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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

The P2 experiment: A future high-precision measurement of the weak mixing angle at low momentum transfer

Here we describe the research and development work for the P 2 experiment which aims for a high precision determination of the weak mixing angle sin 2 θ W to a precision of 0.15% at a four-momentum transfer of 4.5×10 –3 GeV 2 . This accuracy, comparable to existing measurements at the Z pole, allows for a sensitive test of the Standard Model up to a mass scale of 50 TeV, extendable to 60 TeV. The weak mixing angle is connected to the weak charge of the proton which will be extracted from a measurement of the parity violating cross section asymmetry –39.94×10 –9 in elastic electron-proton scattering. A total accuracy of 0.57×10 –9 is achievable in a measurement time of 11000 h using a 150μA polarized electron beam impinging on a 60 cm liquid hydrogen target. The P 2 asymmetry is smaller than any asymmetry measured so far in electron scattering with an unprecedented goal for the accuracy. The use of a solenoid spectrometer with 100% Φ-acceptance as well as an atomic hydrogen trap polarimeter are new features, which have never before been used in parity-violation experiments. In order to collect the enormous statistics required for this measurement, the new Mainz Energy-Recovering Superconducting Accelerator (MESA) is under construction. Plans for the associated beam control system and the polarimetry are described in this article as well. A liquid hydrogen high-power target with an extremely low noise level of 10 ppm needs to be designed and constructed. We report in addition on the conceptual design of the P 2 spectrometer, its Cherenkov detectors, the integrating read-out electronics as well as the ultra-thin, fast tracking detectors. The physics program of the MESA facility comprises indirect, high precision search for physics beyond the Standard Model, measurement of the neutron distribution in nuclear physics, single-spin asymmetries, and a possible future extension to the measurement of hadronic parity violation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Biochemical diversity of betaines in earthworms

Highlights: ► We develop a method for rapid untargetted analysis of betaines. ► We profile betaines in a comparative study of ten earthworm species. ► Earthworms contain a surprisingly high number of different betaine metabolites. ► Earthworms contain betaines normally seen only in plants or marine animals. -- Abstract: The ability to accumulate osmoprotectant compounds, such as betaines, is an important evolutionary feature in many organisms. This is particularly the case for organisms that live in variable environments, which may have fluctuations in moisture and salinity levels. There is, surprisingly, very little known about betaines in soil invertebrates in general, and there is almost no information about earthworms – a group that are important ‘ecosystem engineers’ and key indicators of soil health. Here, we describe a fast and reliable {sup 1}H–{sup 13}C heteronuclear single quantum coherence (HSQC) 2D NMR approach for the metabolic profiling of a series of betaines and related metabolites in tissue extracts, and list {sup 1}H and {sup 13}C chemical shifts for the trimethylammonium signal for 23 such compounds. The analysis of ten different species from three different families (Lumbricidae, Megascolecidae and Glossoscolecidae) showed an unexpected diversity of betaines present in earthworms. In total ten betaines were identified, including hydroxyproline-betaine, proline-betaine, taurine-betaine, GABA-betaine and histidine-betaine, and a further eleven as-yet unassigned putative betaine metabolites detected. The findings clearly indicate a hitherto-unappreciated important role for betaine metabolism in earthworms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

AtomSets as a hierarchical transfer learning framework for small and large materials datasets

Abstract Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (<400) and large structural data (>130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

Chen, Chi (ORCID:0000000180087043)↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A Comprehensive Methodology to Evaluate Losses and Process Variations in Silicon Solar Cell Manufacturing

Novel, high-throughput metrology methods are used in this paper for detailed performance loss analysis of approximately 400 industrial crystalline silicon solar cells, all coming from the same production line. The characterization sequence combines traditional global cell measurements (e.g., current-voltage, Suns-VOC) with camera-based cell imaging of voltage distribution and power dissipation from photoluminescence and lock-in thermography, respectively. Spatial variations in current collection are visualized using a high-speed external quantum efficiency and reflectance measurement technique. A nondestructive transfer length method (TLM) measurement technique is also implemented, featuring circular TLM structures hidden within the busbars of the cells. The variance of individual loss parameters and their impact on cell performance are quantified for this large group of cells. Based on correlations performed across parameters, recombination losses in the bulk and rear surface of the cell are shown to be the primary limiting factor for the cell efficiency. The nature of these distributions and correlations provide important insights about loss mechanisms in industrial solar cells, helping to prioritize efforts for optimizing the performance of the production line. Additionally, many of the parameters extracted from these techniques can be tied back to incoming material quality issues (e.g., poor bulk carrier lifetime, nonuniform wafer doping) and to individual unit processes (e.g., texturing, phosphorus diffusion, silicon nitride deposition, metallization), allowing the data to be used directly for process control in manufacturing. All of the datasets are made available for download.

14 SOLAR ENERGY↗
DOE OSTI2024

Assessment of Machine Learning Wall Modeling Approaches for Large Eddy Simulation of Gas Turbine Film Cooling Flows: An a Priori Study

Here, in this work, a priori analysis of machine learning (ML) strategies is carried out with the goal of data-driven wall modeling for large eddy simulation (LES) of gas turbine film cooling flows. High-fidelity flow datasets are extracted from wall-resolved LES (WRLES) of flow over a flat plate interacting with the coolant flow supplied by a single row of 7-7-7 shaped cooling holes inclined at 30 degrees with the flat plate at different blowing ratios (BR). The WRLES are performed using the high-order Nek5000 spectral element computational fluid dynamics (CFD) solver. Light gradient boosting machine (LightGBM) is employed as the ML algorithm for the data-driven wall model. Parametric tests are conducted to systematically assess the influence of a wide range of input flow features (velocity components, velocity gradients, pressure gradients, and fluid properties) on the accuracy of ML wall model with respect to prediction of wall shear stress. In addition, the use of spatial stencil and time delay is also explored within the ML wall modeling framework. It is shown that features associated with gradients of the streamwise and spanwise velocity components have a major impact on the prediction fidelity of wall model, while the effect of gradients of wall-normal velocity component is found to be negligible. Moreover, adding flow feature information from an x-y-z spatial stencil significantly improves the ML model accuracy and generalizability compared to just using local flow features from the matching location. Overall, highest prediction accuracy is achieved when both spatial stencil and time delay features are incorporated within the data-driven wall modeling paradigm.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $\sqrt{s}=13\hbox{TeV}$ proton–proton collision data

A search for R-parity-violating supersymmetry in final states characterized by high jet multiplicity, at least one isolated light lepton and either zero or at least three b-tagged jets is presented. The search uses 139fb –1 of $\sqrt{s}=13\hbox{TeV}$ proton–proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the b-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine-learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine-learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Stacked-mosaic amplifier and diode delivery concept for kJ-class inertial fusion energy laser driver

We report on a laser amplifier architecture designed as a modular element for scaling to multi-megajoule laser facilities intended for inertial fusion energy (IFE) power plants. This kilojoule-class module features a stacked mosaic of gain media integrated with a diode delivery system for efficient optical pumping of the mosaic-structured gain medium, enabling high-repetition-rate operation at high wall plug efficiencies, necessary for an IFE driver. Details of a diode delivery system capable of pumping the mosaic architecture are presented. Comprehensive numerical modeling demonstrates that the stacked-mosaic approach reduces transverse gain by five orders of magnitude compared to a conventional full-aperture Yb:YAG based amplifier, substantially suppressing transverse amplified spontaneous emission (TASE) and enabling enhanced longitudinal energy extraction. Detailed analysis of thermal management and wavefront distortion in a 4 × 4 mosaic array indicates that temperature gradients and thermally induced aberrations are effectively controlled using gas cooling and commercially available phase-plate and deformable mirror technologies. We further discuss the applicability of a stacked-mosaic architecture to direct-drive IFE schemes, where broad spectral bandwidth is critical, and its compatibility with frequency conversion modules for up-conversion to blue wavelengths. Finally, an example point design for a 10 kJ, 10 Hz Yb:YAG module operating at 175 K with wall-plug efficiency exceeding 10 % is presented, underscoring the feasibility of this approach for next-generation high-energy lasers for IFE drivers. The results establish the stacked-mosaic amplifier as a scalable, robust platform not only for IFE but also for a broad range of advanced scientific and industrial laser applications.

Lasers↗
DOE OSTI2021

A self-floating oscillating surge wave energy converter

Here this paper proposes a new conceptual configuration for oscillating surge wave energy converters. The concept is a self-floating device consisting of two vertical flaps with a common hinge and an averaged density equal to that of water. The two flaps will oscillate in opposite directions when driven by incident waves. Kinetic energy will be extracted from the relative speed between the two flaps by a power take-off embedded at the hinge. Numerical studies reveal that the self-floating device has a smaller resonant period and optimal power take-off damping compared with a bottom-hinged floating device of the same overall dimensions. As long as the power take-off damping is appropriately tuned, the opposite-rotation mechanism is valid in both regular and irregular waves, and no matter whether the hinge is fixed or connected with mooring lines. With the self-floating feature and opposite-rotation mechanism, the proposed concept can potentially benefit deep ocean applications by exerting a significantly less reaction loading on mooring lines and eliminating the need for a supporting frame to house the power take-off. The influence of various design parameters such as flap dimensions, submerged depth, incident wave direction, and water depth is also analyzed for a comprehensive evaluation of the proposed concept.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Shallow Aseismic Slip in the Delaware Basin Determined by Sentinel-1 InSAR

The Delaware Basin, Texas is currently a hot-spot of induced seismicity and ground deformation due to fluid extraction and injection associated with horizontal drilling techniques; however, the driving mechanism behind the seismicity and deformation remains under debate. Here, using vertical and east-west horizontal surface deformation measurements derived from Sentinel-1 interferometric synthetic aperture radar (InSAR), we show that the subsurface responds differently to oil and gas activity in the northern and southeastern portions of the basin. In the north, where there is little seismicity, deformation patterns display long-wavelengths and equidimensional patterns. In contrast, the southeast region hosts most of the seismicity and displays spatial deformation patterns with narrow linear features that strike parallel to the maximum principal horizontal stress and to trends in seismicity, suggesting movement along normal faults. We model a linear deformation feature using edge dislocations and show that the InSAR observations can be reproduced by slip on normal faults contained within the Delaware Mountain Group (DMG), the formation that hosts local wastewater injection and the majority of earthquakes. Our model consists of three parallel, high-angle normal faults, with two dipping toward one another in a graben structure. Slip magnitudes reach up to 25 cm and are spatially correlated with injection wells. Measured seismicity can only explain ~2% of the fault motion predicted by our fault model, suggesting that slip leading to the deformation is predominantly aseismic. We conclude that seismic and aseismic fault motion in the southeastern Delaware Basin is likely driven by wastewater injection near critically-stressed normal faults within the DMG.

58 GEOSCIENCES↗
DOE OSTI2022

Extracting forces from noisy dynamics in dusty plasmas

Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning (ML) represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised ML to extract the electrostatic, dissipative, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma (dusty plasma). By tracking the sub-pixel motion of particles in subsequent images, we successfully estimated these forces from their random motion. The experiments contained important sources of non-Gaussian noise, such as drift and pixel-locking, representing a data mismatch from methods used to analyze simulated data with purely Gaussian noise. Our model was trained on simulated particle trajectories that included all of these artifacts, and used more than 100 dynamical and statistical features, resulting in a prediction with 50\% better accuracy than conventional methods. Lastly, in systems with two interacting particles, the model provided non-contact measurements of the particle charge and Debye length in the plasma environment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking (FY2020 Progress Report)

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g. height, color and grayscale values; all as functions of a location in a plane projection). It is the objective of this project to produce a user-friendly interface that incorporated all operations needed to perform surface examination. For this reason, a Matlab-based Graphical User Interface (GUI) was created to integrate data input with software developed for processing and evaluation. In summary, the GUI permits selective downloading of binary data, interrogation of attributes, data labeling, flagging of significant features, execution of Machine Learning (ML) algorithms, output of parameters for trained ML algorithms, reports of ML model accuracy with respect to labeled data, and generation of graphical representations of various analyses.

3013 corrosion↗
DOE OSTI2025

Imaging nuclear shape through anisotropic and radial flow in high-energy heavy-ion collisions

Most atomic nuclei exhibit ellipsoidal shapes characterized by quadrupole deformation β 2 and triaxiality γ, and sometimes even a pear-like octupole deformation β 3 . The STAR experiment introduced a new ‘imaging-by-smashing’ technique to image the nuclear global shape by colliding nuclei at ultra-relativistic speeds and analyzing outgoing debris. Features of nuclear shape manifest in collective observables like anisotropic flow vn and radial flow via mean transverse momentum [p T ]. We present new measurements of the variances of v n (n = 2, 3, and 4) and [p T ], and the covariance of $v$$^{2}_{n}$ with [p T ], in collisions of highly deformed 238 U and nearly spherical 197 Au. Ratios of these observables between the two systems effectively suppress common final-state effects, isolating the strong impact of uranium’s deformation. By comparing results with state-of-the-art hydrodynamic model calculations, we extract β 2U and γ U values consistent with those deduced from low-energy nuclear structure measurements. Measurements of v 3 and its correlation with [p T ] also provide the first experimental suggestion of a possible octupole deformation for 238 U. These findings provide significant support for using high-energy collisions to explore nuclear shapes on femtosecond timescales, with implications for both nuclear structure and quark-gluon plasma studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

New constraints on sodium production in globular clusters from the Na 23 ( He 3 , d ) Mg 24 reaction

The star-to-star anticorrelation of sodium and oxygen is a defining feature of globular clusters, but, to date, the astrophysical site responsible for this unique chemical signature remains unknown. Sodium enrichment within these clusters depends sensitively on reaction rate of the sodium destroying reactions 23 Na(p, γ) and 23 Na(p,α). In this paper, we report the results of a 23 Na( 3 He,d) 24 Mg transfer reaction carried out at Triangle Universities Nuclear Laboratory using a 21 MeV 3 He beam. Astrophysically relevant states in 24 Mg between 11 < E x < 12 MeV were studied using high-resolution magnetic spectroscopy, thereby allowing the extraction of excitation energies and spectroscopic factors. Bayesian methods are combined with the distorted wave Born approximation to assign statistically meaningful uncertainties to the extracted spectroscopic factors. For the first time, these uncertainties are propagated through to the estimation of proton partial widths. Our experimental data are used to calculate the reaction rate. The impact of the new rates are investigated using asymptotic giant branch star models. Furthermore, it is found that while the astrophysical conditions still dominate the total uncertainty, intramodel variations on sodium production from the 23 Na(p, γ) and 23 Na(p,α) reaction channels are a lingering source of uncertainty.

20 ≤ A ≤ 38↗
DOE OSTI2019

High definition large area mapping of geological samples using a Maia detector array in the Nuclear Microprobe

A Maia detector array has been utilized and installed on the CSIRO-MARC Nuclear Microprobe. Maia uses an annular array of 384 silicon detectors to provide a solid-angle of 1.3 sr to improve the collection of X-rays induced by Particle Induced X-ray Emission (PIXE) using a micron focussed MeV proton beam. It features event-by-event data acquisition with dead-time correction and pileup rejection and a total count-rate capacity above 10 M/s, versatile stage and beam scanning modes (coupled to the DAQ-36 system described previously) to cater for a variety of sample configuration and experimental needs, precise measurement of transit time and integration of beam fluence per pixel, and spectral deconvolution of event data in real-time using the Dynamic Analysis method to provide element images during data collection. In this work we demonstrate the system on thin sections made from geological samples extracted from active seafloor hydrothermal vents in the PACMANUS hydrothermal field in Papua New Guinea.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Scattering processes and resonances from lattice QCD

The vast majority of hadrons observed in nature are not stable under the strong interaction; rather they are resonances whose existence is deduced from enhancements in the energy dependence of scattering amplitudes. The study of hadron resonances offers a window into the workings of quantum chromodynamics (QCD) in the low-energy nonperturbative region, and in addition many probes of the limits of the electroweak sector of the standard model consider processes which feature hadron resonances. From a theoretical standpoint, this is a challenging field: the same dynamics that binds quarks and gluons into hadron resonances also controls their decay into lighter hadrons, so a complete approach to QCD is required. Presently, lattice QCD is the only available tool that provides the required nonperturbative evaluation of hadron observables. This paper reviews progress in the study of few-hadron reactions in which resonances and bound states appear using lattice QCD techniques. The leading approach is described that takes advantage of the periodic finite spatial volume used in lattice QCD calculations to extract scattering amplitudes from the discrete spectrum of QCD eigenstates in a box. An explanation is given of how from explicit lattice QCD calculations one can rigorously garner information about a variety of resonance properties, including their masses, widths, decay couplings, and form factors. Finally, the challenges which currently limit the field are discussed along with the steps being taken to resolve them.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Surface Doping Boosts Triplet Generation Yield in Perovskite-Sensitized Upconversion

Triplet–triplet annihilation-based photon upconversion (TTA-UC) can efficiently generate higher energy photons at low relative fluences. Bulk metal halide perovskites have offered promise in efficiently sensitizing molecular triplet states in the solid state, necessary for the integration of TTA-UC into device-based applications. Recent work focused on TTA-UC from a rubrene triplet annihilator sensitized by perovskite thin films has established relatively efficient charge extraction from the perovskite, forming the triplet exciton in rubrene. Yet, the specifics underpinning charge transfer at the perovskite/rubrene interface are not fully elucidated. Here, to improve device performance and study the properties governing charge transfer at the interface, various organic solvents are explored to treat the perovskite surface. Scanning tunneling microscopy and spectroscopy show a difference in the electronic band structure, where both n- and p-type terminated perovskite surfaces are observed depending on the solvent used. Supported by optical spectroscopy, the impact of the perovskite electronic structure is monitored, indicating that n-type perovskite sensitizers feature higher TTA-UC efficiencies due to favorable band bending resulting in efficient hole-mediated triplet formation. Overall, the tuning of the electronic structure of the perovskite sensitizer through solvent treatment is shown to be a key force in tuning the mechanism of efficient triplet generation.

36 MATERIALS SCIENCE↗