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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fast Cloud Droplet Probe (FCDP) Instrument Handbook

The fast cloud droplet probe (FCDP) is a U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility instrument for measuring the concentration and size of cloud droplets in the diameter size range 1.5-50 μm. It is an open-path instrument mounted on the outside of the aircraft (typically below the wing). When a droplet passes through the sample volume, light from a laser source is scattered, which is used to detect and size the droplet. The sample volume is a focused area of the laser beam that is continuously monitored along the flight path. If a droplet crosses the laser beam but is outside the sample volume, a separate detector will reject the droplet. Only single droplets at a time will be detected and sizes measured over one second have to be averaged to obtain a size distribution. When measuring ice clouds, the FCDP will not yield useful information, since the measurement method, which assumes spherical particles to obtain size and scattering, is very different for ice crystals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

High-Volume Precipitation Spectrometer (HVPS) Instrument Handbook

The high-volume precipitation spectrometer (HVPS) is a U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility instrument for measuring hydrometeors larger than 150 μm. It is an open-path instrument located on the outside of the aircraft, typically below the wing. A laser sheet throws a shadowgraph image of the hydrometeor (cloud droplets or ice crystals) in the sample volume on a photodiode array. As the hydrometeor moves through the sample volume, this optical array records the moving shape and a two-dimensional picture can be recorded. At an air speed of 100 m/s, the sample volume is 310 l/s. The recorded images can be analyzed for size distributions, number concentrations, and further cloud properties detailed below. Hydrometeors in the range of 0.3-19.2 mm can be fully recorded at a 150 μm resolution; larger particles can only be sized along the direction of flight.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimization of a traveling wave superconducting radiofrequency cavity for upgrading the International Linear Collider

The standing wave TESLA niobium-based superconducting radio frequency structure is limited to an accelerating gradient of about 50 MV/m by the critical rf magnetic field. To break through this barrier, we explore the option of niobium-based traveling wave (TW) structures. Optimization of TW structures was done considering experimentally known limiting electric and magnetic fields. It is shown that a TW structure can have an accelerating gradient above 70 MeV/m that is about 1.5 times higher than contemporary standing wave structures with the same critical magnetic field. The other benefit of TW structures shown is R/Q about 2 times higher than the TESLA structure that reduces the dynamic heat load by a factor of 2. A method is proposed how to make TW structures multipactor-free. Some design proposals are offered to facilitate fabrication. Further increase of the real-estate gradient (equivalent to 80 MV/m active gradient) is also possible by increasing the length of the accelerating structure because of higher group velocity and cell-to-cell coupling. Realization of this work opens paths to International Linear Collider energy upgrades beyond 1 to 3 TeV in competition with CLIC. The paper will discuss corresponding opportunities and challenges.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Frequency Comb-Based Remote Methane Observation Network (Final Scientific/Technical Report)

ARPA-E investment in long-range, frequency comb-based methane detection has resulted in the first scalable, cost-effective continuous methane emissions system. At the onset of this ARPA-E MONITOR award, the dual-frequency comb spectrometer was a sprawling, expensive, laboratory-confined device which had only recently been demonstrated capable of measuring atmospheric trace gases over open-paths. Bringing the technology to the point of being able to monitor for and characterize methane emissions at oil and natural gas production facilities seemed an almost impossible task. Over the subsequent months and years, our team transitioned the Nobel-prize-winning frequency-comb-laser spectrometer technology to a field-deployed regional methane leak detection system. The spectrometer enables highly sensitive near-infrared absorption measurements of methane along kilometer-scale laser beam paths. The measurements are coupled with an atmospheric modeling and inversion framework to triangulate the location of emission sources and quantify the emission rates.

03 NATURAL GAS↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River watershed during SPLASH, 2021–2023

Abstract. From autumn 2021 through summer 2023, scientists from the National Oceanic and Atmospheric Administration (NOAA) and partners conducted the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in the East River watershed of Colorado. One objective of SPLASH was to observe the transfer of energy between the atmosphere and the surface, which was done at several locations. Two remote sites were chosen that did not have access to power utilities. These were along the valley floor near the East River in the vicinity of the unincorporated town of Gothic, Colorado. Energy balance measurements were made at these locations using autonomous, single-level flux towers referred to as atmospheric surface flux stations (ASFSs). The ASFSs were deployed on 28 September 2021 at the Kettle Ponds Annex site and on 12 October 2021 at the Avery Picnic site and operated until 19 July and 21 June 2023, respectively. Measurements included basic meteorology; upward and downward longwave and shortwave radiative fluxes and subsurface conductive flux, each at 1 min resolution; 3-D winds from a sonic anemometer and H2O/CO2 from an open-path gas analyzer, both at 20 Hz from which sensible, latent heat, and CO2 fluxes were derived; and profiles of soil properties in the upper 0.5 m (both sites) and temperature profiles through the snow (at Avery Picnic), each reported between 10 min and 6 h. The system uptime was 97 % (Kettle Ponds) and 90 % (Avery Picnic), and collectively 1184 d of data was obtained between the stations. The purpose of this article is to document the ASFS deployment at SPLASH, to document the data acquisition and post-processing of measurements, and to serve as a guide for interested users of the data sets, which are archived at Zenodo (https://doi.org/10.5281/zenodo.10313363, Cox et al., 2023b; https://doi.org/10.5281/zenodo.10327409, Cox et al., 2023c; https://doi.org/10.5281/zenodo.10313894, Cox et al., 2023d; https://doi.org/10.5281/zenodo.10307825, Cox et al., 2023e; https://doi.org/10.5281/zenodo.10310520, Cox et al., 2023f) with the Creative Commons Attribution 4.0 International license.

Cox, Christopher J. (ORCID:0000000322037173)↗
DOE OSTI2025

Vertical column dual-comb spectroscopy to a TBS

Open-path dual-frequency-comb spectroscopy (DCS) is a broadband, high spectral resolution, and high precision method for measuring gas concentrations over kilometer-scale paths. It has been used for detection and quantification of emissions of pollutants, hazardous gasses, and greenhouse gasses (GHGs). DCS has been shown to measure trace-gas mixing ratios with 0.14%-0.4% agreement between instruments. To achieve high signal-to-noise ratios (SNRs) with DCS, comb light is targeted onto a retroreflector at the end of the measurement path, which returns the signal to a detector co-located with the launch signal. Installing the retroreflector on a mobile platform such as a balloon or unmanned aerial vehicle (UAV) extends the capabilities of DCS by enabling variable path lengths, greater mobility, and access to higher altitudes. Mobile-target DCS has many uses in plume and leak detection, emissions modeling, and planetary boundary layer (PBL) studies. It is a promising method for observing vertical distributions of GHGs and mixing processes in the PBL, which are difficult to measure but important for pollution and climate monitoring as well as for understanding transport of gasses through the atmosphere. Tethered balloons are an intriguing platform because they enable longer flight durations and higher altitudes than easily obtainable with a UAV and thus allow for column measurements up to and above the PBL. New measurements completed in October/November 2024 reach the highest altitudes above ground level yet achieved by mobile-target DCS. For these measurements, the retroreflector was mounted on a 7 m diameter tethered helium balloon. An actively tracking gimbal on the ground holds the DCS launch telescope and keeps the 5 cm beam pointed onto the retroreflector while the balloon is lifted, lowered, and moved by wind and turbulence.

atmosphere↗
DOE OSTI2023

8. Open-Loop Toolpath Generation

Open-loop paths, as opposed to closed-loop paths, have start and end points that are at different locations. Open-loop paths, commonly called “skeletons,” can be generated through multiple approaches. Most approaches utilize a shape's medial axis transform and build appropriate pathing around this representation. In this chapter, a specific implementation consisting of three major steps will be discussed. These steps are Voronoi diagram generation, skeleton pruning, and path consolidation. With the major components of the implementation covered, two additional optimizations will also be described. These two optimizations, chamfering and smoothing, will be highlighted as ways to improve overall pathing quality.

Borish, Michael↗
DOE OSTI2021

Sustained Oxygen Spectral Gaps and Their Dynamic Evolution in the Inner Magnetosphere

Van Allen Probes observations of ion spectra often show a sustained gap within a very narrow energy range throughout the full orbit. To understand their formation mechanism, we statistically investigate the characteristics of the narrow gaps for oxygen ions and find that they are most frequently observed near the noon sector with a peak occurrence rate of over 30%. The magnetic moment (μ) of the oxygen ions in the gap shows a strong dependence on magnetic local time (MLT), with higher and lower μ values in the morning and afternoon sectors, respectively. Moreover, we find through superposed epoch analysis that the gap formation also depends on geomagnetic conditions. Those gaps formed at lower magnetic moments (μ < 3,000 keV/G) are associated with stable convection electric fields, which enable magnetospheric ions to follow a steady drift pattern that facilitates the gap formation by corotational drift resonance. On the other hand, gaps with higher μ values are statistically preceded by a gradual increase of geomagnetic activity. Here, we suggest that ions within the gap were originally located inside the Alfven layer following closed drift paths, before they were transitioned into open drift paths as the convection electric field was enhanced. The sunward drift of these ions, with very low fluxes, forms a drainage void in the dayside magnetosphere manifested as the sustained gap in the oxygen spectrum. This scenario is supported by particle-tracing simulations, which reproduce most of the observed characteristics and therefore provide new insights into inner magnetospheric dynamics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Machine learning enabled discovery of application dependent design principles for two-dimensional materials

Abstract The unique electronic and mechanical properties of two-dimensional (2D) materials make them promising next-generation candidates for a variety of applications. Large-scale searches for high-performing 2D materials are limited to calculating descriptors with computationally demanding first-principles density functional theory. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity and train an ensemble of models to predict thermodynamic, mechanical and electronic properties. We carry out a screening of nearly 45,000 structures for two separate applications: mechanical strength and photovoltaics. By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the structures surveyed. Our approach recovers some well-accepted design rules: hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications and titanium based MXenes usually have high stiffness coefficients. Interestingly, other members of the group 4 elements also contribute to increasing the mechanical strength of MXenes. For all-inorganic perovskites, we discover some compositions that have not been deeply studied in the field of photovoltaics and thus open up paths for further investigation. We open-source the code-base to spur further development in this space.

Venturi, Victor (ORCID:0000000156727549)↗
DOE OSTI2023

A novel approach for adaptive skeleton toolpath generation

Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing. In large-format printing, parts are often designed to be an even number of beads wide to produce a completely dense part. However, voids can still arise. This is often due to the part not being an even number of bead widths wide in some areas, or in geometry containing acute angles, as the process of generating closed contours cannot completely fill the space. Voids can be tolerated in smaller models, but in large-format additive manufacturing they may cause mechanical defects. To fill these voids, open loop paths called skeletons are often used, but they are typically limited by the physical constraints defined in the slicing software. To address this, researchers at Oak Ridge National Laboratory have extended skeleton toolpaths via an adaptive methodology. These adaptive skeletons were found to better fill void spaces through manipulation of physical parameters of the build process and were calculated as part of the slicing process.

42 ENGINEERING↗
DOE OSTI2021

Compensated Contacts for Three-Terminal Transistor Solar Cells

The heterojunction bipolar transistor solar cell (HBTSC) is an alternative to conventional double-junction solar cells that combines the advantages of three-terminal architectures with a compact design. A critical part of the HBTSC fabrication is the implementation of the middle contact, the one associated to the base layer in the transistor structure, because this layer can be damaged by the diffusion of metallic species during contact annealing. As a solution we propose to implement a "compensated contact", that is, to leave a thin (< 200 nm) portion of emitter or collector between the base layer and the contact metals and compensate its doping during contact annealing. Using an Au/Zn/Au metal structure to compensate a 150 nm thick portion of the collector, we obtain a specific contact resistance ~8 10-4 ..omega.. cm2. This value is fairly invariant for annealing temperatures between 400 and 460 degrees C and annealing times between 2 and 5 minutes, and it does not improve by adding Pd to the metallic stack. Optimizing the compensated contact technology opens the path to the realization of III-V-material-based HBTSCs with open-circuit voltages and fill factors comparable to those of conventional multijunction architectures.

contact resistance↗
DOE OSTI2025

Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site

Marine boundary layer clouds are crucial in Earth's climate system. They frequently manifest as closed or open cell mesoscale cellular convection (MCC). MCC clouds are challenging to represent accurately in current climate models, highlighting the need for detailed observational data sets and in-depth analyses. This study utilizes over 8 years of observations from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility Eastern North Atlantic (ENA) site at Graciosa Island, Azores, to investigate these clouds. We first apply a convolutional neural network with a U-Net architecture to classify open and closed cells, marking the first application of such an approach for automatically detecting MCC patterns from ground-based radar measurements. This method addresses some observational gaps in satellite data related to low temporal resolution, nighttime challenges, and limited vertical structure capture. The analysis of the MCC cases shows clear differences between closed and open MCCs: Closed MCC clouds are characterized by lower cloud tops and bases, shallower cloud geometrical depth, weaker horizontal wind speeds, stronger atmospheric stability, and a more homogeneous liquid water path than open MCCs. Finally, we demonstrate two potential applications of our radar-based MCC classifications: (a) facilitating the investigation of aerosol-cloud interactions and (b) exploring meteorological factors along with MCC's evolution by integrating satellite imagery and back-trajectory analysis. The identified MCC cases offer a valuable resource for the scientific community to study MCC processes further and improve climate model accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ab initio translationally invariant nucleon-nucleus optical potentials

We combine the ab initio symmetry-adapted no-core shell model (SA-NCSM) with the single-particle Green's function approach to construct optical potentials rooted in first principles. Specifically, we show that total cross sections and phase shifts for neutron elastic scattering from a 4 He target with projectile energies between 0.5 and 10 MeV closely reproduce the experiment. In addition, we discuss an important new development that resolves a long-standing issue with spurious center-of-mass motion in the Green's function formalism for many-body approaches. Furthermore, the new development opens a path for first-principle predictions of cross sections for elastic scattering of single-nucleon projectiles, nucleon capture, and deuteron breakup reactions, feasible for a broad range of open-shell spherical and deformed nuclei in the SA-NCSM approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Plasma Photocathodes

Abstract Plasma wakefield accelerators offer accelerating and focusing electric fields three to four orders of magnitude larger than state‐of‐the‐art radiofrequency cavity‐based accelerators. Plasma photocathodes can release ultracold electron populations within such plasma waves and thus open a path toward tunable production of well‐defined, compact electron beams with normalized emittance and brightness many orders of magnitude better than state‐of‐the‐art. Such beams will have far‐reaching impact for applications such as light sources, but also open up new vistas on high energy and high field physics. This paper reviews the innovation of plasma photocathodes, and reports on the experimental progress, challenges, and future prospects of the approach. Details of the proof‐of‐concept demonstration of a plasma photocathode in 90° geometry at SLAC FACET within the E‐210: Trojan Horse program are described. Using this experience, alongside theoretical and simulation‐supported advances, an outlook is given on future realizations of plasma photocathodes such as the upcoming E‐310: Trojan Horse‐II program at FACET‐II with prospects toward excellent witness beam parameter quality, tunability, and stability. Future installations of plasma photocathodes also at compact, hybrid plasma wakefield accelerators, will then boost capacities and open up novel capabilities for experiments at the forefront of interaction of high brightness electron and photon beams.

Habib, Ahmad Fahim↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Many-Body Majorana Braiding without an Exponential Hilbert Space

Qubits built out of Majorana zero modes constitute the primary path toward topologically protected quantum computing. Simulating the braiding process of multiple Majorana zero modes corresponds to the quantum dynamics of a superconducting many-body system. It is crucial to study the Majorana dynamics both in the presence of all other quasiparticles and for reasonably large system sizes. We present a method to calculate arbitrary many-body wave functions as well as their expectation values, correlators, and overlaps from time evolved single-particle states of a superconductor, allowing for significantly larger system sizes. We calculate the fidelity, transition probabilities, and joint parities of Majorana pairs to track the quality of the braiding process. Here, we show how the braiding success depends on the speed of the braid. Moreover, we demonstrate the topological CNOT two-qubit gate as an example of two-qubit entanglement. Our Letter opens the path to test and analyze the many theoretical implementations of Majorana qubits. Moreover, this method can be used to study the dynamics of any noninteracting superconductor.

Majorana bound states↗