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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

First observations from the Kr multi-monochromatic x-ray imager for time and spatially resolved diagnosis of hot implosion cores

This paper presents initial findings from the recently deployed Kr multi-monochromatic x-ray imager (Kr MMI) at the Omega laser facility. The experiment focuses on exploring implosion dynamics in exploding pusher capsules at three distinct initial gas fill densities. Utilizing time-gated and spatially integrated measurements, core size, electron temperature (Te), and electron densities (ne) are extracted through the analysis of the spectral region encompassing the Kr He α and its satellite lines. A comprehensive spectral database, incorporating atomic kinetics, spectroscopic-quality radiation transport, and Stark broadened line shapes, has been developed for rigorous data analysis. These measurements underscore the utility of the new Kr MMI instrument, which, combined with sophisticated analysis techniques, enables the diagnosis of plasma conditions at Te>2000 eV, thereby extending the capabilities beyond the prior Ar MMI design. This is an important stepping stone for achieving time-gated and space-resolved diagnostics of electron temperature, electron density, and heat transport in high temperature implosion cores.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Topological and Dynamical Representations for Radio Frequency Signal Classification

Radio Frequency (RF) signals are found throughout our world, carrying over-the-air information for both digital and analog uses with applications ranging from WiFi to the radio. One area of focus in RF signal analysis is determining the modulation schemes employed in these signals which is crucial in many RF signal processing domains from secure communication to spectrum monitoring. This work investigates the accuracy and noise robustness of novel Topological Data Analysis (TDA) and dynamic representation based approaches paired with a small convolution neural network for RF signal modulation classification with a comparison to state-of-the-art deep neural network approaches. We show that using TDA tools, like Vietoris-Rips and lower star filtrations, and the Takens' embedding in conjunction with a standard shallow neural network we can capture the intrinsic dynamical, geometric, and topological features of the underlying signal's manifold, offering informative representations of the RF signals. Our approach is effective in handling the modulation classification task and is notably noise robust, outperforming the commonly used deep neural network approaches in mode classification. Moreover, our fusion of dynamical and topological information is able to attain similar performance to deep neural network architectures with significantly smaller training datasets.

Myers, Audun D.↗
DOE OSTI2024

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. Here, this paper introduces “molecular hypernetworks” (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Spectrograph stabilization using a single-delay interferometer on the Hale Telescope

We describe a technique for spectrograph stabilization useful when conventional mitigation techniques of vacuum tanks, thermal insulation, and laser frequency comb may be impractical, expensive, heavy, or bulky. This includes spectrographs on airborne platforms or mounted on telescopes where they suffer a changing gravity vector or other drifts. Placing a fixed-delay interferometer in series with a spectrograph forms an externally dispersed interferometer (EDI). This produces a uniform sinusoidal comb multiplying input spectrum, creating (through heterodyning) beats (moiré patterns). In Fourier space for low frequencies up to the comb frequency, the moiré generated signal counter-rotates to ordinary spectra under an unknown disperser wavenumber drift Δx. This generates a large negative feedback signal useful in a conceptual control loop, to converge rapidly to a stable spectrum and yield Δx. A modified EDI data analysis algorithm (“crossfading”) combines frequency-weighted moiré with conventional spectrum to cancel net output spectrum reaction to Δx. Needing only a single-delay, this is a practical improvement over prior crossfading analyses requiring multiple delays. We test crossfading on ThAr data near 4850 cm−1 taken on Hale telescope in an earlier project. In a single pass, we reduce drift 20 times. Using seven iterations, we reduce 0.5 cm−1 (31 km/s Doppler equivalent) drift to 4×10−7 cm−1 (2.5 cm/s). The interferometer delay can wander, because linearity of phase versus wavenumber interpolates science features between bracketing calibrating spectral references. Second, mathematically reversing the heterodyning effect doubles effective spectral resolution without changing disperser slit.

Erskine, David J [Lawrence Livermore National Labo↗
DOE OSTI2024

Elucidating Photoinduced Processes of Photosystem I Via Multidimensional Electronic and Vibrational Spectroscopies

This project was motivated by an overarching goal to elucidate the mechanism of energy and electron transfer that governs the efficient charge separation in photosystem I (PSI) complexes. PSI is a natural light harvesting complex that drives oxygenic photosynthesis in plants, algae, and cyanobacteria. It uses ~300 tightly packed chlorophylls (Chls) to absorb photons, transfer the excitation energy to the reaction center (RC), and generate a charge separated state with near unity quantum efficiency (QE). A better understanding of the mechanism of energy transfer and charge separation in PSI is required for understanding the high QE of natural light harvesting complexes, and it could lead to the further development of artificial photosynthetic systems for solar energy conversion and modification of light harvesting complexes to improve crop yields. We applied two-dimensional optical spectroscopies to different cyanobacterial photosystem I complexes, including PSI complexes that contain Chl f molecules, to map energy transfer pathways and gain insight into the efficient light harvesting of PSI. We used two-dimensional electronic spectroscopies (2DES) to map energy transfer in Chl a and Chl f containing PSI complexes. To investigate the Chl f PSI complexes, we modified our spectrometer to probe the lower energy states associated with Chl f molecules. We interpreted the 2DES spectra through global analysis procedures to generate maps of energy transfer. We also constructed a two-dimensional electronic vibrational (2DEV) spectrometer that will be used to investigate charge transfer transitions and dynamics within PSI complexes. Measurements were performed on model systems to establish general data analysis procedures for interpreting 2D spectra and gain insight into protein cofactor interactions.

14 SOLAR ENERGY↗
DOE OSTI2025

Glass formation during combinatorial sputtering in binary alloys

Glass formation is a complex phenomenon influenced by thermodynamic and kinetic aspects, which are often controlled by extrinsic contributions. While bulk metallic glasses are typically multicomponent alloys, binary alloys offer a simplified approach to studying glass formation. In this study, we fabricated 57 binary alloy systems through combinatorial sputtering, where each alloy system is represented in 66 different alloys. We developed an automated analysis to determine structure and composition using X-ray diffraction and energy-dispersive X-ray spectroscopy for over 3700 alloys. We found that ∼17 % of the alloys form glasses under the estimated cooling rate during sputtering of ∼10 8 K/s. Data analysis revealed that commonly used factors like atomic size ratio and heat of mixing are ineffective in predicting glass formation. However, the crystal structure mismatch of the alloys’ elements emerged as the strongest indicator of glass formation under sputtering conditions of binary alloys. Here, the differences in glass formation under slow cooling rates used for bulk glass formation and the here observed glass formation under rapid cooling rates are discussed.

Binary alloys↗
DOE OSTI2025

The infrared imaging video bolometer at Wendelstein 7-X

The radiated power distribution is a crucial aspect of heat transport, heat load mitigation, and plasma exhaust performance. An imaging video bolometer camera has been installed to measure the plasma radiated power in the divertor region of the Wendelstein 7-X (W7-X) stellarator. This diagnostic offers a wide-angle (40° × 68°) sampling of the plasma volume in both the poloidal and toroidal directions. The field-of-view is covered with a large number (> 500) of bolometer channels, providing imaging capability. The diagnostic design is introduced here together with its data analysis procedure. A set of laboratory experiments is performed to assess the thermal properties of the gold absorber foil and their spatial uniformity. Following installation, a heat source originating from the inertially cooled front of the diagnostic is identified and filtered out. The discharge data indicate a satisfactory signal-to-noise ratio as well as spatiotemporal resolution. These represent the first toroidally resolved images of the line-integrated radiated power in the W7-X island divertor. The diagnostic was then upgraded with a thinner platinum absorber and adjusted mirrors. Early data from the most recent experimental campaign employing the upgraded design show a considerable improvement in the diagnostic performance with more bolometer channels (> 1400), extended coverage, and increased spatial resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Coherent diffraction imaging in the undergraduate laboratory

We present an undergraduate optics instructional laboratory designed to teach skills relevant to a broad range of modern scientific and technical careers. In this laboratory project, students image a custom aperture using coherent diffraction imaging, while learning principles and skills related to digital image processing and computational imaging, including multidimensional Fourier analysis, iterative phase retrieval, noise reduction, finite dynamic range, and sampling considerations. After briefly reviewing these imaging principles, we describe the required experimental materials and setup for this project. Our experimental apparatus is both inexpensive and portable, and a software application we developed for interactive data analysis is freely available.

Porter, J. Nicholas↗
DOE OSTI2024

Calibrating LArPix for TinyTPC

LArTPCs provide sensitivity to GeV signals, such as accelerator neutrinos and part of the supernova neutrino spectrum. TinyTPC is a LArTPC test stand for R&D of LAr doping to expand the reach of LArTPCs down to the 1-10 MeV range, which would substantially enhance the flagship analyses of experiments like DUNE, while enabling low energy analyses. We aim to dope LAr with Xe and pho- tosensitive dopants to expand the LArTPC range by converting hard-to-detect scintillation light to efficiently detected ionization charge. A critical element of the data analysis in TinyTPC is calibrating the readout. This poster will cover the calibration of TinyTPC, a pixelated liquid argon detector, where we find the distance a muon travels through each pixel. We then calculate the energy loss of muons traveling through the detector from cosmic data. We can reconstruct the path of the particles through the TPC using a density-based clustering algorithm designed to sort straight cosmic muon tracks from low energy radioactive decay curled paths and electronic noise

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Thermal expansion of LaB 6 from 298 to 998 K

We propose LaB 6 as a temperature calibration standard for high-temperature (HT) X-ray diffractometry owing to its high temperature stability. Such HT applications require a reliable HT lattice parameter or, equivalently, peak position data, which have not been readily accessible to the diffraction community to date. As such, the thermal expansion behavior of NIST SRM 660a LaB6 was assessed in the temperature range 298–998 K using HT Bragg–Brentano parafocusing θ:θ X-ray diffractometry in conjunction with Rietveld analysis. Data were collected in the 2θ range 20–150° at a data collection rate of 0.5° θ min −1 in air and at 1 atm. The temperature was stepped in 50 K increments. The cubic unit-cell lattice parameter [a(T)] of LaB 6 in Å was found to vary as a(T) = 4.15678 (±0.00001) + Ξ(T − 298 K) + Ψ(T − 298 K) 2 , where Ξ = 2.4645 × 10 −5 (±4.8904 × 10 −8 ) Å K −1 and Ψ = 1.0325 × 10 −8 (±6.7376 × 10 −11 ) Å K −1 . The isobaric volume thermal expansion coefficient (TEC) was obtained as α V P = (5.9291 × 10 −6 ) + (4.9680 × 10 −9 )(T − 298 K) K −1 , from which the corresponding linear TEC was obtained as α L P = (1.9764 × 10 −6 ) + (1.6560 × 10 −9 )(T − 298 K) K −1 . The 3 × 3 matrix representations of the single-crystal isobaric linear TEC and the volume expansivity were obtained for the cubic crystal class to which LaB 6 belongs. Also, the temperature dependence of the lattice parameter data of this study was compared with past landmark studies on LaB 6 by Dutchak et al. [Inorg. Mater. (1972), 8, 1877–1880] and Aivazov et al. [Inorg. Mater. (1979), 15, 1015–1016].

LaB6 standard material↗
DOE OSTI2024

Modifying the Asynchronous Jacobi Method for Data Corruption Resilience

Moving scientific computation from high-performance computing (HPC) and cloud computing (CC) environments to devices on the edge, i.e., physically near instruments of interest, has received tremendous interest in recent years. Such edge computing environments can operate on data in situ, offering enticing benefits over data aggregation to HPC and CC facilities that include avoiding costs of transmission, increased data privacy, and real-time data analysis. Because of the inherent unreliability of edge computing environments, new fault-tolerant approaches must be developed before the benefits of edge computing can be realized. Motivated by algorithm-based fault tolerance, a variant of the asynchronous Jacobi (ASJ) method is developed that achieves resilience to data corruption by rejecting solution approximations from neighbor devices according to a bound derived from convergence theory. Numerical results on a two-dimensional Poisson problem show that the new rejection criterion, along with a novel approximation to the shortest path length on which the criterion depends, restores convergence for the ASJ variant in the presence of certain types data corruption. Numerical results are obtained for when the singular values in the analytic bound are approximated. Additional linear systems are also explored, one with a more dense sparsity pattern and one that includes advection. All results indicate that successful resilience to data corruption depends on whether the bound tightens fast enough to reject corrupted data before the iteration evolution deviates significantly from that predicted by the convergence theory defining the bound. This observation generalizes to future work on algorithm-based fault tolerance for other asynchronous algorithms, including upcoming approaches that leverage Krylov subspaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Automated analysis of unlabeled PV data with Solar Data Tools software: Overview and feature updates

Distributed rooftop PV systems: ubiquitous, yet commonly have unlabeled data Difficult or impossible to form a performance index We developed Solar Data Tools (SDT), an open-source Python library for analyzing PV power (and irradiance) time-series data SDT enables analysis of unlabeled PV data—no model, no meteorological data, no performance index required Takes a statistical signal processing approach Data processing steps are largely pre-defined and automatic regardless of system type—from utility tracking systems to multi-pitch rooftop systems

Meyers-Im, Bennet E↗
DOE OSTI2025

dCache: The Storage System of Choice for Data-Intensive Applications

The ever-increasing volumes of data produced by modern scientific facilities like EuXFEL and LHC put significant stress on data management infrastructure operated by laboratories and research centers. The challenges to be addressed span the entire data life cycle, from ingest and efficient data analysis to long-term preservation, typically involving large tape libraries. dCache, a storage system developed in collaboration between the Deutsches Elektronen-Synchrotron (DESY), Fermi National Accelerator Laboratory, and Nordic e-Infrastructure Collaboration (NeIC), is designed to manage a large number of disk servers and to facilitate transparent data migration to and from archival storage. Its multifaceted approach offers a unified method to support a variety of scientific use cases with the same storage infrastructure, including high-throughput data ingest, data sharing over wide area networks, efficient access from HPC clusters, and long-term data preservation on tertiary storage. Initially developed for high energy physics (HEP) experiments, dCache is now used by various scientific communities, including astrophysics, biomedical research, and life sciences, each having specific requirements. This paper presents architecture, deployment strategies, performance and scalability enhancements, and recent advancements in dCache addressing the needs of scientific communities. Finally, we touch on the development and release process, ensuring the software’s high quality.

DCache↗
DOE OSTI2024

Probabilistic neural networks for improved analyses with phenomenological R -matrix

Here we present a method for measurement analyses based on probabilistic deep neural networks that provide several advantages over conventional analyses with phenomenological models. These include predicting physical quantities directly from data, the rapid generation of statistically robust uncertainties, and the ability to bypass some parameters that may induce ambiguities and complications in data analysis. As deep learning methods make predictions through “black boxes,” the uncertainty quantification is typically challenging. We use a probabilistic framework that provides thorough uncertainty quantification and is straightforward to follow in practice. With the network architecture based on the Transformer, we demonstrate the current method for predicting nuclear resonance parameters from scattering data using the phenomenological R-matrix model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Quantile regression-enriched event modeling framework for dropout analysis in high-temperature superconductor manufacturing

High-temperature superconductor (HTS) tapes have shown promising characteristics of high critical current, which are prerequisites for applications in high-field magnets. Due to the unstable growth conditions in the HTS manufacturing process, however, the frequent occurrences of dropouts in the critical current impede the consistent performance of HTS tapes. To manufacture HTS tapes with large scale, high yield, and uniform performance, it is essential to develop novel data analysis approaches for modeling the dropouts and identifying the related important process parameters. Conventional methods for modeling recurrent events, such as the point process, require the extraction of events from quality measurements. As the critical current is a continuous process, it may not comprehensively represent the drop patterns by transforming the time-series measurements into a set of events. Here, to solve this issue, we develop a novel quantile regression-enriched event modeling (QREM) framework that integrates the non-homogeneous Poisson process for modeling the occurrence of dropouts and the quantile regression for capturing the drop patterns. By incorporating the feature selection and regularization, the proposed framework identifies a set of significant process parameters that can potentially cause the dropouts of HTS tapes. The proposed method is tested on real HTS tapes produced using an advanced manufacturing process, successfully identifying important parameters that influence dropout events including the substrate temperature and voltage. The results demonstrate that the proposed QREM method outperforms the standard point process in predicting the occurrence of dropouts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Wearable Technology and Its Influence on Motor Development and Biomechanical Analysis

The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.

Morouço, Pedro (ORCID:0000000259569790)↗
DOE OSTI2026

TEMPEST3 surface runoff water chemistry and organic matter composition

Coastal flooding, driven by storm surges and sea level rise, can mobilize organic matter (OM) via runoff, while introducing compositionally distinct OM (e.g., estuarine OM) into the system. To understand event-scale OM dynamics, we monitored source waters and surface runoff during an ecosystem-scale field manipulation experiment, TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments), in June 2024. The TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales – Field, Measurements, and Experiments) project and designed to investigate biogeochemical and ecological impacts of freshwater and seawater flooding on coastal terrestrial-aquatic interface ecosystems by simulating freshwater and seawater storm events in two 2000m2 coastal upland forest plots (freshwater and brackish seawater plots). The temporal coverage of this dataset is during the TEMPESTⅢ event (June 11-13, 2024). This dataset contains: - Surface runoff discharge measured by flumes - Sensor data (specific conductivity, salinity, dissolved oxygen, and temperature) - Particle size distribution - Total suspended sediment concentrations (TSS), particulate and dissolved organic carbon (POC, DOC) concentrations, total nitrogen and total dissolved nitrogen (TN, TDN) concentrations - Bulk particulate and dissolved OM compositions (stable C and N isotopes of particulates and optical measurements of chromophoric dissolved OM) - High resolution mass spectrometry analysis data - Water isotope data All data files are plain-text CSV (comma-separated value), and no special software is required to read them.

COMPASS-FME↗