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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-time biomass feedstock particle quality detection using image analysis and machine vision

Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2022

A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA

Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.

58 GEOSCIENCES↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗
DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 5. Global Power Market Trends

The evolution of the electricity sector toward competitive modern markets is occurring over a long period and responding to rapid changes in technologies, business models, and other market factors. No reformed power market anywhere in the world looks the same today as it did a decade ago. Technology has enabled some of the evolution, but perhaps it is more important that people and institutions learn from experience how to make markets better over time. Governments still have an important role—not as the authority controlling prices, but as the authority safeguarding the integrity of the economic processes that create prices in response to supply and demand and ensure reliability and other objectives are met. More VRE resources will change market conditions, but they will not change market fundamentals. Prices provide important signals for new investments and how the grid operates—including new technologies and processes discussed elsewhere in this report. If poor market design or the exercise of market power by a large entity distort these signals, outcomes will cease to be efficient and the public will bear the additional cost. Fundamentally, prices reflect supply, demand, and the cost of technological options.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 4. Demand-Side Development

This chapter focuses on recent developments on the demand side of the electricity system in the United States and beyond. After decades of slow-moving change in power system technology, business models, regulatory structures, and even financing schemes, changes on the demand side are accelerating rapidly. These changes are also more tightly linking the supply side with the demand side. This chapter provides high-level summaries of traditional subjects like demand-side management, which includes energy efficiency and demand response, and then transition to a subject that has gathered increasing attention as the most dynamic change likely to impact power systems in the coming decades: beneficial electrification. In each case, we attempt to address the impacts on system flexibility to the degree possible. These demand-side subjects are widely considered essential elements of a clean grid strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 3. Transmission Grid-Supporting Technologies

High renewable penetration will bring different challenges to power system reliability and stability. Reduction of inertia has been of concern for more wide-spread instable events; however, with the advanced control functionalities provided by power electronics interface and proper storage compensation, renewable generators are capable of providing wide-range of grid services that can maintain and improve grid reliability and stability matrices. Many studies and testing has demonstrated those functions and many pilot projects throughout the world had proved the concepts are working.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 2. Distribution Issues and Tools

The continued growth in distributed energy resources is fundamentally changing the way the distribution system is operated and planned. Additionally, an increasing number of grid issues are being regularly experienced by distribution engineers within an increasing number of utilities. Fortunately, these fundamental changes and increasingly regular issues are being addressed via continual improvements in existing distribution system modeling tools—for both tools used by industry and researchers—and via novel modeling methods, which are combining transmission and distribution system modeling to investigate new, often complex, system interactions at the transmission/distribution power system interface. As DERs continue to increase more and more utilities, regulators, and developers are finding usable solutions in the deployment of smart inverters with ever more capable functionalities. This increase in functionality is welcome but comes at the cost of more complex analysis when determining what specific settings should be used for an individual interconnection. With more time, experience, and likely the further development of advanced analysis tools, even higher levels of DER integration will result in a reliable, resilient, low-cost, and safely operating grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Computer Vision: EO Imaging & Video Tasks, Metrics, and Datasets [Slides]

Classification is an assignment of an image to a certain category. Localization is identified by the box surrounding the object in the image. Object detection detects instances of semantic objects of a certain class. Semantic segmentation categorizes all the pixels of an image into classes of objects. Instance segmentation identifies each instance of each object in the image. Panoptic (i.e., showing or seeing everything at once) segmentation is a combination of semantic and instance segmentation and classifies all the pixels in the image.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computer Vision on Edge Devices for the Short Term Prediction of Cloud Cover

Edge Computing and IoT are important pieces of today's technological landscape. Here, we build a low-cost IoT sensor for sky imaging and program it using AWS GreenGrass, one of the leading IoT platforms. We demonstrate remote reprogramming of this device to load software that predicts sun shading events through the linear advection method, which is a baseline algorithm that can be used to benchmark algorithmic improvements in future work. Some future directions for sky imaging research are enumerated.

14 SOLAR ENERGY↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2020

Future Vision for Autonomous Ocean Observations

Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass; and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as EGO for the emerging glider operations should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.

47 OTHER INSTRUMENTATION↗
NASA NTRS1999

Stereo Vision Based Velocity Control

The next phase of the unmanned ground vehicle development sponsored by OSD calls for intelligent velocity control to support cross-country driving speeds of 10 mph by day and 5 mph by night.

vision speed control spline↗
NASA NTRS2012

PheniX: A New Vision for the Hard X-ray Sky

We are proposing a mission devoted to high energy X-ray astronomy that is based on a focusing telescope operating in the 1-200 keV energy range but optimized for the hard X-ray range. The main scientific topics concern: Physics of compact objects: The proximity of compact objects provides a unique laboratory to study matter and radiation in extreme conditions of temperature and density in strong gravitational environment. The emission of high energy photons from these objects is far from being understood. The unprecedented sensitivity in the high energy domain will allow a precise determination of the non-thermal processes at work in the vicinity of compact objects. The full 1-200 keV energy coverage will be ideal to disentangle the emission processes produced in the spacetime regions most affected by strong-gravity, as well as the physical links: disk-thermal emission-iron line-comptonisation-reflection-non-thermal emission-jets. Neutron stars-magnetic field-cyclotron lines: Time resolved spectroscopy (and polarimetry) at ultra-high sensitivity of AXP, milliseconds pulsars and magnetars will give new tools to study the role of the synchrotron processes at work in these objects. Cyclotron lines-direct measurement of magnetic filed-equation of state constraints-short bursts-giant flares could all be studied with great details. AGN: The large sensitivity improvement will provide detailed spectral properties of the high energy emission of AGN's. This will give a fresh look to the connection between accretion and jet emission and will provide a new understanding of the physical processes at work. Detection of high-redshift active nuclei in this energy range will allow to introduce an evolutionary aspect to high-energy studies of AGN, probing directly the origin of the Cosmic X-ray Background also in the non-thermal range (> 20 keV). Element formation-Supernovae: The energy resolution achievable for this mission (<0.5 keV) and a large high energy effective area are ideally suited for the 44Ti line study (68 and 78 keV). This radioactive nuclei emission will give an estimate of their quantities and speed in their environment. In addition the study of the spatial structure and spectral emission of SNR will advance our knowledge of the dynamics of supernovae explosions, of particles acceleration mechanisms and how the elements are released in the interstellar medium. Instrumental design: The progress of X-ray focusing optics techniques allows a major step in the instrumental design: the collecting area becomes independent of the detection area. This drastically reduces the instrumental background and will open a new era. The optics will be based on depth-graded multi-layer mirrors in a Wolter I configuration. To obtain a significant effective area in the hundred of keV range a focal length in the 40-50 meters range (attainable with a deployable mast) is needed. In addition such a mission could benefit from recent progress made on mirror coating. We propose to cover the 1-200 keV energy range with a single detector, a double-sided Germanium strip detector operating at 80 K. The main features will be: (a) good energy resolution (.150 keV at 5 keV and <.5 keV at 100 keV), (b) 3 dimensional event localization with a low number of electronic chains, (c) background rejection by the 3D localization, (d) polarisation capabilities in the Compton regime.

PHENIX↗