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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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Joint Polar Satellite System (JPSS) Ground Segment Support to the GOSAT-3 Mission introduction, mission parameters, assumptions and constraints.
A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that multimodal textures cannot be well represented by a single Gaussian cluster.
Highly efficient, segmented thermoelectric unicouple incorporating advanced thermoelectric materials with superior thermoelectric figures of merit are currently being developed at the Jet Propulsion Laboratory (JPL).
A new version of a segmented thermoelectric unicouple incorporating advanced thermoelectric materials with superior thermoelectric figures of merit has been recently proposed and is currently being developed at the Jet Propulsion Laboratory.
Coronagraphs (together with starshades) are an important tool to directly image and characterize exoplanets, and enable the search for biomarkers in reflected light on potentially habitable worlds. Their performance and efficiency has steadily been improving over the past several decades, but has not yet reached performance limits. In particular, the expected exoplanet yield for missions such as the Astro2020-recommended “IR/O/UV Flagship” can still be improved by factors of at least 2-3, simply by continued improvements in coronagraph performance, before they plateau due to physics limits. One possible architecture that can enable at least a part of this improvement is the Phase-Induced Amplitude Apodization Complex Mask Coronagraph (PIAACMC). Itoffersthe advantages of high throughput, small inner working angle (IWA),and almost noloss inPSF sharpness, and natively supports obstructed and segmented apertures, which is essential to the Astro2020 “IR/O/UV Flagship”mission. Historically, key disadvantages of PIAA have been poor tolerance to stellar angular sizes and maturity, but latest designs and demonstrations have made significant strides in this respect.In this paper, we present the current status and overview of our program to mature the PIAACMC technology. We first review PIAACMC designs for LUVOIR-A and B, which resultedin improved expected yield of Exo-Earths relative tothe baselines for both mission concepts. In particular, for LUVOIR-B, the yield improves from 28 to 42due to improvedtolerance to stellar angular size in our design. The improvement in yield is marginal for LUVOIR-A, but our design enables detecting planets around smaller diameter stars (nearby K-dwarfs and/or farther FG stars). We also describe our modeling and vacuum demonstrationsfor the LUVOIR-A aperture(which is more challengingthan LUVOIR-B due to the central obstruction).The demonstration included a LUVOUR-A pupil mask, an on-axis PIAA set of 2 mirrors with central holes, a Boston Micromachines DM, a patterned CMC mask, Lyot stop, and supporting masks and optics. Demonstrations were conducted at JPL’s High Contrast Imaging Testbed (HCIT) using several characterization andwavefront control techniques (primarily standard EFC, several experimental techniques were attempted, as well as speckle nulling). So far, our results include 1.9e-8 raw contrast in 10% broadband light between 3.5 and 8 l/D; 4.1e-8 and 1.6e-9 coherent contrasts in monochromatic light between 2-4 and 4-8 l/D, respectively. We also present measurements and analysis of sensitivity to tip/tilt jitter and stellar angular size. Finally, we compare our test results to models, present an analysis of our limiting factors, and explorefuture prospectsfor improvement based on validated models.
Abstract Wind turbine design encompasses many different aspects including aerodynamic, structural, electrical, and control system design. To achieve optimal plant performance, a system design approach is utilized in which the performance of the whole wind turbine is evaluated and quantified during operational scenarios with subsystem interactions. In this paper, the design for a Segmented Ultralight Morphing Rotor (SUMR) 50‐MW wind turbine is presented utilizing levelized cost of energy (LCOE) for design choices, with additional quantification of simulated performance shortcomings at the 50‐MW scale. The multi‐disciplinary design process results in a final ultra‐scale turbine configuration that outperforms other existing offshore wind farms regarding the LCOE.
Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at https://github.com/vitorgrizzi/nugraph_phys/tree/main_phys.
Here, the present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density.
Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.
In addition to its ability to produce geometrically complex parts, additive manufacturing offers a unique opportunity to collect data about a component while it is being fabricated. However, there has only been limited effort to characterize parts morphologically and compositionally in situ. In this article, we present a layer-by-layer, laser profilometry-based in situ characterization technique as a method to digitally reconstruct a multi-material part. Data collected by the laser profilometer yields height maps and grayscale images which are voxelized using purpose-built software to volumetrically reconstruct the part. Additionally, the same part was also analyzed using X-ray computed tomography (CT) which was not able to resolve the different compositional regions within the part, but captured the filament morphology. The part was then bisected to compare the digital reconstruction to the actual part morphology and composition. Overall, the digital reconstruction was in good agreement with both the CT and bisected images. Deviations between the digital reconstruction and the CT/bisected images are likely the result of image segmentation settings or material shifts after data was collected. The in situ characterization method demonstrated here sets the stage for real time process monitoring and paves the way for additively manufactured parts that are “born qualified.”
Developing high-performance wide bandgap polymer donors to match the rapidly growing non-fullerene small molecule acceptors (NF-SMAs) is of great importance to further improve the photovoltaic performances of organic solar cells (OSCs). PM6 and D18 are two leading wide bandgap donor polymers, but their performances are highly sensitive to the molecular weights, which have distinct impact on the morphology and thereby the charge generation and recombination processes. Here, in this work, we reported the random ternary copolymerization of PM6 and D18 segments to develop random terpolymers with finely tuned photoelectronic properties and optimized blend morphology for high-performance OSCs. The resulting random terpolymers (PMD-5, PMD-10, PMD-15 and PMD-20) exhibited finely tuned absorptions, energy levels and blend morphology. The optimal morphology of PMD-15:L8-BO contributed to the fast exciton diffusion and dissociation, efficient charge extraction with suppressed recombination. Thus, the power conversion efficiency was improved from 17.22% of the control PM6 devices and 17.47% of the control D18 devices to 18.12% of PMD-15 based devices, which is the top performance of random terpolymers. Moreover, the PMD-15 exhibited much higher batch-to-batch reproducibility at a broad molecular weight range than that of PM6 and D18, showing promising for practical applications.
Large-scale deployment of Carbon Capture and Storage (CCS) will require a commensurately large number of sites. Efficient screening methods are needed to create investment assurance and focus efforts on the most promising sites. The problem is similar to petroleum exploration, for which there are well-developed (though seldom published) workflows, including Common Risk Segment (CRS) mapping. In brief, the process requires 1) defining the key play elements; 2) identifying candidate geologic intervals for each; 3) creating fact-based maps for those intervals; 4) determining minimum criteria for the success of each element; 5) reinterpreting the fact-based maps in terms of chance of success; and 6) combining the individual maps to form a composite, basin-scale view of prospectivity. We adapt the CRS process to screening for CO 2 storage sites. Critically, we redefine the process in terms of cost of characterization and development, rather than chance of success. For illustration, we apply the process to the example of the Lower Miocene on the Texas and Louisiana Gulf Coast. We show that the predictions are consistent with historic hydrocarbon production volumes and rates. The power of the CRS method is that it creates a systematic approach to geologic evaluation and translates complex, multidimensional analysis into clear, graphical and easily comprehended business inputs. The results highlight sweet spots and identifies critical risks, suggesting a focus for further data collection and analysis. Furthermore, the method developed here can be applied to both surface and subsurface factors anywhere that there is interest in geologic storage of CO 2 .
This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer.
Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.
Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.
The study of neutron-unbound systems using invariant mass spectroscopy is often performed using low-intensity radioactive ion beams. Low reaction yields can be countered by using thick targets but at the expense of larger uncertainties in the reconstructed invariant mass. In this work, we present a new segmented target designed to address this trade-off. It is composed of three mm thick passive beryllium targets interleaved between four thick position sensitive silicon detectors. In the first experiment to use this new system the half-life of two-neutron unbound was measured to be (stat) (syst) ps, which agrees with a previous measurement made by the MoNA Collaboration.
The magnetized near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrino–nucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm 3 cubes. The SuperFGD cube unit shows promising optical performance, including a high light yield of about 40 photoelectrons (p.e.) per channel, a low cube-to-cube crosstalk rate below 3%, and a sub-nanosecond time resolution of 0.96 ns. By combining tracking and stopping power measurements of final state particles, this novel detector enables precise 3D-imaging of GeV neutrino interactions with reduced systematic uncertainties. A detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported.
Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.