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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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Oral presentation describing the development and commissioning of the MUlti-Segmented Electron conversion Spectrometer (MUSES).
X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.
Microfluidic reactors have remarkably promoted the synthesis and investigation of advanced nanomaterials due to their continuous mode and accelerated heat/mass transfer. Notably, segmented microfluidic flow reactors (SMFRs) are an important class of microfluidic reactors that have been developed to accurately manipulate nanomaterial synthesis by further improvement of the residence time distributions and unique flow behaviors. This review provided a survey of the nanomaterial synthesis in SMFRs for the aspects of fluid dynamics, flow patterns, and mass transfer among and within distinct phases and provided examples of the synthesis of versatile nanomaterials via the use of different flow patterns.
Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.
Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information
Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.
This presentation describes a technoeconomic analysis using NLR's Windfarm Operations & Maintenance cost-Benefit Analysis Tool (WOMBAT) to compare operational expenses and levelized cost of energy for wind turbines using standard spherical roller bearings with a novel segmented journal bearing developed by Miba.
The present disclosure is directed to the operation and turndown of a segmented annular combustion system. The method includes injecting, via a fuel nozzle, a combustible mixture into a primary combustion zone between an adjacent pair of integrated combustor nozzles and burning the combustible mixture. The method further includes flowing air and injecting fuel into a premixing channel defined within a first integrated combustor nozzle to produce a second combustible mixture. The second combustible mixture is injected into a secondary combustion zone where it is combusted. The flow of combustion gases is accelerated, via turbine nozzles of the integrated combustor nozzles, toward turbine blades of a downstream turbine section. The method permits turndown of the combustion system by reducing or shutting off fuel to various components of the combustion system.
The present disclosure is directed to a segmented annular combustion system including a first panel fuel injector including a premix air plenum, a fuel plenum, and a plurality of first side premixing channels, where each first side premixing channel is in fluid communication with the premix air plenum, the fuel plenum, and a respective first side injection aperture of a plurality of first side injection apertures. A second panel fuel injector is circumferentially spaced from the first panel fuel injector and includes a premix air plenum, a fuel plenum, and a plurality of second side premixing channels where each second side premixing channel is in fluid communication with the premix air plenum, the fuel plenum, and a respective second side injection aperture of a plurality of second side injection apertures. A fuel nozzle is disposed circumferentially between the first panel fuel injection and the second panel fuel injector.
As the number of connected devices in the energy grid increase exponentially, so too are the cybersecurity risks. With the development of modern communications standards such as 5G and beyond the extent to which devices will continue to connect will continue to increase exponentially along with the inherent risks. However, 5G also includes features to help address cybersecurity concerns and therefore helping to mitigate many of these risks. This paper proposes a new service-based network architecture implementing network-slicing capabilities for connected systems and devices to improve performance, availability, security, and reliability of the grid devices and services. This paper considers the quality of service requirements and criticality of services needed for securely monitoring, operating, and securing Distributed Energy Resource (DER) devices. From developed use cases, network slicing is implemented based on these requirements and resource allocations. This work then highlights examples of how slicing can help prevent standard existing attack methods such as a denial-of-service or similar attack which limits resource availability and network bandwidth to the service and thus limiting its ability to affect other services by misbehaving. The designed network architecture use case will be further tested on a local virtualized testbed to verify secure operation and availability of services. Using hardware-in-the-loop devices and systems on this local testbed, this fully segmented, secure network may be realized and evaluated. Finally, this paper presents the results of this testing.
A 3D track segment seeding engine core based on the Tiny Triplet Finder has been implemented and tested in a low-cost FPGA device. The seeding engine is designed to preselect and group hits (stubs) from cylindrical detector layers to feed subsequent track fitting stage. The seeding engine consists of a Hough transform space for r-z view and a Tiny Triplet Finder for r-phi view to implement 3D constraints. The seeding engine is organized as a pipeline so that each hit is processed in a single clock cycle. Test results show that the seeding engine operates as expected.
Workflow and results from a multi-lab examination of various machine learning based image segmentation techniques.
The present disclosure is directed to a fuel injection module for a segmented annular combustion system. The fuel injection module includes a housing body, a fuel nozzle portion, and at least one fuel injection lance. The fuel nozzle portion is fluidly coupled to a fuel nozzle plenum within the housing body, and the at least one fuel injection lance is fluidly coupled to an injector fuel plenum within the housing body. In some cases, the fuel nozzle portion is a bundled tube fuel nozzle having one or more subsets of tubes. The fuel injection lances are positioned along a radial side of the housing body or circumferentially between two subsets of tubes. Liquid fuel cartridges extend through the fuel nozzle portion, the fuel injection lances, or both.
A commonly applied method for the non-destructive characterization of the radioactive inventory of waste packages (e.g. 200-l drums) is segmented gamma-scanning. The gamma-emitting inventory is identified via their characteristic lines in the measured spectra. The corresponding net peak areas allow for quantification applying appropriate models. The identification and quantification of beta-emitters present in the waste package require further evaluation. While some of them emit gamma-rays, too, which can be used for identification and quantification, pure beta-emitters contribute to the gamma-spectra only by their Bremsstrahlung-spectra. {sup 32}P, {sup 90}Sr and {sup 90}Y are typical representatives of these isotopes. A method is developed for identification and quantification of these pure beta-emitters in radioactive waste packages. Origin of the procedure is the evaluation of the measured gamma-spectrum. After the identification of all lines present in the spectrum (characteristic gamma-lines, single- and double-escape-lines, X-ray-lines, annihilation peak etc.) this information is used together with calibration data of the detection system and further a-priori information like matrix composition, cross-section data from databases etc. to simulate the spectra without the Bremsstrahlung-components by an appropriate method. This may be Monte Carlo simulations or, like in our case, a computer code taking into account mathematical descriptions of the physical effects like photo effect, (multiple) Compton scattering, backscattering etc. and the attenuation effects of the waste matrix. In any case, the resulting spectrum must simulate the real spectrum without its Bremsstrahlung-component as good as possible. Subtracting the simulated from the measured spectrum then results in the Bremsstrahlung-components caused by the beta-emitters. Prior to evaluation, a set of Bremsstrahlung-spectra is calculated and stored for different beta-emitters in different matrices using the Bethe-Heitler equation. This data set can be extended for new conditions, if necessary, and is allocated in a data base for future application. Applying a non-linear optimization using the generalized reduced gradient method for comparing the calculated with the derived Bremsstrahlung-spectra results in the identification of the beta-emitter and a factor, which is proportional to its activity. The proportionality is determined by previous calibration measurements. The application of the method is demonstrated by measurements on a 200-l mock-up drum and a real waste package. (authors)
The present disclosure relates to a thermal emissivity control system. The system may have a segmented array that makes use of a thermally conductive base layer configured to be connectable to an external heat generating subsystem, with the base layer including a thermally emissive surface. The array may also have a plurality of actuation elements at least one of positioned on or adjacent to the thermally emissive surface. A plurality of movable shutter elements is disposed adjacent one another in a grid pattern, and controlled in movement by the actuation elements to create gaps of controllably varying dimension therebetween. The shutter elements control at least one of a magnitude of, or direction of, thermal radiation through the gaps.
Disclosed is a miniaturized plasma propulsion device with minimized surface area of the thruster walls exposed to the plasma and, as a result, reduced plasma-surface interactions including a set of segmented electrodes to facilitate the following improvements compared to relevant existing technologies: 1) control of the plasma flow including focusing of the plasma plume 2) increase of the thrust 2) reduction of inefficiencies associated with the electron cross field current, and 3) mitigation of low frequency oscillations. The electrodes affect all these actions when a DC or modulated voltage is applied to one or all of them with the same or different amplitudes, with the same or different frequencies or phases which are all optimized to realize the best performance through changes in the acceleration and/or ionization regions. In addition, the applied voltage to the main electrodes may also be modulated.
Not Available
Gamma-ray emission from special nuclear material (SNM) is relatively easy to shield from detection using modest amounts of high-Z material. In contrast, fast-neutrons are much more penetrating and can escape relatively thick high-Z shielding without losing significant energy. Furthermore, fast neutrons provide a clear and unambiguous signature of the presence of SNM with few competing natural background sources. The challenge of detecting fast neutrons is twofold. First, the neutron flux from SNM are only a fraction of the corresponding gamma-ray flux. Second, fast neutrons can be difficult to differentiate from gamma rays. The ability to discriminate gamma rays from neutrons, combined with a neutron imaging capability, can in some situations enable the localization of SNM neutron sources distinct from background. With the developments of pulse-shape-sensitive plastic scintillators that offer excellent gamma-ray/neutron discrimination, and arrays of silicon photomultipliers combined with highly scalable and fast positron-emission-tomography (PET) multi-channel readout systems, field-deployable neutron imagers suitable for SNM detection might now be within reach. In this paper, we present a characterization of the performance of a recently available commercial PET-scanner readout, including its sensitivity to pulse-shape differences between fast neutrons and gamma rays, energy and timing resolution. Here, we find that, while the pulse-shape discrimination is achievable with stilbene, further improvement of the readout is required to achieve it with the PSD-capable available plastic scintillators.