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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Design, Fabrication and Testing of Surveillance Test Articles for MSR Materials Degradation Management

This report details the design, fabrication, and testing of surveillance test articles aimed at assessing material damage in reactor-relevant environments for effective degradation management. Two types of surveillance test articles with reduced sizes were developed based on design algorithms and finite element modeling: welded design and interlocking design. A furnace heating setup was adopted to apply multiple thermal cyclic loading profiles on the test articles with a temperature range of 500°C - 700°C, while the strain response was monitored using a digital image correlation technique. The testing results demonstrated the successful capturing of expected strain range for welded design while machining tolerance should be improved to engage strain coupling in the interlocking design. Mid-term (500 hours) and long-term (1500 hours) cyclic tests were conducted on welded test articles. A constant strain range of ~0.6% was observed at the specimen with testing under 500 hours, while a gradual decrease of strain at specimen was observed after 500 hours. Non-destructive evaluation through X-ray computed tomography confirmed the microcracks in the welds at specimen-driver joints after cyclic test that caused the strain change. Creep testing of the specimen after long-term cyclic test revealed a short creep life than expected. A multi-profile cyclic test was also conducted on a test article and demonstrated consistent strain response under different temperature ramp rates. The report also briefly discussed the challenges and future research efforts to advance test article development for material surveillance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Towards retrieving cloud top entrainment velocities from MISR cloud motion vectors

Although important, direct retrievals of entrainment rates in cloud-topped planetary boundary layer (PBL) remain elusive. Here we present a novel technique for retrieving cloud-top entrainment velocities using only Multi-angle Imaging Spectro-Radiometer (MISR) stereoscopic retrievals of cloud-motion vectors (CMVs) and cloud-top heights (CTHs). Mesoscale vertical air velocity at CTH is diagnosed from the continuity equation and then used to derive entrainment velocities from the PBL mass-budget equation. The algorithm is demonstrated through a case of marine stratocumulus deck off the California coast, with comparisons made against data from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis (ERA5) and the data from other satellites. MISR low-cloud CTH for this case were lower than the ERA5 reported PBL depth by 189 ± 87 m. These differences in cloud top heights partly modulate the differences in the ERA5 and MISR horizontal winds, with larger differences in meridional over zonal wind components. Average difference between ERA5 and MISR derived mesoscale vertical air motion at cloud top was 0.14 ± 0.73 cm s −1 , while the same for entrainment rate was −0.09 ± 0.46 cm s −1 . The uncertainties in the utilized CTHs and CMVs are propagated to derive systematic and random retrieval uncertainties. Fractional uncertainty is lower than 25 % when the retrieved mesoscale vertical air motion is stronger than ±0.04 cm s −1 and entrainment velocities are stronger than ±0.03 cm s −1 . These results showcase the ability to derive mesoscale vertical air motion and entrainment rates from MISR observations and motivate its extension to generate a global climatology leveraging its full 23-year record (2000–2022). Nonetheless comprehensive validation of the retrievals is warranted through comparisons with estimates from an independent dataset across diverse weather conditions.

Mitra, Arka [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2026

Thermal neutron imaging detector with wavelength shifting fiber and digital readout

Here, we developed a large area, digital thermal neutron imaging detector. The detector uses a 6 LiF/ZnS(Ag) neutron-sensitive scintillator combined with wavelength shifting fiber technology. The signals from fiber channels are amplified, integrated, and digitized using individual analog-to-digital converters for each channel. The neutron position is determined from the digitized signal using a least-squares gaussian-fitting algorithm. The detector size was 77 × 38 cm 2 (2926 cm 2 ), it provided 1.3 mm resolution, and its resolution can further be improved. The detector was designed for a powder diffraction neutron scattering beamline and provided substantial improvement of d-space resolution compared with existing detectors. This detector may have broader applications due to its large area and high spatial resolution extended over its large area.

6LiF/ZnS(Ag) scintillator↗
DOE OSTI2024

CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI

Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: the tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-aperture photometry, mapping the light distribution of the sources. The accuracy (σNMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Notes on Real-Beam Ground Mapping with Monopulse Radar

The spatial awareness required of modern flight systems is facilitated by images generated by ground-mapping radar. In the forward and aft directions, synthetic aperture techniques are not viable, leaving us with enhancing real aperture radar data. Enhancing real aperture radar data with monopulse information can be achieved with any of several monopulse beam sharpening techniques. Two such algorithms are discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

CESER: wildfire mapping

Multispectral satellite imagery has been demonstrated to accurately detect wildfires over a variety of land cover types at coarse- and medium-resolution (i.e., 1km to 30m), typically by detecting burned area after the fire has caused substantial damage. We developed an algorithm to map active fire (i.e., flame) in 2-meter resolution WorldView time-series imagery and monitor fire trajectory over time. Our results depict robust mapping across scenes collected from different fires over grassy and forested land cover, identifying fires as small as 4m 2 throughout the image. Operational deployment at scale may provide valuable near-real time maps of active fire for responders to leverage for timely, targeted mitigation efforts. This white paper describes the current capability specifications and indicates requirements for scaling.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Miscentring of optical galaxy clusters based on Sunyaev–Zeldovich counterparts

ABSTRACT The ‘miscentring effect’, i.e. the offset between a galaxy cluster’s optically defined centre and the centre of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalogue from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev–Zeldovich cluster catalogue from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \le z \le 1.4$ over an area of 469 deg$^2$. By modelling the distribution of centring offsets in this fiducial sample, we find a miscentred fraction (corresponding to clusters offset by more than 330 kpc) of ∼25 per cent, a value consistent with previous miscentring studies. We examine the image of each miscentred cluster in our sample and identify one of several reasons to explain the miscentring. Some clusters show significant miscentring for astrophysical reasons, i.e. ongoing cluster mergers. Others are miscentred due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentring from the sample, we find a considerably smaller miscentred fraction, $\sim 10~\,\rm per\,cent$. We show that the gravitational lensing signal within 1 Mpc of miscentred clusters is considerably smaller than that of well-centred clusters, and we suggest that the ACT SZ centres are a better estimate of the true cluster potential centroid.

Ding, Jupiter (ORCID:0000000296119799)↗
DOE OSTI2025

Dark Energy Survey Year 6 results: cell-based coadds and METADETECTION weak lensing shape catalogue

We present the metadetection weak lensing galaxy shape catalogue from the 6-yr Dark Energy Survey (DES Y6) imaging data. This data set is the final release from DES, spanning 4422 deg 2 of the southern sky. We describe how the catalogue was constructed, including the two new major processing steps, cell-based image coaddition, and shear measurements with metadetection. The DES Y6 M etadetection weak lensing shape catalogue consists of 151 922 791 galaxies detected over riz bands, with an effective number density of n eff = 8.22 galaxies per arcmin 2 and shape noise of σ e = 0.29. We carry out a suite of validation tests on the catalogue, including testing for point spread function (PSF) leakage, testing for the impact of PSF modelling errors, and testing the correlation of the shear measurements with galaxy, PSF, and survey properties. In addition to demonstrating that our catalogue is robust for weak lensing science, we use the DES Y6 image simulation suite to estimate the overall multiplicative shear bias of our shear measurement pipeline. We find no detectable multiplicative bias at the roughly half-per cent level, with m = (3.4 ± 6.1) x 10 –3 , at 3σ uncertainty. This is the first time both cell-based coaddition and Metadetection algorithms are applied to observational data, paving the way to the Stage-IV weak lensing surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SITCOMTN-154: Initial studies of photometric redshifts with LSSTComCam from DP1

This technote holds reports based on the first analyses of the Data Preview 1 (DP1) data by the Science Unit for photometric redshifts. Although photometric redshifts are not an official DP1 data product, the "Photo-z Science Unit" generated photo-z estimates for every galaxy in DP1 using the available multi-band imaging on a best-effort basis. This work included developing training and test datasets by matching DP1 data to high-quality reference redshifts obtained with spectroscopy, Grism data, and multi-band photometry. The Science Unit used the RAIL software package to make photometric redshift estimates using eight different algorithms, developed simple scientific performance metrics, used those metrics to explore how the performance of the algorithms varied with configuration changes, derived more optimized configurations of the algorithms and tested the performance of those configurations. This work, the resulting data products and expected data distribution mechanism are all described there.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline to train the trigger algorithm.

Damish, Stephanie↗
DOE OSTI2025

Statistically elucidated responses from low-signal contrast mechanisms in ultrafast electron microscopy

The emergence of ultrafast electron microscopy (UEM) has enabled the discovery of strongly correlated dynamic mechanisms, including electron–phonon coupling, structural phase transitions, thermal transport, and electromagnetic deflection. Most UEM systems operate stroboscopically, meaning that the technique is susceptible to artifacts, mistakes, and misinterpretation of the data due to extensive experimental effort. In contrast to the ultrafast designation, data acquisition is extraordinarily slow because the electron beam has significantly reduced signal compared to traditional transmission electron microscopy due to pulsing the electron beam. Consequently, the sample may drift, tilt, or undergo irreversible structural changes that are independent of the time-resolved dynamics throughout the experimental time frame. Furthermore, these datasets require significant user interpretation that can be problematic when proper controls are not implemented thoroughly. Here, we demonstrate a new algorithm designed to separate ultrafast structural dynamics from long-term artifacts using a LiNbO 3 sample experiencing electrically driven surface acoustic wave propagation. Additionally, we provide examples of the impact of user bias when analyzing the data and provide a methodology, which enables the extraction of time-resolved responses when the image signal is extraordinarily low. Overall, the goal of this publication is to provide methods that validate the experimental results and reduce researcher biases during UEM data interpretation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline that will train the trigger algorithm.

Damish, Stephanie↗
DOE OSTI2024

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High redshift LBGs from deep broadband imaging for future spectroscopic surveys

Lyman break galaxies (LBGs) are promising probes for clustering measurements at high redshift, z > 2, a region only covered so far by Lyman-α forest measurements. Here, in this paper, we investigate the feasibility of selecting LBGs by exploiting the existence of a strong deficit of flux shortward of the Lyman limit, due to various absorption processes along the line of sight. The target selection relies on deep imaging data from the HSC and CLAUDS surveys in the g, r, z and u bands, respectively, with median depths reaching 27 AB in all bands. The selections were validated by several dedicated spectroscopic observation campaigns with DESI. Visual inspection of spectra has enabled us to develop an automated spectroscopic typing and redshift estimation algorithm specific to LBGs. Based on these data and tools, we assess the efficiency and purity of target selections optimised for different purposes. Selections providing a wide redshift coverage retain 57% of the observed targets after spectroscopic confirmation with DESI, and provide an efficiency for LBGs of 83 ± 3%, for a purity of the selected LBG sample of 90 ± 2%. This would deliver a confirmed LBG density of ~ 620 deg$^{-2}$ in the range 2.3 < z < 3.5 for a r-band limiting magnitude r < 24.2. Selections optimised for high redshift efficiency retain 73% of the observed targets after spectroscopic confirmation, with 89 ± 4% efficiency for 97 ± 2% purity. This would provide a confirmed LBG density of ~ 470 deg$^{-2}$ in the range 2.8 < z < 3.5 for a r-band limiting magnitude r < 24.5.A preliminary study of the LBG sample 3d-clustering properties is also presented and used to estimate the LBG linear bias. A value of b$_{LBG}$ = 3.3 ± 0.2 (stat.) is obtained for a mean redshift of 2.9 and a limiting magnitude in r of 24.2, in agreement with results reported in the literature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Predicting ptychography probe positions using single-shot phase retrieval neural network

Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of 10 2 pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2026

rustpix

rustpix is a high-performance, open-source Rust library with first-class Python bindings (via PyO3) for processing pixel-detector data in neutron imaging. It targets time-stamping detectors such as Timepix3 (TPX3) at ORNL's Spallation Neutron Source (VENUS beamline), where each detected neutron deposits charge across a cluster of pixels within a very high-rate event stream (96M+ hits/sec). rustpix parses TPX3 event data in parallel using memory-mapped I/O, offers four interchangeable clustering algorithms (ABS adjacency-based search, DBSCAN, graph/union-find connected components, and a parallel grid method), and extracts weighted, super-resolved centroids to produce neutron-event lists. A streaming architecture lets it process files larger than available memory. rustpix is distributed as a pip-installable Python package (with NumPy integration), Rust crates, a command-line tool, and an interactive GUI; it writes HDF5, Apache Arrow, and CSV; and it is designed to extend to TPX4 and other detector types. Released as open-source under the MIT License.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗