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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cryogenic electronics for noble liquid neutrino detectors

In this paper we present the general features of cryogenic (or “cold”) electronics for noble liquid time projection chambers, with design principles and details for neutrino physics, a brief history of the technology and details of recent research and development that is driving the design of the detectors under construction. Finally, some comments on future R&D envisioned and the impact of this work on other fields is described. “Cold” in the context of this work applies to CMOS devices operated at 77 K and above, at liquids temperatures of LAr (89 K), LKr (125 K) and LXe (165 K), with most of the tests performed in, or at LN 2 (77 K). Additionally, the paper is concentrated on the design of cold electronics for large liquid argon TPCs, those that have been successfully operated, MicroBooNE and ProtoDUNE, and those designed or under construction, such as SBND and DUNE first and second 10 kton modules. The high performance achieved with MicroBooNE and ProtoDUNE – a high signal-to-noise ratio combined with high stability of response – is mainly due to the integral approach to design and construction of sensing electrodes with cold readout electronics in a modular approach with the cryostat signal feed-throughs incorporating warm interface electronics into a Faraday cage with the cryostat. The integral concept is described in some detail in this paper.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

ERF: Energy Research and Forecasting Model

High performance computing (HPC) architectures have undergone rapid development in recent years. As a result, established software suites face an ever increasing challenge to remain performant on and portable across modern systems. Many of the widely adopted atmospheric modeling codes cannot fully (or in some cases, at all) leverage the acceleration provided by General-Purpose Graphics Processing Units, leaving users of those codes constrained to increasingly limited HPC resources. Energy Research and Forecasting (ERF) is a regional atmospheric modeling code that leverages the latest HPC architectures, whether composed of only Central Processing Units (CPUs) or incorporating GPUs. ERF contains many of the standard discretizations and basic features needed to model general atmospheric dynamics. The modular design of ERF provides a flexible platform for exploring different physics parameterizations and numerical strategies. ERF is built on a state-of-the-art, well-supported, software framework (AMReX) that provides a performance portable interface and ensures ERF's long-term sustainability on next generation computing systems. This paper details the numerical methodology of ERF, presents results for a series of verification/validation cases, and documents ERF's performance on current HPC systems. The roughly 5× speed up of ERF (using GPUs) over Weather Research and Forecasting (CPUs only) for a 3D squall line test case highlights the significance of leveraging GPU acceleration.

17 WIND ENERGY↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Molecular insights into the in situ early-stage assembly of metal–organic frameworks on cellulose nanofibrils

The integration of metal–organic frameworks (MOFs) into renewable matrices, including those derived from cellulose, to create cellulose/MOF hybrids has attracted significant interest due to the synergistic combination of cellulosic biopolymer properties and MOFs' multifunctional features. The interfacial interactions between cellulose and MOF not only ensure stable bonding, but ultimately also determine the bulk physical properties of the macroscopic composites. However, the mechanistic understanding of the in situ assembly between the two materials remains unclear. In this study, we employ a combination study of synthesis, experimental characterization, and molecular dynamics simulations to explore the early-stage assembly of a cellulose/MOF hybrid. Our results revealed that the growth of MOF clusters on the 2,2,6,6-tetramethyl-1-piperidinyloxy oxidized cellulose nanofibrils (TOCNF) follows an inhomogeneous sequential transformation pathway. The carboxylates of TOCNF form coordination-like bonds with the metal ions, while the hydroxyl groups of TOCNF form hydrogen bonds with MOF ligands. These interactions provide the initial nucleation sites that mediate the growth of MOF clusters and also guide the assembly of larger MOF clusters onto the TOCNF substrate. In conclusion, the fundamental insights into the in situ assembly of MOF nanoparticles on cellulosic substrates are essential for the rational design of high-performance materials with tailored morphology and optimized properties.

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

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Ferreras, Ignacio (ORCID:0000000345843127)↗
DOE OSTI2023

Universal and nonuniversal features of Bardeen-Cooper-Schrieffer theory with finite-range interactions

Here, we study analytic solutions to the Bardeen-Cooper-Schrieffer (BCS) gap equation for isotropic superconductors with finite-range interaction potentials over the full range of temperatures from absolute zero to the superconducting critical temperature 0 ≤ T ≤ T c . Using these solutions Δ (ε,T), we provide a proof of the universality of the temperature dependence of the BCS gap ratio at the Fermi level Δ (ε = 0,T)/T c . Moreover, by examining the behavior of this ratio as a function of energy ε, we find that nonuniversal features emerge away from the Fermi level, and these features take the form of a temperature-independent multiplicative factor F(ε), which is equal to Δ (ε,T)/Δ(ε = 0,T) up to exponentially small corrections, i.e., the error terms vanish like e -1/λ in the weak-coupling limit λ → 0. We discuss the model-dependent features of both F(ε) and T c , and we illustrate their behavior focusing on several concrete examples of physically relevant finite-range potentials. Comparing these cases for fixed coupling constants, we highlight the importance of the functional form of the interaction potential in determining the size of the critical temperature and provide guidelines for choosing potentials which lead to higher values of T c . We also propose experimental signatures which could be used to probe the energy dependence of the gap and potentially shed light on the underlying mechanisms giving rise to superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Regulating loops in de Sitter spacetime

Perturbative quantum field theory (QFT) calculations in de Sitter space are riddled with contributions that diverge over time. These contributions often arise from loop integrals, which are notoriously hard to compute in de Sitter. We discuss an approach to evaluate loop integrals that contribute to equal-time correlators of a scalar field theory in a fixed de Sitter background. Our method is based on the Mellin-Barnes representation of correlation functions, which allows us to regulate loop divergences by adjusting the masses of the fields, or by gently deforming the underlying de Sitter spacetime. The resulting expressions have a similar structure as a standard answer from dimensional regularization in flat space QFT. These features of the regulator are illustrated with two examples, worked out in detail. Along the way, we illuminate the physical origin of these divergences and their interpretation with the machinery of the dynamical renormalization group. Our approach regulates the IR divergences of massless and massive particles in the same way. For massless scalars, the loop corrections can be incorporated as systematic improvements to the stochastic inflation framework, allowing for a more precise description of the IR dynamics of such fields in de Sitter. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2023

On the confluence of ultrafast high-temperature sintering and flash sintering phenomena

Ultrafast high-temperature sintering (UHS) and flash sintering are novel methods for rapid sintering of ceramics, often completed in just a few seconds. Here, we show that both also share two additional features: an abrupt rise in electrical conductivity, which is electronic, and electroluminescence. More fundamentally, both are related to phonon physics where MD calculations have shown that proliferation of phonons at the edge of the Brillouin zone can induce Frenkel pairs without the application of electrical fields. Here, we show that, indeed, heating without the application of electric field, can also induce flash: Rapid heating processes of thin films of an oxide-salt deposited on silk fibers, with a propane torch, are shown to induce electronic conductivity, electroluminescence, and rapid sintering of the oxide. Further, the discussion in this article harkens back to two inventions, more than a century ago, which can now be related to flash and UHS: (i) the Nernst glow lamp circa 1900, made from zirconia, and (ii) the Welsbach mantle, constituted from ceria doped thorium oxide, in the late nineteenth century. Thus, the confluence between high heating rate and electric field induced flash phenomena links the past to the new. The emerging question is how injection of phonons that has been shown to create Frenkels can further induce high electronic conductivity and electroluminescence in oxides. Both electronic conductivity and luminescence are likely related to the generation of electron–hole pairs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Biases associated with a particular image volume metric

Image analysis area and volume metrics A 80 and V 80 , used by others, are studied as a function of image morphology, a 3D feature, and Monte Carlo noise in simulated images. The A 80 area metric is found to be stable, with consistent results (and meaning) obtainable without a detailed understanding of 3D features and/or simulation noise levels. However, the V 80 volume metric varies significantly with image morphology, 3D features, and simulation noise. If an experimental image set gives A 80 area inferences that are consistent with 2D image simulations, but V 80 volume inferences that are significantly larger than those corresponding to simulated images, then issues related to 3D features and simulation noise should be explored before conclusions are drawn related to a possible mismatch in the real physical size of experimental and simulated source volumes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Muon Neutrino Reconstruction at ICARUS with Machine Learning

The ICARUS T600 LArTPC detector successfully ran for three years at the underground LNGS laboratories, providing a first sensitive search for LSND-like anomalous electron neutrino appearance in the CNGS beam. After a significant overhauling at CERN, the T600 detector has been placed in its experimental hall at Fermilab, fully commissioned, and the first events observed with full detector readout. Regular data-taking began in May 2021 with neutrinos from the Booster Neutrino Beam (BNB) and neutrinos six degrees off-axis from the Neutrinos at the Main Injector (NuMI). Modern developments in machine learning have allowed for the development of an end-to-end machine learning-based event reconstruction for ICARUS data. This reconstruction folds in 3D voxel-level feature extraction using sparse convolutional neural networks and particle clustering using graph neural networks to produce outputs suitable for physics analyses. This poster will summarize the performance of a high-purity and high-efficiency end-to-end machine learning-based selection of muon neutrinos from the BNB and highlight studies of electromagnetic shower reconstruction from a neutral pion selection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Surface Structure of Reacting Solid Ramjet Fuels

Solid fuel ramjets are a developing technology that will enable extended range for supersonic vehicles. The simplicity of the system in terms of moving parts results in the performance being dictated by the solid fuel combustion processes. The physics near the surface, particularly in the fuel-rich recirculation zone, are challenging to resolve due to flow opacity. Here, regression rate measurements of hydroxyl-terminated polybutadiene from a miniature, portable slab burner are reported. Mass-averaged regression rates agree with correlations from the literature, suggesting that the proper physics in the burner are captured. Synchrotron-based phase-contrast imaging is used to quantify local surface features, including a multiphase froth layer, molten polymer transport, and local regression rate measurements in the recirculation zone. Regression rates near the inlet are less than the mass-averaged values, whereas near the reattachment point the local value exceeds the mass-averaged values. Further, the observed froth layer is as large as 2 mm near the inlet and decreases to sub-millimeter thicknesses near the reattachment point. An analytical heat transfer model is used to quantify the influence of the surface froth layer on the regression rates. Results indicate that the froth layer has an insulating effect and will reduce the local regression rate.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Cryo-EM visualization of viruses from partially irrigated soils

Viruses are numerically the most abundant forms on Earth, and most are present in soil. Even though viruses are highly abundant in soil and critical to rhizosphere function, visualizing the diverse morphotypes within soil has been challenging. The difficulty is primarily due to the heterogenous nature of isolated suspensions that typically contain nanometer to micron scale debris which renders protein crystallography for structural studies unfeasible and hinders cryo-electron microscopy due to ice thickness and contrast issues. Here we employed and compared a simple spin filtration method to cleanup solutions of extracted viruses for direct observation with cryo-electron microscopy. The method employs common physical biochemical separation steps to remove large and small debris which dramatically improves image quality and preservation of structural features to permit visualizing morphotypes not typically seen with conventional negative stain approaches. In addition to tailed and non-tailed polyhedral phages, several under reported or novel morphotypes of soil viruses are directly visualized as a particle library with both 2D and 3D information.

cryo-EM↗
DOE OSTI2024

A digital twin solution for floating offshore wind turbines validated using a full-scale prototype

Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim of estimating the fatigue lifetime of the tower. Our digital twin solution integrates (1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, (2) an aerodynamic estimator, and (3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration) and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work and the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads of the tower fore–aft bending moment with an accuracy of approximately 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results is promising and demonstrates the possibility of using digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.

17 WIND ENERGY↗
DOE OSTI2021

Utility-scale Building Type Assignment Using Smart Meter Data

United States building energy use accounted for 40% of total energy use, 74% of peak demand, and $412 billion in 2019. Building energy modeling allows researchers to simulate building physics, gain insights into possible energy/demand saving opportunities, and assess cost-effective resilience amidst climate change. Many building features needed to create building energy models are readily available such as 2D footprints and LiDAR (height). A critical feature that is not generally obtainable is the building type. In partnership with a utility, a years worth of real-world, 15-minute electrical use data has been examined. The smart meter data is compared to 97 different prototype building energy models to assign building type. Real-world considerations including data preparation, quality assurance, and handling of missing values for advanced metering infrastructure data are addressed. Euclidean distance for pattern-matching of energy use, dynamic time warping, and time-window statistics with machine learning are compared for determining building type from measured electricity use.

Bass, Brett↗
DOE OSTI2024

Standard and Non-Standard Neutrino Oscillations at the NOvA Experiment

Initially believed to be massless, the discovery of neutrino oscillations proved that neutrinos have masses, a feature not anticipated by the Standard Model of Elementary Particles. Investigations of the existence of the so-called physics Beyond the Standard Model is a subject of active effort in the field of particle physics. Interactions of neutrinos with matter are well described by the Standard Model, however in the presence of new physics our knowledge and ability to measure the standard neutrino oscillation parameters may be affected. Non-Standard Interactions (NSI) may arise as a subleading effect to the neutrino oscillation framework by introducing additional parameters resulting from a generalized matter potential.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗
DOE OSTI2023

Student Travel Support for the Inaugural Gordon Research Seminar on Multiphoton Processes (Final Technical Report)

This project provided financial support for early-career participants of the 2022 Gordon Research Seminar (GRS) on Multiphoton Processes, which was held on June 11 & 12, 2022 in Smithfield, Rhode Island, in conjunction with the Gordon Research Conference (GRC) on the same topic, held on June 12-17, 2022 in the same location. The GRC on Multiphoton Processes is an established conference series in the field of Atomic, Molecular and Optical (AMO) Physics with intense lasers. It regularly attracts around one hundred fifty researchers from around the world to present and discuss their forefront research featuring light-matter interaction in a variety of systems, ranging from atoms and molecules to nanosystems. In order to increase the attendance of early-career researchers at the GRC, in particular graduate students, post-docs, and other scientists with comparable levels of experience and education, the general assembly of the GRC on Multiphoton Processes decided during their last session in 2018 to organize a Gordon Research Seminar in conjunction with the next conference and organized and attended by early-career researchers. The GRS's primary intent is to provide a forum to graduate students and post-docs working in the field of intense and ultrafast AMO physics to present and exchange new data and cutting-edge ideas and to network with their peers at other research institutions around the U.S. and the world.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

79 ASTRONOMY AND ASTROPHYSICS↗