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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Detection of Nonthermal Hard X-Ray Emission from the “Fermi Bubble” in an External Galaxy

We report new Chandra hard X-ray (>2 keV) and Jansky Very Large Array C-band observations of the nuclear super bubble of NGC 3079, an analog of the “Fermi bubble” in our Milky Way. We detect extended hard X-ray emission on the southwest (SW)side of the galactic nucleus with coherent multiwavelength features in radio, Hα, and soft X-ray. The hard X-ray feature has a cone shape with possibly a weak cap, forming a bubble-like structure with a diameter of∼1.1 kpc. A similar extended feature, however, is not detected on the northeast (NE)side, which is brighter in all other wavelengths such as radio, Hα, and soft X-ray. Scattered photons from the nuclear region or other nearby point-like X-ray bright sources, inverse Compton emission from cosmic-ray(CR)electrons via interaction with the cosmic microwave background, or any individually faint stellar X-ray source populations, cannot explain the extended hard X-ray emission on the SW side and the strongly NE/SW asymmetry. A synchrotron emission model, plus a thermal component accounting for the excess at∼1 keV, can well characterize the broadband radio/hard X-ray spectra. The broadband synchrotron spectra do not show any significant cutoff, and even possibly slightly flatten at higher energy. This rule out a loss-limited scenario in the acceleration of the CR electrons in or around this superbubble. As the first detection of kiloparsec-scale extended hard X-ray emission associated with a galactic nuclear superbubble, the spatial and spectral properties of the multiwavelength emissions indicate that the CR leptons responsible for the broadband synchrotron emission from the SW bubble must be accelerated in situ, instead of transported from the nuclear region of the galaxy.

Jiang-Tao Li↗
NASA NTRS1996

Mid-Infrared Spectra of Comets P/Borrelly, P/Faye, and P/Schaumasse

A 10 micron silicate emission feature has been discovered in the spectra of comets P/BorrelIy and P/Faye at R approximately 1.5 AU. These are the first short period comets in which silicate emission has definitely been detected. The broad emission features are about 25% above the continuum. No emission feature was present in the spectrum of P/Schaumasse; it is possible that the nucleus of P/Schaumasse was directly detected. If all of the observed flux originated from the nucleus, then the effective radius is about 3 km; the observed color temperature is consistent with a rapidly rotating nucleus. We present models that show how the shape of the silicate feature can depend on the way in which silicate and absorbing material are mixed in the grains.

Hanner, Martha S.↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
DOE OSTI2025

Characterization of the Multi-Coincident Bismuth Germanate Oxide Spectrometer at the Nuclear Counting Facility

A multi-detector γ-ray spectroscopy system including 3 high-purity germanium (HPGe) detectors has been developed at the Nuclear Counting Facility at Lawrence Livermore National Laboratory. This system operates not only in conventional singles γ-ray detection mode but also incorporates features like coincidence and anti-coincidence techniques. These advanced detection modes can significantly enhance the system’s sensitivity, making it a powerful tool for isotope identification and quantification, especially for complex samples with high background and overlapping peaks. In this re port, we present a comprehensive performance characterization of the Multi-Coincident Bismuth Oxide Spectrometer (MCBOS) using a variety of sample types, including 60 Co, 48 Sc, and mixed fission product samples. Each sample provides unique tests for the MCBOS capabilities. Additionally, a GEANT4 model of the MCBOS has been generated to aid with the characterization of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1998

Wavelet Analysis of SAR Images for Coastal Monitoring

The mapping of mesoscale ocean features in the coastal zone is a major potential application for satellite data. The evolution of mesoscale features such as oil slicks, fronts, eddies, and ice edge can be tracked by the wavelet analysis using satellite data from repeating paths. The wavelet transform has been applied to satellite images, such as those from Synthetic Aperture Radar (SAR), Advanced Very High-Resolution Radiometer (AVHRR), and ocean color sensor for feature extraction. In this paper, algorithms and techniques for automated detection and tracking of mesoscale features from satellite SAR imagery employing wavelet analysis have been developed. Case studies on two major coastal oil spills have been investigated using wavelet analysis for tracking along the coast of Uruguay (February 1997), and near Point Barrow, Alaska (November 1997). Comparison of SAR images with SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data for coccolithophore bloom in the East Bering Sea during the fall of 1997 shows a good match on bloom boundary. This paper demonstrates that this technique is a useful and promising tool for monitoring of coastal waters.

Liu, Antony K.↗
NASA NTRS2000

Fine Structure in the Corona and Solar Wind at High Heliographic Latitudes at Solar Maximum

Microstreams and pressure balance structures in fast solar wind were more easily detected at Ulysses at 2.2 AU over the poles than at Helios at 0.3 AU. This is because solar rotation brings sources of fast solar wind beneath sources of slow solar wind at a rate that depends on latitude, for the same size features. Dynamic interaction between the fast and slow wind tends to mix the flows and make features more difficult to detect with increasing distance from the Sun. A given sized feature takes proportionally longer to pass a longitude when it is at 80 degrees latitude than when it is at the equator. At solar maximum, Ulysses will mainly be sampling solar wind coming from above streamers and from near streamers. The data will provide information on fine structure at the tops of streamers - the stalks - and on the source of slow solar wind, The visibility of the boundaries between fast and slow wind and of the stalk will increase with increasing latitude. We will present quantitative calculations of the visibility of various sized features, with various differences in flow speed, at the location of Ulysses when it is over the south (in 2000) and north (in 2001) polar regions of the Sun.

Suess. S. T.↗
NASA NTRS2001

Fine Structure in the Corona and Solar Wind at High Heliographic Latitudes at Solar Maximum

Microstreams and pressure balance structures in fast solar wind were more easily detected at Ulysses at 2.2 AU over the poles than at Helios at 0.3 AU. This is because solar rotation brings sources of fast solar wind beneath sources of slow solar wind at a rate that depends on latitude, for the same size features. Dynamic interaction between the fast and slow wind tends to mix the flows and make features more difficult to detect with increasing distance from the Sun. A given sized feature takes proportionally longer to pass a longitude when it is at 80 degrees latitude than when it is at the equator. At solar maximum, Ulysses will mainly be sampling solar wind coming from above streamers and from near streamers. The data will provide information on fine structure at the tops of streamers - the stalks - and on the source of slow solar wind. The visibility of the boundaries between fast and slow wind and of the stalk will increase with increasing latitude. I will present quantitative calculations of the visibility of various sized features, with various differences in flow speed, at the location of Ulysses when it is over the south (in 2000) and north (in 2001) polar regions of the Sun.

Suess, S. T.↗
NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗
NASA NTRS1989

The 3.4 micron emission in comets

Emission features near 3.4 microns were detected in comet Bradfield (1987s) on 17 Nov. 1987 UT, and, marginally, on two earlier dates, with the Cooled Grating Array Spectrometer at the NASA Infrared Radio Telescope Facility (IRTF) (Brooke et al., 1988b). The central wavelength (3.36 microns) and width (approx. 0.15 microns) of the strongest feature coincide with those observed in comet Halley. A weaker emission feature at 3.52 microns and a strong feature extending shortward of 2.9 microns were also detected. This brings the number of comets in which these three features have been seen to three, two new (Bradfield, Wilson) and one old (Halley). It seems almost certain that the 3.4 micron features are emissions by C-H groups in complex molecules. Based on the similarity of the 3.4 micron features in comets Halley and Wilson, the authors suggest that a particular set of organic compounds may be common to all comets (Brooke et al. 1988a). The absence of the feature in some comets could then be due to photodestruction or evaporation of the organics when the comet approaches the sun, in combination with a predominance of thermal emission from non C-H emitting grains. Detection of the 3.4 micron emission feature in comet Bradfield at 4 = 0.9 AU provides support for this argument. Complex organics in comets could have been formed by particle irradiation of parent ices in the nucleus or been incorporated as grains at the time the comets formed. Since the most heavily irradiated layers of Halley would have been lost in its hundreds of perihelion passages, the authors believe the more likely explanation is that the 3.4 micron emitting material was incorporated in comet nuclei at the time of formation. The 3.4 micron comet feature resembles, but is not identical to, the interstellar 3.29 micron (and longer wavelength) emission features and the broad 3.4 micron feature seen in absorption toward the Galactic center. Detailed comparisons of cometary and interstellar organics will require comet spectra with signal-to-noise and spectral resolution comparable to that available in spectra of the interstellar medium. Such observations are currently being planned.

Brooke, Tim Y.↗
NASA NTRS1974

The application of ERTS imagery to monitoring Arctic sea ice

The author has identified the following significant results. Because of the effect of sea ice on the heat balance of the Arctic and because of the expanding economic interest in arctic oil and minerals, extensive monitoring and further study of sea ice is required. The application of ERTS data for mapping ice is evaluated for several arctic areas, including the Bering Sea, the eastern Beaufort Sea, parts of the Canadian Archipelago, and the Greenland Sea. Interpretive techniques are discussed, and the scales and types of ice features that can be detected are described. For the Bering Sea, a sample of ERTS-1 imagery is compared with visual ice reports and aerial photography from the NASA CV-990 aircraft. The results of the investigation demonstrate that ERTS-1 imagery has substantial practical application for monitoring arctic sea ice. Ice features as small as 80-100 m in width can be detected, and the combined use of the visible and near-IR imagery is a powerful tool for identifying ice types. Sequential ERTS-1 observations at high latitudes enable ice deformations and movements to be mapped. Ice conditions in the Bering Sea during early March depicted in ERTS-1 images are in close agreement with aerial ice observations and photographs.

Barnes, J. C.↗
DOE OSTI2022

SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control

Herein we present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Enhancing detection of topological order by local error correction

The exploration of topologically-ordered states of matter is a long-standing goal at the interface of several subfields of the physical sciences. Such states feature intriguing physical properties such as long-range entanglement, emergent gauge fields and non-local correlations, and can aid in realization of scalable fault-tolerant quantum computation. However, these same features also make creation, detection, and characterization of topologically-ordered states particularly challenging. Motivated by recent experimental demonstrations, we introduce a paradigm for quantifying topological states—locally error-corrected decoration (LED)—by combining methods of error correction with ideas of renormalization-group flow. Our approach allows for efficient and robust identification of topological order, and is applicable in the presence of incoherent noise sources, making it particularly suitable for realistic experiments. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We subsequently apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator. Finally, we extend LED to generic topological phases, including those with non-abelian order.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Advancing Tassel Detection and Counting: Annotation and Algorithms

Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Detecting Small-Scale topographic Changes and Relict Geomorphic Features on Barrier Islands using SAR

The shapes and elevations of barrier islands may change dramatically over a short period of time such as during a storm. Even between storms, sediment is constantly shifting to and from these islands and between different areas of the islands at varying rates and in varying amounts. Coastal scientists and engineers, however, are currently unable to measure these changes occurring over an entire barrier island at once. This three-year project, which is funded by NASA and jointly conducted by the Bureau of Economic Geology and the Center for Space Research at The University of Texas at Austin, is designed to overcome this problem by developing the use of interferometry from airborne synthetic aperture radar (AIRSAR) to detect changes in coastal topography. Surrogate measures of topography observed in fully polarimetric AIRSAR are also being investigated.

Gibeaut, James C.↗
DOE OSTI2020

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
NASA NTRS1995

ROSAT observations of the unusual supernova remnant CTB 80 containing the pulsar PSR 1951 + 32

The unusal supernova remnant CTB, 80 containing the 39.5 ms pulsar PSR 1951 + 32, has been observed with the Position Sensitive Proportional Counter (PSPC) and the High Resolution Imager (HRI) aboard ROSAT. The HRI image, centered on the pulsar, is composed of a bright compact core of approximately 1 arcminute radius containing the pulsar and a compact nebula, as well as a diffuse nebula extending approximately 5 arcminutes eastward of the pulsar. The PSPC allowed us to model the spectra of the point source, the compact nebula and the 5 arcminute diffuse nebula. For a power-law spectrum with photon index Gamma approximately 2 and an interstellar column density of N(sub H) approximately 3 x 10(exp 21)/cm(exp 21) the derived luminosities are approximately 2.3 x 10(exp 33) d(sub 2.5)(exp 2) ergs/s from the pointlike source, approximately 3.9 x 10(exp 33) d(sup 2.5)(exp 2) ergs/s from the compact nebula, and approximately 1.8 x 10(exp 33) d(sub 2.5)(exp 2) ergs/s from the 5 arcminutes diffuse nebula. In addition, the 2 deg diameter circular field of view of the PSPC reveals a hard emission feature southeast of the pulsar with a conical geometry extending out to the edge of the detector. The spectrum from this region is well described by a two-temperature Raymond-Smith thermal plasma with an average temperature of approximately 10(exp 7) K and a luminosity of approximately 10(exp 34)d(sub 2.5)(exp 2) ergs/s. Pulsations from the 39.5 ms puslar, PSR 1951 + 32, are detected at the 99% confidence level. The implied pulsed fration is approximately 35% with a complicated energy-dependent behavior. The compact core and the extended diffuse nebula can be explained as synchrotron radiation from the relativistic pulsar wind confined by the ram pressure of the surrounding inhomgeneous medium. The conelike feature detected southeast of PSR 1951 + 32 is consistent with emission from an optically thin SNR in the radiative cooling phase of its evolution.

Safi-Harb, Samar↗