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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive Control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

data processing↗
NASA NTRS1981

Apparatus and method for determining the position of a radiant energy source

The position of a terrestrial RF source is determined from a geostationary, synchronous satellite by scanning the beam of a narrow beam width antenna in first and second orthogonal directions over a region including the source. The peak level of energy transduced by the antenna in each of the scanning directions is detected and correlated with the scanning position of the beam by feeding the output of a detector responsive to the transduced signal to an indicator of an X-Y recorder. The X and Y axes of the recorder are scanned in synchronism with the beam being respectively scanned in the first and second directions to form X and Y traces on which are indicated the detected peak position in each of the scanning directions. The source position is determined from an intersection of lines drawn parallel to the X and Y axes and including the detected peak position of each trace.

Schaefer, G. J.↗
DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2022

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Understanding carbon sourcing and transport originating from the helicon antenna surfaces during high-power helicon discharge in DIII-D Tokamak

The high-power helicon wave system in the DIII-D tokamak could potentially introduce new plasma–material interaction (PMI) challenges owing to rectified RF sheath potentials that develop near the antenna and surrounding plasma-facing components. We present the first application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to helicon-induced PMIs, extending previous STRIPE studies of ICRH antennas by incorporating net erosion, local re-deposition, and three-dimensional global impurity transport. The integrated workflow couples SOLPS, COMSOL, RustBCA, GITR, and GITRm to simulate carbon erosion, re-deposition, and impurity transport for two experimentally constrained DIII-D H-mode helicon operating scenarios with different antenna–plasma gaps, coupled RF powers, and edge plasma conditions. COMSOL predicts rectified RF sheath potentials of 1–5 kV localized near the lower portion of the antenna, where the magnetic field intersects the surface at grazing incidence. Carbon self-sputtering dominates the erosion source, whereas RF-accelerated D + ions contribute approximately 1% of the total gross erosion. The smaller-gap operating scenario exhibits substantially stronger gross erosion, enhanced local re-deposition (∼12%), and a larger confined carbon inventory owing to increased plasma accessibility and broader RF sheath coverage. Comparison with available DIII-D measurements shows no distinct change in the global carbon signal that correlates with the helicon RF pulse, consistent with the simulations indicating that the helicon-generated carbon source remains small compared with the existing background carbon inventory under the present graphite-wall operating conditions. These results demonstrate the capability of STRIPE to integrate RF sheath modeling, plasma transport, surface interaction physics, and three-dimensional impurity transport for the interpretation of helicon-induced PMIs. The study further identifies the principal sources of modeling uncertainty, including grazing-angle RF sheath physics, slow-wave resolution, plasma-background extrapolation, and trace-impurity transport assumptions, providing a framework for future validation and model development.

Kumar, Atul [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

Audible enclaves crafted by nonlinear self-bending ultrasonic beams

Delivering audible content to a targeted listener without disturbing others is paramount in audio engineering. However, achieving this goal has long been challenging due to the diffraction of low-frequency (long-wavelength) audio waves in linear acoustics. Here, we introduce an approach for creating remote audio spots, dubbed audible enclaves, by harnessing the local nonlinear interaction of two self-bending ultrasonic beams with distinct spectra. The self-bending ultrasonic beams created by acoustic metasurfaces, though inaudible, can bypass obstacles such as human heads. At their intersection behind obstacles, highly localized audible enclaves are formed due to the local nonlinear interactions. Additionally, we demonstrate the ultrabroadband capabilities of our metasurface-based implementation both numerically and experimentally, spanning from 125 Hz to 4 kHz (6 octave bands), covering the majority of the audible frequency range. The practicality of our proposed technique is underscored by its compact implementation size (0.16 m, equivalent to 0.06 wavelengths at 125 Hz), as well as its robust performance under wideband transient audio signal excitation and in a common room with reverberations. Our proposed audible enclaves hold significant potential for various applications in advanced audio engineering, including private speech communications, immersive spatial audio reproduction, and high-resolution sound/quiet zone control.

Science & Technology - Other Topics↗
NASA NTRS2016

Weak-Lensing Mass Calibration of the Atacama Cosmology Telescope Equatorial Sunyaev-Zeldovich Cluster Sample with the Canada-France-Hawaii Telescope Stripe 82 Survey

Mass calibration uncertainty is the largest systematic effect for using clustersof galaxies to constrain cosmological parameters. We present weak lensing mass measurements from the Canada-France-Hawaii Telescope Stripe 82 Survey for galaxy clusters selected through their high signal-to-noise thermal Sunyaev-Zeldovich (tSZ) signal measured with the Atacama Cosmology Telescope (ACT). For a sample of 9 ACT clusters with a tSZ signal-to-noise greater than five, the average weak lensing mass is (4.8 plus or minus 0.8) times 10 (sup 14) solar mass, consistent with the tSZ mass estimate of (4.7 plus or minus 1.0) times 10 (sup 14) solar mass, which assumes a universal pressure profile for the cluster gas. Our results are consistent with previous weak-lensing measurements of tSZ-detected clusters from the Planck satellite. When comparing our results, we estimate the Eddington bias correction for the sample intersection of Planck and weak-lensing clusters which was previously excluded.

Battaglia, N.↗
NASA NTRS1980

Heterodyne detection of the 752.033-GHz H2O rotational absorption line

A tunable high resolution two stage heterodyne radiometer was developed for the purpose of investigating the intensity and lineshape of the 752.033 GHz rotational transition of water vapor. Single-sideband system noise temperatures of approximately 45,000 K were obtained using a sensitive GaAs Schottky diode as the first stage mixer. First local oscillator power was supplied by a CO2 laser pumped formic acid laser (761.61 GHz), generating an X-band IF signal with theoretical line center at 9.5744 GHz. Second local oscillator power was provided by means of a 3 GHz waveguide cavity filter with only 9 dB insertion loss. In absorption measurements of the H2O taken from a laboratory simulation of a high altitude rocket plume, the center frequency of the 752 GHz line was determined to within 1 MHz of the reported value. A rotational temperature 75 K, a linewidth 5 MHz and a Doppler shift 3 MHz were measured with the line-of-sight intersecting the simulated-plume axis at a distance downstream of 30 nozzle diameters. These absorption data were obtained against continuum background radiation sources at temperatures of 1175 and 300 K.

Dionne, G. F.↗
NASA NTRS1982

Ice depolarization of the Comstar beacon at 28.56 GHz during low fades and correlation with radar backscatter

Radar correlation with significant ice depolarization events accompanied by low copolarization fades of the 28.56-GHz Comstar beacon signal are described for an experimental program at Wallops Island, VA. Using a Faraday switch at the front end of the receiver, the copolarization and cross-polarization levels of the 28.56-GHz beacon signal are sequentially monitored. A nearby high resolution S-band radar pointing along the earth-satellite path monitors the simultaneous ice and rain reflectivity. For a first case considered, excellent correlation is noted between the cross-polarization events and relatively large and extended ice reflectivities along a segment of the earth-satellite path at altitudes near and above the 0 C isotherm. In a second case, the radar and receiver data strongly suggest the cross-polarization mechanism is due to a hailshaft which intersects the path at altitudes well below the 0 deg isotherm.

Goldhirsh, J.↗
DOE OSTI2021

Numerical and Analytical Modeling of Flow Partitioning in Partially Saturated Fracture Networks

Infiltration processes in fractured-porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially-saturated conditions flows in fractures, percolating fracture networks, and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components and hence the bulk flow velocity and dispersion of fracture networks. In this work, we derive an approximate analytical solution for the partitioning process and validate it using smoothed particle hydrodynamics simulations. The developed transfer function is conceptually based on simulation results and laboratory experiments carried out in previous works. It allows to efficiently simulate flow through fracture networks with simple cubic structure and arbitrary number of fractures and aperture sizes via linear response theory and convolution of a given input signal. We derive a non-dimensional bulk flow velocity ($\widetilde{v}$) and dispersion coefficient ($\widetilde{D}$) to characterize fracture networks in terms of dimensionless horizontal and vertical time scales $\tau_m$ and $\tau_0$. The dispersion coefficient is shown to strongly depend on the horizontal time scale and converges towards a constant value of $0.08$ within reasonable fluid and geometrical parameter ranges, while the non-dimensional velocity exhibits a characteristic $\widetilde{v} \sim \tau_m^{-1/2}$ scaling. Given that hydraulic information is often only available at limited places within (fractured-porous) aquifer system, such as boreholes or springs, our study intends to provide a rudimentary analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, e.g., the dispersive properties of discharge (groundwater level fluctuations).

42 ENGINEERING↗
NASA NTRS1982

Method and apparatus for instantaneous band ratioing in a reflectance radiometer

A hand-held instrument is provided to compare information from selected infrared and visible bands in the 0.4 to 2.5 micrometer range, to perform ratioing via a dividing circuit (17) and to directly read out, via a display system (18), ratio values in a continuous digital display. The dual-beam, ratioing radiometer contains two optical trains (10, 12), each having two repeater lenses (L1a, L1b and L2a, L2b) and a cooled lead sulfide detector (D1, D2). One of the trains (10) is pivotal to facilitate measurements at distances ranging from about 1 meter to infinity. The optical trains are intersected by a set of two coaxially-mounted filter wheels (F1, F2), each containing up to five interference filters and slits to pass radiation filtered by the other. Filters with band passes as narrow as 0.01 micrometer are used in the region 0.4 to 2.5 micrometers. The total time for a calibration and measurement is only a few seconds. It is known from previous field studies using prior art devices, that materials, e.g., clay minerals, and carbonate minerals such as limestone, have unique spectral properties in the 2.0 to 2.5 micrometer region. Using properly chosen spectral filters, and ratioing the signals to remove the effect of topography on the brightness measured, the instrument can be used for real-time analysis of reflecting materials in the field. Other materials in the broader range of 0.4 to 2.5 micrometers (and even beyond) could be similarly identified once the reflectance spectrum of the material is established by any means.

Goetz, Alexander F. H.↗
DOE OSTI2023

Identification of ancestral gnathostome Gli3 enhancers with activity in mammals

Abnormal expression of the transcriptional regulator and hedgehog (Hh) signaling pathway effector Gli3 is known to trigger congenital disease, most frequently affecting the central nervous system (CNS) and the limbs. Accurate delineation of the genomic cis-regulatory landscape controlling Gli3 transcription during embryonic development is critical for the interpretation of noncoding variants associated with congenital defects. Here, we employed a comparative genomic analysis on fish species with a slow rate of molecular evolution to identify seven previously unknown conserved noncoding elements (CNEs) in Gli3 intronic intervals (CNE15–21). Transgenic assays in zebrafish revealed that most of these elements drive activities in Gli3 expressing tissues, predominantly the fins, CNS, and the heart. Intersection of these CNEs with human disease associated SNPs identified CNE15 as a putative mammalian craniofacial enhancer, with conserved activity in vertebrates and potentially affected by mutation associated with human craniofacial morphology. Finally, comparative functional dissection of an appendage-specific CNE conserved in slowly evolving fish (elephant shark), but not in teleost (CNE14/hs1586) indicates co-option of limb specificity from other tissues prior to the divergence of amniotes and lobe-finned fish. In conclusion, these results uncover a novel subset of intronic Gli3 enhancers that arose in the common ancestor of gnathostomes and whose sequence components were likely gradually modified in other species during the process of evolutionary diversification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Analysis and testing of a new method for drop size measurement using laser scatter interferometry

Research was conducted on a laser light scatter detection method for measuring the size and velocity of spherical particles. The method is based upon the measurement of the interference fringe pattern produced by spheres passing through the intersection of two laser beams. A theoretical analysis of the method was carried out using the geometrical optics theory. Experimental verification of the theory was obtained by using monodisperse droplet streams. Several optical configurations were tested to identify all of the parametric effects upon the size measurements. Both off-axis forward and backscatter light detection were utilized. Simulated spray environments and fuel spray nozzles were used in the evaluation of the method. The measurements of the monodisperse drops showed complete agreement with the theoretical predictions. The method was demonstrated to be independent of the beam intensity and extinction resulting from the surrounding drops. Signal processing concepts were considered and a method was selected for development.

Bachalo, W. D.↗
NASA NTRS2000

Kursk Magnetic Anomaly at Satellite Altitude: Revisited with the Orsted Satellite

The Kursk Magnetic Anomaly (KMA) of Russia (51 deg north, 37 deg east) has long been recognized as one of the largest magnetic anomalies on Earth. It is associated with the massive iron-ore formations of this region, however, model studies have revealed that the relationship between the two is not obvious. In an early effort to demonstrate the validity of Magsat data for crustal research a detailed study of the KMA, at an average altitude of 350 km and the surrounding region was made. They recorded a 27 nT high and a -9 nT low giving a 37 nT peak-to-trough anomaly over the immediate area of the KMA. Despite the much higher altitude of Orsted (620 to 850 km) we revisited the KMA to determine if this mission would also be able to record an associated anomalous crustal signature. The Orsted profiles we selected were from April to August 1999. From these data we chose those with an altitude range of 644 to 700 km and they were subsequently gridded, by least-squares collocation, to a mean elevation of 660 km. Both ascending and descending data were examined and signals common to both were extracted and averaged. A correlation coefficient between these two orbit orientations of 0.82 was computed. The quadrant-swapping method of Kim et al. was applied. Removal of the main geomagnetic field was accomplished with a polynomial fitting procedure. A positive anomaly of >2.5 nT with ari associated negative of <-0.5 nT for a >3 nT peak-to-trough range were computed. These Magsat and Orsted results are consistent with the decay of a dipole field over the studied altitude range. Significant differences between these two anomaly fields are due to the greater number of orbit profiles and therefore greater number of intersecting orbits (ascending and descending) available in the Orsted compilation. Of the four largest amplitude anomalies in the Orsted field three are present in the Magsat map. The fourth (>2.5 nT), however, is associated with the Belorussian-Lithuanian anteclise. This sugaests that additional geologic information may be apparent in the new Orsted field.

Taylor, Patrick T.↗
NASA NTRS2004

Faster Processing for Inverting GPS Occultation Data

A document outlines a computational method that can be incorporated into two prior methods used to invert Global Positioning System (GPS) occultation data [signal data acquired by a low-Earth-orbiting satellite as either this or the GPS satellite rises above or falls below the horizon] to obtain information on altitude-dependent properties of the atmosphere. The two prior inversion methods, known as back propagation and canonical transform, are computationally expensive because for each occultation, they involve numerical evaluation of a large number of diffraction-like spatial integrals. The present method involves an angular-spectrum-based phase-extrapolation approximation in which each data point is associated with a plane-wave component that propagates in a unique direction from the orbit of the receiving satellite to intersect a straight line tangent to the orbit at a nearby point. This approximation enables the use of fast Fourier transforms (FFTs), which apply only to data collected along a straight-line trajectory. The computation of the diffraction-like integrals in the angular-spectrum domain by use of FFTs takes only seconds, whereas previously, it took minutes.

Ao, Chi↗