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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 19 records

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NASA NTRS1988

A representational framework and user-interface for an image understanding workstation

Problems in image understanding involve a wide variety of data (e.g., image arrays, edge maps, 3-D shape models) and processes or algorithms (e.g., convolution, feature extraction, rendering). The underlying structure of an Image Understanding Workstation designed to support mulitple levels and types of representations for both data and processes is described, also the user-interface. The Image Understanding Workstation consists of two parts: the Image Understanding (IU) Framework, and the user-interface. The IU Framework is the set of data and process representations. It includes multiple levels of representation for data such as images (2-D), sketches (2-D), surfaces (2 1/2 D), and models (3-D). The representation scheme for processes characterizes their inputs, outputs, and parameters. Data and processes may reside on different classes of machines. The user-interface to the IU Workstation gives the user convenient access for creating, manipulating, transforming, and displaying image data. The user-interface follows the structure of the IU Framework and gives the user control over multiple types of data and processes. Both the IU Framework and user-interface are implemented on a LISP machine.

Schroeder, Joyce D.↗

NASA NTRS1989

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

Barrett, Eamon B.↗

NASA NTRS1989

Image understanding and interpretation in microwave diversity imaging

The authors investigate microwave imaging of metallic objects using a diversity method and interpret and predict the reconstructed image from an approach based on analysis of the scattering mechanism and a back-projection algorithm used in image retrieval. The connection between the various scattering mechanisms and the reconstructed images is discussed, what the images represent is interpreted, and a prediction is made as to what the image will look like over given spectral and angular windows. A brief description is given of the microwave diversity imaging system and the formulation of the microwave diversity imaging based on the physical optics approximation. The scattering mechanism of a complex shaped metallic object is then briefly reviewed and an alternate approach to interpreting the reconstructed image based on the understanding of the scattering mechanism and the reconstruction algorithm is given. Several numerical and experimental examples are included to support this interpretation approach.

Li, Hsueh-Jyh↗

NASA NTRS1986

Hypothesis integration in image understanding systems

The goal of this research is to develop a robust control strategy for constructing image understanding systems (IUS). This paper proposes a general framework based on the integration of related hypotheses. Hypotheses are regarded as predictions of the occurrences of objects in the image. Related hypotheses are clustered together. A composite hypothesis is computed for each cluster. The goal of the IUS is to verify the hypotheses. We constructed an image understanding system, SIGMA, based on this framework and demonstrated its performance on an aerial image of a suburban housing development.

Hwang, Vincent Shang-Shouq↗

NASA NTRS1985

Fuzzy vision - Multiple inputs speed image understanding

The fuzzy vision system designed for the interpretation of multiple successive images is described. The system is noise insensitive and can be mapped directly onto parallel processing hardware. The system consists of a region generator and a viewer which access a common semantic net; the components and operation of these subsystems are examined. The advantages and disadvantages of the fuzzy vision system are discussed. Diagrams of the region generator, viewer, and a semantic net are provided. An example depicting the operation of the fuzzy vision system is presented.

Meier, R. J., Jr.↗

DOE OSTI2021

Imaging, understanding, and control of nanoscale materials transformations

The development of liquid cells for transmission electron microscopy has enabled breakthroughs in our ability to follow nanoscale structural, morphological, or chemical changes during materials growth and applications. Time-resolved high-resolution imaging and chemical analysis through liquids opened the opportunity to capture nanoscale dynamic processes of materials, including reaction intermediates and the transformation pathways. In this article, a series of work is highlighted with topics ranging from liquid cell developments to in situ studies of nanocrystal growth and transformations, dendrite formation, and suppression of lithium dendrites through in situ characterization of the solid–electrolyte interphase chemistry. The understanding garnered is expected to accelerate the discovery of novel materials for applications in energy storage, catalysis, sensors, and other functional devices.

36 MATERIALS SCIENCE↗

NASA NTRS1988

Orbital navigation, docking and obstacle avoidance as a form of three dimensional model-based image understanding

Range imagery from a laser scanner can be used to provide sufficient information for docking and obstacle avoidance procedures to be performed automatically. Three dimensional model-based computer vision algorithms in development can perform these tasks even with targets which may not be cooperative (that is, objects without special targets or markers to provide unambiguous points). Role, pitch, and yaw of a vehicle can be taken into account as image scanning takes place, so that these can be correlated when the image is converted from egocentric to world coordinated. Other attributes of the sensor, such as the registered reflectance and texture channels, provide additional data sources for algorithm robustness.

Beyer, J.↗

NASA NTRS1987

Orbital navigation, docking and obstacle avoidance as a form of three dimensional model-based image understanding

Range imagery from a laser scanner can be used to provide sufficient information for docking and obstacle avoidance procedures to be performed automatically. Three dimensional model-based computer vision algorithms in development can perform these tasks even with targets which may not be cooperative (that is, objects without special targets or markers to provide unambiguous location points). Roll, pitch and yaw of the vehicle can be taken into account as image scanning takes place, so that these can be corrected when the image is converted from egocentric to world coordinates. Other attributes of the sensor, such as the registered reflectence and texture channels, provide additional data sources for algorithm robustness. Temporal fusion of sensor immages can take place in the work coordinate domain, allowing for the building of complex maps in three dimensional space.

Beyer, J.↗

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Computational modeling and neutron imaging to understand interface shape and solute segregation during the vertical gradient freeze growth of BaBrCl:Eu

In this work we apply continuum models to analyze phase change, heat transfer, fluid flow, solute transport, and segregation in order to understand prior neutron imaging observations of the vertical gradient freeze growth of Eu-doped BaBrCl. The models provide a rigorous framework in which to understand the mechanisms that are responsible for the complicated evolution of interface shape and dopant distribution in the growth experiment. We explain how a transition in the solid/liquid interface shape from concave to convex is driven by changes in radial heat transfer caused by furnace design. We also provide a mechanistic explanation of how dynamic growth conditions and changes of the flow structure in the melt result in complicated segregation patterns in this system. A growth pause caused by controller lock-up is shown to result in a band of solute depletion in accordance with classical theory. However, changing flow patterns during growth result in a non-monotonic axial distribution of solute that cannot be explained by simple application of classical segregation models. We assert that the approach presented here, namely the use of rigorous models in conjunction advanced diagnostics, such as neutron imaging, provides an exciting path forward for process optimization and control, accelerating the incremental advances that have, in the past, typically relied on empiricism, experience, and intuition.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The VIBES Are Shifting: Assessing Emergent Capabilities in Multi-Modal Models

Researchers assessing open-source domains such as the internet, and particularly those studying information conflict, often have no single prescribed workflow. In the course of their research, they may need to perform a diverse array of tasks far beyond simply identifying an ever-changing set of objects. These analytical tasks can include ascertaining the provenance of images, understand an image in the context of accompanying text-based data, or identifying indicators of digital image manipulation. They must further be able to do this at the scale of tens of thousands of images or more. Traditional machine vision models have typically lacked the flexibility and breadth of performance sufficient for these needs. The research team from Pacific Northwest National Laboratory assessed the performance of a single baseline CLIP ViT-L model against a series of analytical tasks relevant for the study of online information conflict.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Fill Factor Loss in Fielded Photovoltaic Modules Due to Metallization Failures, Characterized by Luminescence and Thermal Imaging

Understanding the causes of photovoltaic module failure in the field can be achieved using spatially-resolved imaging tools in combination with current-voltage (IV) characterization. We evaluate 23 field-degraded modules by comparing their IV characteristics with module images obtained by electroluminescence, photoluminescence, UV-fluorescence, and dark lock-in thermography. We identify primarily metallization failures (metallization series resistance, solder point heating, broken fingers, disrupted interconnects) in these modules that has led to a severe drop in the fill factor and up to 35% power loss over only 2 years of deployment.

14 SOLAR ENERGY↗

NASA NTRS2004

The Role of Auroral Imaging in Understanding Ionosphere-Inner Magnetosphere Interactions

The more ways we probe the ionosphere and inner magnetosphere, the better we can understand their interaction. For example, the multifaceted imaging of geospace with the IMAGE mission complements the more traditional in situ measurements made with many previous missions. Together they have enabled new knowledge of the ionosphere-magnetosphere (IM) coupling. The role of imaging the aurora in understanding this interaction has received renewed attention recently. Based on in situ data, such as FAST or DMSP, and our recent theories, we believe that imaging multiscale features of the aurora is a key component to gaining insight into the processes and mechanisms at work. This talk will explore how auroral imaging can be used to provide improved insight of the dynamics of IM interaction on micro and meso scales, with an emphasis on the current limitations and future possibilities of quantitative analyses.

Spann, Jim↗

DOE OSTI2021

Quantifying particle-scale 3D granular dynamics during rapid compaction from time-resolved in situ 2D x-ray images

Understanding the particle-scale dynamics of granular materials during rapid compaction and flow is of fundamental importance for manufacturing, planetary science, geology, and defense applications. Time-resolved 2D radiography and static 3D x-ray tomography are powerful in situ tools for studying particle-scale dynamics but provide detail only in 2D or with significant time-scale limitations, respectively. Here, we introduce a new method that uses 2D in situ x-ray imaging for determining time-resolved 3D particle-scale dynamics in rapidly compressed granular materials. The method employs initial particle packing structures obtained from x-ray tomography, a 2D x-ray image generation algorithm, and an optimization algorithm. We first describe and validate the method using finite element simulations. Furthermore, we then apply the technique to x-ray phase-contrast images obtained during rapid compaction of granular materials with varying particle morphology and sample thickness. The depth-resolved particle-scale dynamics reveal complex velocity and porosity fields evolving heterogeneously along and perpendicular to the compaction direction. We characterize these features, their fluctuations near the compaction front, and the compaction front thickness. Our technique can be applied to understanding granular dynamics during rapid compaction events, and rearrangements during slower, but non-quasi-static, flows.

36 MATERIALS SCIENCE↗

DOE OSTI2022

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

New perspectives on materials and device dynamics using time-resolved full-field diffraction X-ray imaging

Understanding how materials evolve during synthesis, processing, or device operation requires experimental access to structural dynamics across wide ranges of length and time scales, often in bulk samples or even within packaged devices. Time-resolved full-field diffraction X-ray microscopy has recently emerged as a powerful way to meet this need by combining the penetration and structural sensitivity of X-ray diffraction with objective-lens-based magnification, sensitive high-resolution X-ray imaging detectors, and pump-probe and real-time imaging strategies. Advances in full-field X-ray diffraction microscopy, often termed dark-field X-ray microscopy (DFXM), enable simultaneous imaging of extended fields of view while retaining crystallographic selectivity. Time-resolved DFXM promises to enable advances in materials processing and dynamics, electrochemical and photochemical processes, and the design of electronic devices. This Perspective summarizes the key instrumental concepts that define the performance of these methods, including the choice of imaging optics, detector considerations, and the impact of source time structure at synchrotron light sources and X-ray free-electron lasers (XFELs). We discuss the simultaneous use of complementary imaging modes that are increasingly used in practice. The early demonstrations of the potential of time-resolved DFXM include real-time defect and grain-boundary dynamics during metal annealing, stroboscopic imaging of functional devices with high strain sensitivity, and ultrafast pump-probe implementations at XFELs that directly visualize acoustic-wave propagation and energy dissipation in bulk crystals.

Dark-field X-ray microscopy↗

NASA NTRS2023

Tracking Nonradial Outflows in Extreme Ultraviolet and White Light Solar Images

Understanding the solar corona requires knowledge of its dynamics through its various layers and subsequent connectivity to the heliosphere. This requires understanding the nature of the outflows and the physical transitions through the middle corona (∼1.5–6.0 Rs). While this region is still inaccessible to in situ measurements, remote sensing observations are available, but their interpretation can be controversial due to line-of-sight effects and the nonradial motion of outflowing structures close to the Sun (≤3.0 Rs). In this work, we describe a method to mitigate these challenges by generating nonradial height–time profiles of outflows by using advanced image processing techniques. The northern and southern boundaries of a large equatorial streamer during the 2008 solar minimum were identified in STEREO/SECCHI solar images, using two different methodologies based on thresholds of brightness and piecewise polynomial function fitting. To address line-of-sight issues, we used tomographic reconstruction of the 3D distribution of the coronal electron density based on SECCHI/COR2 images. Spectral analysis of the time series of the position angle of the streamer boundary revealed its oscillatory nature at some heights at 36–48 and 10.5–14.6 hr. Dividing the distance between the northern and southern streamer boundaries in equal parts at each height, we obtained nonradial height–time paths from which we generated nonradial profiles of corona/solar wind plasma outflow. We tracked outflows as they moved uninterruptedly from the Sun in EUVI, through COR1 and into COR2. Finally, we discuss the preliminary results of nonradial plane-of-sky velocities for a coronal mass ejection and two small-scale features.

Nathalia Alzate↗

DOE OSTI2023

Temperature-Dependent Morphological Evolution during Corrosion of the Ni-20Cr Alloy in Molten Salt Revealed by Multiscale Imaging

Understanding the mechanisms leading to the degradation of alloys in molten salts at elevated temperatures is significant for developing several key energy generation and storage technologies, including concentrated solar and next-generation nuclear power plants. Specifically, the fundamental mechanisms of different types of corrosion leading to various morphological evolution characteristics for changing reaction conditions between the molten salt and alloy remain unclear. Here, in this work, the three-dimensional (3D) morphological evolution of Ni–20Cr in KCl–MgCl 2 is studied at 600 °C by combining in situ synchrotron X-ray and electron microscopy techniques. By further comparing different morphology evolution characteristics in the temperature range of 500–800 °C, the relative rates between diffusion and reaction at the salt–metal interface lead to different morphological evolution pathways, including intergranular corrosion and percolation dealloying. In this work, the temperature-dependent mechanisms of the interactions between metals and molten salts are discussed, providing insights for predicting molten salt corrosion in real-world applications.

36 MATERIALS SCIENCE↗