstein and Hyland, 1988). Consideration is given to: (1) a pendulum experiment on large-amplitude LF dynamics; (2) a plate experiment on broadband vibration suppression in a two-dimensional structure; (3) a multiple-hexagon experiment combining the factors studied in (1) and (2) to simulate the complexity of a large space structure; and (4) the NASA Marshall ACES experiment on a lightweight deployable 45-foot beam. Extensive diagrams, drawings, graphs, and photographs are included. The results are shown to validate the MEOP design approach, demonstrating that good performance is achievable using relatively simple low-order decentralized controllers.

Phillips, Doug↗
NASA NTRS1948

Theoretical motions of hydrofoil systems

Results are presented of an investigation that has been undertaken to develop theoretical methods of treating the motions of hydrofoil systems and to determine some of the important parameters. Variations of parameters include three distributions of area between the hydrofoils, two rates of change of downwash angle with angle of attack, three depths of immersion, two dihedral angles, two rates of change of lift with immersion, three longitudinal hydrofoil spacings, two radii of gyration in pitching, and various horizontal and vertical locations of the center of gravity. Graphs are presented to show locations of the center of gravity for stable motion, values of the stability roots, and motions following the sudden application of a vertical force or a pitching moment to the hydrofoil system for numerous sets of values of the parameters.

Imlay, Frederick H↗
NASA NTRS2026

High Temperature Dielectric Properties and Differential Scanning Calorimetry of Lunar Simulants

To guide development of microwave process technology that could be used during in situ construction on the Moon, we measured the high-temperature basic dielectric properties (εʹ and εʺ) of 17 lunar simulants and related materials. In order to confidently use these data one needs to understand the data’s strengths and weaknesses. Therefore, a goal of this publication is to provide insights into the comparative effects of sample composition, pre-treatments, experimental variables, high temperatures, and other factors on the measured response. The dielectric measurements were performed using the cavity perturbation method over a temperature range between room temperature to 1000 °C, or higher, and provided the real and imaginary components of permittivity at six frequencies. The utility of the original values was limited by the varying density of the pellets used in the measurement. Therefore, all of the εʹ and εʺ measurements at the frequency of 2466 MHz have been scaled to a constant density, 1.75 g/cm 3 . Here the data are presented as graphs chosen to aid analysis within and across simulant groups. To gain additional insight into the processes happening at the elevated temperatures in the dielectric measurements, heat capacity data was obtained using differential scanning calorimetry (DSC) on several of the simulant materials. Our data show that over the frequency range 397 MHz – 2985 MHz a material’s behavior does not greatly change, as compared to the scale of differences observed between lunar mare and highland simulants at high temperatures. For example at 1000 °C, the mare simulant JSC-1A absorbs 10 times more power than the highland simulant NUW-LHT-5M. We observe that as melting temperatures are reached both permittivity and dielectric loss rise non-linearly, helping to explain thermal runaway during microware heating. Our data show that even less than a few weight % of many non-lunar minerals, and the use of mixtures in simulants can affect the dielectric behavior at higher temperatures. A comparison of our results with published dielectric data for Apollo samples and with remote sensing of the Moon supports the conclusion the simulants and lunar material at room temperature have very similar dielectric values.

Differential Scanning Calorimetry↗
DOE OSTI2016

SU-G-TeP1-15: Toward a Novel GPU Accelerated Deterministic Solution to the Linear Boltzmann Transport Equation

Purpose: To develop a Graphic Processor Unit (GPU) accelerated deterministic solution to the Linear Boltzmann Transport Equation (LBTE) for accurate dose calculations in radiotherapy (RT). A deterministic solution yields the potential for major speed improvements due to the sparse matrix-vector and vector-vector multiplications and would thus be of benefit to RT. Methods: In order to leverage the massively parallel architecture of GPUs, the first order LBTE was reformulated as a second order self-adjoint equation using the Least Squares Finite Element Method (LSFEM). This produces a symmetric positive-definite matrix which is efficiently solved using a parallelized conjugate gradient (CG) solver. The LSFEM formalism is applied in space, discrete ordinates is applied in angle, and the Multigroup method is applied in energy. The final linear system of equations produced is tightly coupled in space and angle. Our code written in CUDA-C was benchmarked on an Nvidia GeForce TITAN-X GPU against an Intel i7-6700K CPU. A spatial mesh of 30,950 tetrahedral elements was used with an S4 angular approximation. Results: To avoid repeating a full computationally intensive finite element matrix assembly at each Multigroup energy, a novel mapping algorithm was developed which minimized the operations required at each energy. Additionally, a parallelized memory mapping for the kronecker product between the sparse spatial and angular matrices, including Dirichlet boundary conditions, was created. Atomicity is preserved by graph-coloring overlapping nodes into separate kernel launches. The one-time mapping calculations for matrix assembly, kronecker product, and boundary condition application took 452±1ms on GPU. Matrix assembly for 16 energy groups took 556±3s on CPU, and 358±2ms on GPU using the mappings developed. The CG solver took 93±1s on CPU, and 468±2ms on GPU. Conclusion: Three computationally intensive subroutines in deterministically solving the LBTE have been formulated on GPU, resulting in two orders of magnitude speedup. Funding support from Natural Sciences and Engineering Research Council and Alberta Innovates Health Solutions. Dr. Fallone is a co-founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi-planar linac MR for commercialization).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-277: Dose Calculation Comparisons Between Monaco, Pinnacle and Eclipse Treatment Planning Systems

Purpose: Monaco treatment planning system (TPS) version 5.0 uses a Monte-Carlo based dose calculation engine. The aim of this study is to verify and compare the Monaco based dose calculations with both Pinnacle{sup 3} collapsed cone convolution superposition (CCC) and Eclipse analytical anisotropic algorithm (AAA) calculations. Methods: For this study, previously treated SBRT lung, head and neck and abdomen patients were chosen to compare dose calculations between Pinnacle, Monaco and Eclipse. Plans were chosen from those that had been treated using the Elekta VersaHD or a NovalisTX linac. The plans included 3D conventional and IMRT beams using 6MV and 6MV Flattening filter free (FFF) photon beams. The original plans calculated with CCCS or AAA along with the recalculated ones using MC from the three TPS were exported into Velocity software for inter-comparison. Results: To compare the dose calculations, Mean Lung Dose (MLD), lung V5 and V20 values, and PTV Heterogeneity indexes (HI) and Conformity indexes (CI) were all calculated and recorded from the dose volume histograms (DVH). For each patient, the CI values were identical but there were differences in all other parameters. The HI was computed higher by 5 and 4% for calculated plans AAA and CCCS respectively, compared to the MC ones. The DVH graphs showed large differences between the CCCS and AAA and Monaco for 3D FFF, VMAT and IMRT plans. Better DVH agreement between was observed for 3D conventional plans. Conclusion: Better agreement was observed between CCCS and MC calculations than AAA and MC calculations. Those differences were more profound as the field size was decreasing and in the presence of inhomogeneities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Evaluating single-scale and/or non-planar diagrams by differential equations

We apply a recently suggested new strategy to solve differential equations for Feynman integrals. We develop this method further by analyzing asymptotic expansions of the integrals. We argue that this allows the systematic application of the differential equations to single-scale Feynman integrals. Moreover, the information about singular limits significantly simplifies finding boundary constants for the differential equations. To illustrate these points we consider two families of three-loop integrals. The first are form-factor integrals with two external legs on the light cone. We introduce one more scale by taking one more leg off-shell, $p_2^2\neq 0$. We analytically solve the differential equations for the master integrals in a Laurent expansion in dimensional regularization with $\epsilon=(4-D)/2$. Then we show how to obtain analytic results for the corresponding one-scale integrals in an algebraic way. An essential ingredient of our method is to match solutions of the differential equations in the limit of small $p_2^2$ to our results at $p_2^2\neq 0$ and to identify various terms in these solutions according to expansion by regions. The second family consists of four-point non-planar integrals with all four legs on the light cone. We evaluate, by differential equations, all the master integrals for the so-called $K_4$ graph consisting of four external vertices which are connected with each other by six lines. We show how the boundary constants can be fixed with the help of the knowledge of the singular limits. We present results in terms of harmonic polylogarithms for the corresponding seven master integrals with six propagators in a Laurent expansion in $\epsilon$ up to weight six.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Capturing Infrastructure Interdependencies for Power Outages Prediction During Extreme Events

As extreme weather events such as hurricanes, severe thunderstorms, and floods grow in frequency and intensity, the disruption of power grid systems poses significant challenges, including widespread electrical outages, economic losses, and threats to public safety. This paper presents a forward-looking approach that leverages geographical graph-based machine learning models to predict county-level maximum power outages during such events. By capturing the intricate interdependencies within power system networks, our approach aims to provide precise and actionable predictions that can optimize emergency response efforts and enhance grid resilience. Through the integration of real-world data, including hurricane advisories and power outage records, we have trained and benchmarked multiple machine learning models, demonstrating the feasibility and potential of this method. While our initial results are promising, this paper also charts a course for advancing these models, addressing the remaining challenges, and ultimately transforming how we anticipate and respond to the impacts of extreme weather on power systems.

Lee, Sangkeun (Matt) [ORNL] (ORCID:000000021317511↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
NASA NTRS2011

A Hyperbolic Ontology Visualization Tool for Model Application Programming Interface Documentation

Spacecraft modeling, a critically important portion in validating planned spacecraft activities, is currently carried out using a time consuming method of mission to mission model implementations and integration. A current project in early development, Integrated Spacecraft Analysis (ISCA), aims to remedy this hindrance by providing reusable architectures and reducing time spent integrating models with planning and sequencing tools. The principle objective of this internship was to develop a user interface for an experimental ontology-based structure visualization of navigation and attitude control system modeling software. To satisfy this, a number of tree and graph visualization tools were researched and a Java based hyperbolic graph viewer was selected for experimental adaptation. Early results show promise in the ability to organize and display large amounts of spacecraft model documentation efficiently and effectively through a web browser. This viewer serves as a conceptual implementation for future development but trials with both ISCA developers and end users should be performed to truly evaluate the effectiveness of continued development of such visualizations.

mission operations system (MOS)↗
DOE OSTI2023

Use of Graph Theory and Neural Networks for Microstructural Classification

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Modifications in segmentation also show potential in improving the accuracy of our current pyCHIP classifier. Replacing previous encoders trained on ImageNet with those trained on microscopy images like MicroNet has initially shown better performance at classifying images of irradiated samples. Additionally, different normalization approaches were tested to show no discernable effect on classification. The Louvain method for community detection is analyzed on a set of irradiated samples with different parameters to determine which proved beneficial under what circumstances. We suggest that microscopy experiments be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Space Station Freedom Disturbance Simulation and Management Tool

The design and functions of the Disturbance Simulation and Management Tool, (DMST), a software package developed to evaluate the effects of design changes and disturbance inputs on the Space Station dynamic environment, are described. DMST is a menu-driven interactive package, written in the PRO-MATLAB, which incorporates methods such as FEM model reduction, mode synthesis, and discrete-time numerical simulation. Particular attention is given to the main-menu options, including building a payload and payload-pointing-system (PPS) configuration, selecting disturbance inputs, running PPS and lab-module microgravity-environment (MGE) simulations, calculating limits to meet PPS and MGE requirements, and performing MGE spectral analyses using power-spectral density or shock spectra. Typical numerical results are presented in graphs.

Lisman, Sima S.↗
NASA NTRS2004

Field Effect Transistor Behavior in Electrospun Polyaniline/Polyethylene Oxide Demonstrated

Novel transistors and logic devices based on nanotechnology concepts are under intense development. The potential for ultra-low-power circuitry makes nanotechnology attractive for applications such as digital electronics and sensors. For NASA applications, nanotechnology offers tremendous opportunities for increased onboard data processing, and thus autonomous decisionmaking ability, and novel sensors that detect and respond to environmental stimuli with little oversight requirements. Polyaniline/polyethylene oxide (PANi/PEO) nanofibers are of interest because they have electrical conductivities that can be changed from insulating to metallic by varying the doping levels and conformations of the polymer chain. At the NASA Glenn Research Center, we have observed field effect transistor (FET) behavior in electrospun PANi/PEO nanofibers doped with camphorsulfonic acid. The nanofibers were deposited onto Au electrodes, which had been prepatterned onto oxidized silicon substrates. The preceding scanning electron image shows the device used in the transistor measurements. Saturation channel currents are observed at surprisingly low source/drain voltages (see the following graph). The hole mobility in the depletion regime is 1.4x10(exp -4)sq cm/V sec, whereas the one-dimensional charge density (at zero gate bias) is calculated to be approximately 1 hole per 50 two-ring repeat units of polyaniline, consistent with the rather high channel conductivity (approx.10(exp -3) S/cm). Reducing or eliminating the PEO content in the fiber is expected to enhance device parameters. Electrospinning is thus proposed as a simple method of fabricating one-dimensional polymer FET's.

Mueller, Carl H.↗
NASA NTRS1972

Application of selected methods of remote sensing for detecting carbonaceous water pollution

The use of aerial photography to determine the nature and extent of water pollution from carbonaceous materials is discussed. Flights were conducted over the Galveston Bay estuarine complex. Ground truth data were developed from field sampling of the waters in a region near the Houston Ship Channel. Tests conducted in the field were those for the following physical and chemical factors: (1) ph, (2) dissolved oxygen, (3) temperature, and (4) light penetration. Laboratory analyses to determine various properties of the water are described and the types of instruments used are identified. Results of the analyses are presented as charts and graphs.

Davis, E. M.↗
DOE OSTI2023

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm

Abstract Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

36 MATERIALS SCIENCE↗