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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Evaluation of IGS Orbits with Satellite Laser Ranging

The accuracy with which orbits for the Global Positioning System (GPS) spacecraft, can be computed directly affects the accuracy of the resulting site coordinates and polar motion. Several groups routinely analyze GPS ground tracking data to compute precise orbits and terrestrial reference frame solutions. In this paper, we infer the accuracy of the orbits of two of the GPS satellites by comparing to independent laser ranges of subcentimeter accuracy obtained by a small but reasonably well distributed network of tracking sites. We find that all seven International GPS Service for Geodynamics (IGS) analysis centers achieve range residual root mean square (rms) errors at or below the 100 mm level. The best orbit solutions, from JPL, CODE, and the IGS combined product, yield a residual rms of about 50 mm. These residuals are consistent with three dimensional orbit errors of less than 150 mm. Estimating yaw rates for the spacecraft during shadow events, and using these estimates to compute the laser residual, significantly improves the fit. A small mean residual value of -15 to -30 mm seems to exist for most centers and laser sites which is not fully explained at present, but may be due to uncertainties in the corrections to the laser data, such as the reflector to spacecraft center of mass vector or small reference frame differences between the SLR sites and the GPS orbits.

Watkins, M. M.↗
NASA NTRS1998

Water Isotopes in Precipitation: Data/Model Comparison for Present-Day and Past Climates

Variations of HDO and H2O-18 concentrations are observed in precipitation both on a geographical and on a temporal basis. These variations, resulting from successive isotopic fractionation processes at each phase change of water during its atmospheric cycle, are well documented through the IAEA/WMO network and other sources. Isotope concentrations are, in middle and high latitudes, linearly related to the annual mean temperature at the precipitation site. Paleoclimatologists have used this relationship to infer paleotemperatures from isotope paleodata extractable from ice cores, deep groundwater and other such sources. For this application to be valid, however, the spatial relationship must also hold in time at a given location as the location undergoes a series of climatic changes. Progress in water isotope modeling aimed at examining and evaluating this assumption has been recently reviewed with a focus on polar regions and, more specifically, on Greenland. This article was largely based on the results obtained using the isotopic version of the NASA/GISS Atmospheric General Circulation Model (AGCM) fitted with isotope tracer diagnostics. We extend this review in comparing the results of two different isotopic AGCMs (NASA/GISS and ECHAM) and in examining, with a more global perspective, the validity of the above assumption, i.e. the equivalence of the spatial and temporal isotope-temperature relationship. We also examine recent progress made in modeling the relationship between the conditions prevailing in moisture source regions for precipitation and the deuterium-excess of that precipitation.

Jouzel, J.↗
DOE OSTI2024

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

42 ENGINEERING↗
DOE OSTI2023

Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective

Abstract Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute‐in‐memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute‐unit to memory, known as the von Neumann bottleneck. At its heart is a cross‐bar architecture with nodal non‐volatile‐memory elements that performs an analog multiply‐and‐accumulate operation, enabling the matrix‐vector‐multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system‐level characteristics and chip performance, including speed, power, and classification accuracy. With an over‐arching co‐design viewpoint, this review assesses the use of cross‐bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non‐volatile memory materials will need to demonstrate for a successful CIM technology.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phylogenomic analysis of the hemp family (Cannabaceae) reveals deep cyto‐nuclear discordance and provides new insights into generic relationships

Abstract Cannabaceae are a relatively small family of angiosperms, but they include several species of huge economic and cultural significance: marijuana or hemp ( Cannabis sativa ) and hops ( Humulus lupulus ). Previous phylogenetic studies have clarified the most deep relationships in Cannabaceae, but relationships remain ambiguous among several major lineages. Here, we sampled 82 species representing all genera of Cannabaceae and utilized a new dataset of 90 nuclear genes and 82 chloroplast loci from Hyb‐Seq to investigate the phylogenomics of Cannabaceae. Nuclear phylogenetic analyses revealed a robust and consistent backbone for Cannabaceae. We observed nuclear gene‐tree conflict at several deep nodes in inferred species trees, also cyto‐nuclear discordance concerning the relationship between Gironniera and Lozanella and the relationships among Trema s.l. (including Parasponia ), Cannabis + Humulus , and Chaetachme + Pteroceltis . Coalescent simulations and network analyses suggest that observed deep cyto‐nuclear discordances were most likely to stem from incomplete lineage sorting (ILS); nuclear gene‐tree conflict might be caused by both ILS and gene flow between species. All genera of Cannabaceae were recovered as monophyletic, except for Celtis , which consisted of two distinct clades: Celtis I (including most Celtis species) and Celtis II (including Celtis gomphophylla and Celtis schippii ). We suggest that Celtis II should be recognized as the independent genus Sparrea based on both molecular and morphological evidence. Our work provides the most comprehensive and reliable phylogeny to date for Cannabaceae, enabling further exploration of evolutionary patterns across this family and highlighting the necessity of comparing nuclear with chloroplast data to examine the evolutionary history of plant groups.

Plant Sciences↗
DOE OSTI2023

Digital Signal Processing Using Deep Neural Networks

Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network, to accomplish several important communications network and digital signals processing (DSP) tasks. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer, and correct transmission channel effects, and directly demodulate baseband RF signals.

42 ENGINEERING↗
DOE OSTI2020

Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Superthermal Electron Energy Interchange in the Ionosphere-Plasmasphere System

A self-consistent approach to superthermal electron (SE) transport along closed field lines in the inner magnetosphere is used to examine the concept of plasmaspheric transparency, magnetospheric trapping, and SE energy deposition to the thermal electrons. The dayside SE population is generated both by photoionization of the thermosphere and by secondary electron production from impact ionization when the photoelectrons collide with upper atmospheric neutral particles. It is shown that a self-consistent approach to this problem produces significant changes, in comparison with other approaches, in the SE energy exchange between the plasmasphere and the two magnetically conjugate ionospheres. In particular, plasmaspheric transparency can vary by a factor of two depending on the thermal plasma content along the field line and the illumination conditions of the two conjugate ionospheres. This variation in plasmaspheric transparency as a function of thermal plasma and ionospheric conditions increases with L-shell, as the field line gets longer and the equatorial pitch angle extent of the fly-through zone gets smaller. The inference drawn from these results is that such a self-consistent approach to SE transport and energy deposition should be included to ensure robustness in ionosphere-magnetosphere modeling networks.

Heliophysics↗
DOE OSTI2020

Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof of principle, here we demonstrate that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra of molecules to quantitative accuracy. Specifically, the predicted spectra reproduce nearly all prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Besides its own utility in spectral analysis and structure inference, our method can be combined with structure search algorithms to enable high-throughput spectrum sampling of the vast material configuration space, which opens up new pathways to material design and discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
DOE OSTI2021

Bayesian Optimal Sensor Augmentation Via Estimated Mutual Information

We consider the problem of designing a sensor network to most efficiently locate the source of a seismic event. The left panel of Figure 1 shows 982 possible sensor locations in a region of interest. The objective is to infer the unknown location of an earthquake, indicated by the gold dot for illustration purposes. Assume that three sensors have already been placed for data collection. Travel times of the P-wave from the source to the three sensors are observed and used to infer the unknown source location via a Bayesian statistical model described subsequently. The right panel of Figure 1 shows samples from the resulting posterior distribution for source location along with a posterior estimate of this location.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Conditional Generative Adversarial Networks for Solving Heat Transfer Problems

Generative Adversarial Networks (GANs) have been used as a deep learning approach to solving physics and engineering problems. Using deep learning for these problems is attractive in that reasonably accurate models can be inferred from only raw data, eliminating the need to define the exact physical equations governing a problem. We expand on previous work using GANs to generate steady-state solutions to the two-dimensional heat equation. Using a basic conditional GAN (cGAN), we generate accurate solutions for rectangular domains conditioned on four edge boundary conditions (MAE < 0.5%). For finding steady-state solutions over arbitrary two-dimensional domains (not constrained to rectangles), we use a cGAN designed for image-to-image translation. We train this GAN on various types of geometric domains (circles, squares, triangles, shapes with one circular or rectangular hole), achieving accurate results on test data made up of geometries similar to those in training (MAE < 1%). For both of these GANs, we experiment with different loss function terms, showing that a term using the gradients of solution images significantly improves the basic cGAN but not the image-to-image GAN. Lastly, we show that the image-to-image GAN performs poorly when applied to two-dimensional geometries that vary in structure from training data (MAE < 8% for shapes with multiple holes or different shaped holes). This demonstrates the cGAN's lack of generalizability. While the cGAN is an accurate and computationally efficient method when trained and tested on similarly structured data, it is a much less reliable method when applied to data that is slightly different in structure from the training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

A tomographic glimpse of the upper mantle source of magmas of the Jemez lineament, New Mexico

In this study, the lateral variations of the P wave velocity as a function of depth were examined for the regions of the Rio Grande rift and the Jemez lineament, to infer spatial distributions of partial melt in the upper mantle source zones for the rift and the lineament. The method involved measurements of teleismic P wave delays at a 22-station network followed by performing a damped least-squares three-dimensional inversion for these lateral variations. Results indicate that, directly beneath the Jemez lineament (but not beneath the Rio Grande rift), there is a 100-km-wide 1-2-percent low-P-wave-velocity feature in the depth range of 50-160 km. This implies that the volcanic potential of the Jemez lineaments continues to greatly exceed that of the Rio Grande rift.

Spence, William↗
NASA NTRS1992

Chemical reaction path modeling of hydrothermal processes on Mars: Preliminary results

Hydrothermal processes are thought to have had significant roles in the development of surficial mineralogies and morphological features on Mars. For example, a significant proportion of the Martian soil could consist of the erosional products of hydrothermally altered impact melt sheets. In this model, impact-driven, vapor-dominated hydrothermal systems hydrothermally altered the surrounding rocks and transported volatiles such as S and Cl to the surface. Further support for impact-driven hydrothermal alteration on Mars was provided by studies of the Ries crater, Germany, where suevite deposits were extensively altered to montmorillonite clays by inferred low-temperature (100-130 C) hydrothermal fluids. It was also suggested that surface outflow from both impact-driven and volcano-driven hydrothermal systems could generate the valley networks, thereby eliminating the need for an early warm wet climate. We use computer-driven chemical reaction path calculation to model chemical processes which were likely associated with postulated Martian hydrothermal systems.

Plumlee, Geoffrey S.↗
NASA NTRS2004

Objective Lightning Forecasting at Kennedy Space Center/Cape Canaveral Air Force Station using Cloud-to-Ground Lightning Surveillance System Data

The 45th Weather Squadron (45 WS) forecasters at Cape Canaveral Air Force Station (CCAFS) in Florida include a probability of thunderstorm occurrence in their daily morning briefings. This information is used by personnel involved in determining the possibility of violating Launch Commit Criteria, evaluating Flight Rules for the Space Shuttle, and daily planning for ground operation activities on Kennedy Space Center (KSC)/CCAFS. Much of the current lightning probability forecast is based on a subjective analysis of model and observational data. The forecasters requested that a lightning probability forecast tool based on statistical analysis of historical warm-season (May - September) data be developed in order to increase the objectivity of the daily thunderstorm probability forecast. The tool is a set of statistical lightning forecast equations that provide a lightning occurrence probability for the day by 1100 UTC (0700 EDT) during the warm season. This study used 15 years (1989-2003) of warm season data to develop the objective forecast equations. The local CCAFS 1000 UTC sounding was used to calculate stability parameters for equation predictors. The Cloud-to-Ground Lightning Surveillance System (CGLSS) data were used to determine lightning occurrence for each day. The CGLSS data have been found to be more reliable indicators of lightning in the area than surface observations through local informal analyses. This work was based on the results from two earlier research projects. Everitt (1999) used surface observations and rawinsonde data to develop logistic regression equations that forecast the daily thunderstorm probability at CCAFS. The Everitt (1999) equations showed an improvement in skill over the Neumann-Pfeffer thunderstorm index (Neumann 1971), which uses multiple linear regression, and also persistence and climatology forecasts. Lericos et al. (2002) developed lightning distributions over the Florida peninsula based on specific flow regimes. The flow regimes were inferred from the average wind direction in the 1000-700 mb layer at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), Florida, and the lightning data were from the National Lightning Detection Network. The results suggested that the daily flow regime may be an important predictor of lightning occurrence on KSC/CCAFS.

Lambert, Winifred↗
NASA NTRS2013

Polarimetric and Multi-Doppler Radar Observations of Electrified and Unelectrified Wildfire Smoke Plumes

Pyrocumulus clouds above three Colorado wildfires (Hewlett Gulch, High Park, and Waldo Canyon; all occurred during summer 2012) electrified and produced small intracloud discharges whenever the smoke plumes grew to high altitudes (over 10 km above mean sea level, or MSL). This occurred during periods of rapid wildfire growth, as indicated by the shortwave infrared channel on a geostationary satellite, as well as by incident reports. In the Hewlett Gulch case, the fire growth led to increased updrafts within the plume, as inferred by multiple‐ Doppler radar syntheses, which led to the vertical development and subsequent electrification ‐ a life cycle as short as 30 minutes. The lightning, detected by a threedimensional lightning mapping network, was favored in high‐altitude regions (~10 km MSL) containing modest reflectivities (25 dBZ and lower), ~0 dB differential reflectivity, and reduced correlation coefficient (~0.6‐0.7). This indicated the likely presence of ice particles (crystals and aggregates, possibly rimed) mixed with ash. Though neither multiple‐Doppler nor polarimetric observations were available during the electrification of the High Park and Waldo Canyon plumes, their NEXRAD observations showed reflectivity structures consistent with Hewlett Gulch. In addition, polarimetric and multiple‐Doppler scanning of unelectrified High Park plumes indicated only irregularly shaped ash, and not ice, was present (i.e., reflectivities < 25 dBZ, differential reflectivity > 5 dB, correlation < 0.4), and there was no broaching of the 10 km altitude. Based on these results, the electrification likely was caused by ice‐based processes that did not involve significant amounts of graupel. The results demonstrate the scientific value of multiple‐Doppler and polarimetric radar observations of wildfire smoke plumes ‐ including the ability to distinguish between regions of pure hydrometeors, regions of pure ash, and mixtures of both ‐ and also suggest a possible new application for lightning data in monitoring wildfires.

Lang, Timothy J.↗
DOE OSTI2024

Designing observables for measurements with deep learning

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design targeted observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest and can be well-measured by construction. The networks are trained using a custom loss function that rewards outputs that are sensitive to the parameter(s) of interest while simultaneously penalizing outputs that are different between particle-level and detector-level (to minimize detector distortions). We demonstrate this idea in simulation using two physics models for inclusive measurements in deep inelastic scattering. We find that the new approach is more sensitive than classical observables at distinguishing the two models and also has a reduced unfolding uncertainty due to the reduced detector distortions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗