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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Fabrication of coronagraph masks and laboratory scale star-shade masks: characteristics, defects and performance

NASA WFIRST mission has planned to include a coronagraph instrument to find and characterize exoplanets. Masks are needed to suppress the host star light to better than 10-8 – 10-9 level contrast over a broad bandwidth to enable the coronagraph mission objectives. Such masks for high contrast coronagraphic imaging require various fabrication technologies to meet a wide range of specifications, including precise shapes, micron scale island features, ultra-low reflectivity regions, uniformity, wave front quality, etc. We present the technologies employed at JPL to produce these pupil plane and image plane coronagraph masks, and lab-scale external occulter masks, highlighting accomplishments from the high contrast imaging testbed (HCIT) at JPL and from the high contrast imaging lab (HCIL) at Princeton University. Inherent systematic and random errors in fabrication and their impact on coronagraph performance are discussed with model predictions and measurements.

Casey Wilson, Robert↗
DOE OSTI2021

Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control

Abstract Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets. The multivariate linear unmixing methods generally explore similarities in the energy dimension, but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) explicitly incorporate spatial correlations in the form of kernel functions but is computationally intensive. Here, we implement a GP method operating on the full spatial domain and reduced representations in the energy domain. In this multivariate GP, the information between the components is shared via a common spatial kernel structure, while allowing for variability in the relative noise magnitude or image morphology. We explore the role of kernel constraints on the quality of the reconstruction, and suggest an approach for estimating them from the experimental data. We further show that spatial information contained in higher-order components can be reconstructed and spatially localized.

36 MATERIALS SCIENCE↗
DOE OSTI2025

AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere

Materials Acceleration Platforms (MAPs) – also known as self-driving laboratories– present a new paradigm for materials science and promise an order of magnitude accelerated materials discovery compared to the traditional trial-and-error approach. Metal halide perovskites (MHPs) are an emerging class of materials for optoelectronic applications but are plagued by irreproducible optoelectronic quality, particularly for films fabricated in a humid atmosphere. Here, in this work, a machine learning (ML)-guided closed-loop platform is developed with a multimodal data fusion approach to predict synthesis–property relations for the optical quality of MHP thin films in relative humidities (RHs) ranging from 5–55%. The efficiency of this approach is confirmed by the fast-dropping learning rate to 2% after experimentally sampling less than 1% of the possible 5,000+ combinations. The prediction of synthesis–property relations is done by optical and imaging characterizations. In situ photoluminescence characterization revealed the origin of thin film quality variation at different RH. These insights provide an avenue for controlling the MHP crystallization by fine-tuning the synthesis parameters and RH for a given chemistry, thus lifting the need for stringent atmosphere control. The MAP enables an accelerated screening and understanding of the synthesis design space, facilitating rational synthesis recipe choice for a wide range of materials.

AI-driven robot↗
NASA NTRS1987

Remote sensing technology and applications

Electro-optical imaging sensors form the principal devices that are typically used to obtain images of earth from satellite platforms, and substantial improvements in optics, electro-optical detection devices, cooling subsystems and composite structures have enabled the high quality performance exhibited by the current instrumentation. This paper reviews the fundamentals of system design for imaging sensors such as the Thematic Mapper on board Landsat, points out the key areas of current R&D, and briefly alludes to some important applications.

Schueler, C. F.↗
DOE OSTI2022

Uncertainties Induced by Processing Parameter Variation in Selective Laser Melting of Ti6Al4V Revealed by In-Situ X-ray Imaging

Selective laser melting (SLM) additive manufacturing (AM) exhibits uncertainties, where variations in build quality are present despite utilizing the same optimized processing parameters. In this work, we identify the sources of uncertainty in SLM process by in-situ characterization of SLM dynamics induced by small variations in processing parameters. We show that variations in the laser beam size, laser power, laser scan speed, and powder layer thickness result in significant variations in the depression zone, melt pool, and spatter behavior. On average, a small deviation of only ~5% from the optimized/reference laser processing parameter resulted in a ~10% or greater change in the depression zone and melt pool geometries. For spatter dynamics, small variation (10 μm, 11%) of the laser beam size could lead to over 40% change in the overall volume of the spatter generated. The responses of the SLM dynamics to small variations of processing parameters revealed in this work are useful for understanding the process uncertainties in the SLM process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Overview of the earth resources program of the Jet Propulsion Laboratory

Proposed is an investigation to develop and correlate remote sensing from satellites and aircraft into an effective prototype system for California statewide water quality surveillance. Aircraft overflights, computer enhanced recognition of pollution signatures, ground truth measurements, and data processing will be employed to develop a simplified and automated monitoring system. ERTS A image data will be used for nine major drainage basins of California having unique water quality influences from waste discharges, thermal contributions, oil spills, turbidity, sea water intrusion, agricultural drainage, toxicity, biostimulation, and eutrophication.

Burcham, D. P.↗
NASA NTRS1989

Infrared thermal imaging figures of merit

Commercially available types of infrared thermal imaging instruments, both viewers (qualitative) and imagers (quantitative) are discussed. The various scanning methods by which thermal images (thermograms) are generated will be reviewed. The performance parameters (figures of merit) that define the quality of performance of infrared radiation thermometers will be introduced. A discussion of how these parameters are extended and adapted to define the performance of thermal imaging instruments will be provided. Finally, the significance of each of the key performance parameters of thermal imaging instruments will be reviewed and procedures currently used for testing to verify performance will be outlined.

Kaplan, Herbert↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

June 2023 Canadian Wildfire Effects on Hampton Roads VA using ASDC Data

In June of 2023 thousands of acres of wildfires spread across northwestern Canada. This event emitted huge amounts of smoke, and pollutants that travelled east across the North America, the Atlantic ocean and eventually all the way to Continental Europe. This smoke created a dangerous air quality event throughout the Atlantic Coast. As climate change increases the prevalence of wildfires more cities and states will be affected by the dangerous smoke and pollutants produced by these events. In this poster we documented and investigated the June 06th Air Quality Event over Hampton Roads, VA. To perform a comparative analysis, we utilized two data sources: NASA’s Atmospheric Science Data Center we used the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), the Deep Space Climate ObserVatoRy (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC), data product, and the EPA’s ground-based Air Quality System (AQS).

Air Quality↗
DOE OSTI2021

Surfactant inhibition mechanisms of carbonate mineral dissolution in shale

Surfactants are common additives to hydraulic fracturing and enhanced oil recovery (EOR) fluids, and are under consideration for amendment to supercritical carbon dioxide for geological carbon sequestration (GCS). The effect of a common anionic surfactant, internal olefin sulfonate (IOS), on mineral dissolution from shale into brine was evaluated. When added to brine at concentrations exceeding the critical micelle concentration (94 mg/L), IOS inhibited carbonate mineral dissolution in an Eagle Ford shale, as well as dissolution of optical quality calcite (the dominant carbonate in the shale). Laser profilometry images provide spatial resolution across > 3 orders of magnitude, and indicate that IOS addition to brine both enhances the formation of new etch pits in calcite, and impedes their further growth. Time-of-flight secondary ion mass spectrometry surface profiles show for the first time that IOS preferentially adsorbs at calcite pit edges versus flat calcite surfaces (i.e., terraces). Surface pressure calculations, sulfur K-edge near edge X-ray absorption fine structure (NEXAFS) spectroscopy results, and density functional theory (DFT) calculations support this observation; the DFT results indicate that the sulfonate head group of the IOS molecule binds strongly to the calcite step site as compared to the terrace site. The S K-edge NEXAFS results indicate that IOS adsorbed more to etched calcite surfaces compared to smooth calcite surfaces. Overall, the results indicate that weak adsorption on flat calcite surfaces (i.e., terraces) disrupts water structure and enhances mass transfer of dissolution, while strong adsorption on calcite pit edges displaces adsorbed water and inhibits further etch pit growth. This work provides the first direct evidence of preferential adsorption of IOS to etched calcite surfaces and links it to macroscopic dissolution kinetics. Finally, this work has implications for surfactant-containing fluids used in hydraulic fracturing, EOR and potentially GCS for subsurface injection into carbonate rich reservoirs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dense Image Matching Uncertainty Estimation and Confidence Metrics

Dense stereo matching takes overlapping image pairs as input and outputs a disparity map which encodes pixel-by-pixel matches between the images. Recently, there has been an interest in ranking the quality, or even quantifying the accuracy, of disparity estimates. The proposed methods can be described as either uncertainty estimators or confidence metrics. Uncertainty estimators are a small minority of the research. However, they have the potential to be the most useful because they estimate disparity accuracy (in pixel units) that can be used to threshold matches or carried forward using error propagation. The majority of the research deals with confidence metrics which give an ordinal (or binary) ranking of a match’s quality relative to other matches. Confidence metrics do not have units and thus are useful primarily for thresholding matches from mismatches. The methods could also be described as handcrafted or deep-learning based. The majority of the research focused on outdoor driving scenes. Hence, our interest–application to a satellite semi-global matching pipeline–is a domain shift that may challenge deep-learning based methods. We conclude by recommending five handcrafted and two deep-learning based methods for evaluation in our pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Photometric Data Set for Cosmology

In this work, we describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmologic analyses, and provide usage notes aimed at the broad astrophysics community. Y3 GOLD improves on previous releases from DES, Y1 GOLD, and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification. Y3 GOLD comprises nearly 5000 deg 2 of grizY imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching a signal-to-noise ratio ~10 for extended objects up to iAB ~ 23.0, and top-of-the-atmosphere photometric uniformity <3 mmag. Compared to DR1, photometric residuals with respect to Gaia are reduced by 50%, and per-object chromatic corrections are introduced. Y3 GOLD augments DES DR1 with simultaneous fits to multi-epoch photometry for more robust galactic color measurements and corresponding photometric redshift estimates. Y3 GOLD features improved morphological star–galaxy classification with efficiency >98% and purity >99% for galaxies with 19 < i AB < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmologic analysis samples.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1972

Hydrologic interpretations based on infrared imagery of Long Island, New York

Six remote-sensing flights over Long Island's north and south shores were made during the period July 13, 1967, to February 25, 1970. Infrared imagery in the 8- to 14-micrometer range was obtained; results varied from poor to excellent in quality. The ability of the RS 7 and Reconofax 4 imagers to discern thermal contrasts of as little as 1 to 2 C (Celsius) permitted identification of areas of heavy ground-water discharge. These areas were concentrated primarily along the eroded headlands of the north shore and in the lower reaches of watercourses draining into Great South Bay. Only a few highly localized examples of direct ground-water discharge into the embayments along Long Island's south shore were detected in the imagery. Thermal loading emanating from a powerplant near Oceanside is shown to be quickly dissipated in Middle Bay. Optimal time for the collection of infrared imagery for hydrologic studies on Long Island is in summer and in winter, when surface-water thermal differences are relatively large.

Pluhowski, E. J.↗
NASA NTRS1977

Proceedings of the Eleventh International Symposium on Remote Sensing of Environment, volume 2

Application and processing of remotely sensed data are discussed. Areas of application include: pollution monitoring, water quality, land use, marine resources, ocean surface properties, and agriculture. Image processing and scene analysis are described along with automated photointerpretation and classification techniques. Data from infrared and multispectral band scanners onboard LANDSAT satellites are emphasized.

Source record↗
NASA NTRS1993

Geometric moire methods with enhanced sensitivity by optical/digital fringe multiplication

A robust fringe-shifting and image processing scheme is applied to geometric moire experiments. High quality contour maps of U, V and W displacement fields are obtained with sensitivity enhanced by a factor of ten. The method is compatible with complicated intensity distributions, variable bar-to-space ratios of gratings, and optical noise. It is applicable to large fields for both in-plane moire and shadow moire measurements.

Han, B.↗