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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unveiling the properties of the dimuonium at the energies available at the Large Hadron Collider at CERN

We study the production of dimuonium (also known as true muonium) in two- and three-photon fusion processes in nucleus–nucleus collisions at the CERN Large Hadron Collider (LHC). A new formalism is introduced for the production process, and valuable new information is extracted, which will be helpful in proposals of future experiments. We explore the phase space constraints, the reaction mechanisms, and how dimuonium decay observables might be jeopardized by other physical processes. Here, we show that the energies available at the Large Hadron Collider at CERN might lead to the first identification of dimuonium in a terrestrial laboratory.

Bertulani, Carlos A.↗
DOE OSTI2024

Fusion Neutron Generator

The proposed code, named FROG (Fusion neutron Generator) is built upon the open-source particle transport Monte Carlo toolkit Geant4. Geant4 provides C++ classes that can be leveraged to build application-specific codes dealing with the transport of particles through matter. Geant4-based codes are applied in high-energy particle physics experiments, medical applications, shielding, and space applications for example. The FROG code allows the user to define the geometry of a neutron converter device shaped as a hollow cylinder, where a neutron breeding material such as lithium deuteride (LiD) is cladded by two concentric cylinders. Such neutron converter is then placed inside a regular nuclear fission reactor, where thermal neutrons will react with the neutron breeder material (typically, Lithium 6), and through a series of reactions, will generate high-energy neutrons – neutrons whose kinetic energy are around 14 MeV. The hollowed central portion can hold a specimen that will be bombarded by high-energy neutrons created inside the neutron breeding material. Figuratively speaking, this type of device transforms neutrons from thermal (~0.625 eV) to fusion (~14 MeV) energies and is sometimes termed “fusion-to-thermal neutron converters” in the literature. The code consists of C++ source file compiled and linked to generate an executable. The user can select the dimensions of the converter (radius, length, and thickness of the breeder material), the breeder material type, the cladding material, and the specimen material that will be activated or irradiated. As input, the neutron flux for a specific location inside a reactor, for instance, positions in ATR, is required. As output, the code predicts the number of high-energy neutrons produced, the total neutron flux and fluence as well as its detailed spectrum. The physics involved in such device is very complex, as it requires modeling neutron transport, light-ion (tritons) transport, as well as fusion reactions. The Geant4 toolkit provides the required physical models.

Martin, NicholasP. [Idaho National Laboratory (INL↗
DOE OSTI2021

Probing Phase Transitions and Magnetism in Minerals with Neutrons

We report the development of sophisticated sample environments to control temperature, pressure, and magnetic field has grown in parallel with neutron source and instrumentation development. High-pressure apparatus, with high- and low-temperature capability, novel designs for diamond cells, and large volume presses are matched with next-generation neutron sources and moderator designs to provide unprecedented neutron beam brightness. Recent developments in sample environments are expanding the pressure–temperature space accessible to neutron scattering experiments. Researchers are using new capabilities and an increased understanding of the fundamentals of structural and magnetic transitions to explore new territories, including hydrogenous minerals (e.g., ices and hydrates) and magnetic structural phase diagrams.

58 GEOSCIENCES↗
DOE OSTI2022

Scalable Gaussian Processes, GPyTorch Application Benchmarking, and Targeted Adaptive Design (TAD) on ThetaGPU

We aim at showcasing the scalability of Gaussian Process (GP). The naive GP implementation scales cubically with data size, which can be prohibitive, so GP has not heretofore been considered suitable for very large-scale problem settings. We take advantage of GPyTorch, a library for scalable GPs built on top of PyTorch that incorporates GPU acceleration. With GPyTorch, one can achieve nearly linear scaling with structured kernel interpolation (SKI) and constant-time predictive covariances computation with LanczOs Variance Estimates (LOVE) while preserving accuracy. We also take advantage of the computational power of ThetaGPU, a supercomputer of Argonne Leadership Computing Facility (ALCF). In addition, we implement a scalable, GPU-ready version of Targeted Adaptive Design (TAD), a GP-based data-driven algorithm that efficiently searches the control space of an advanced manufacturing experiment for settings capable of producing a required design within a specified tolerance, despite the poorly known mapping from control settings to design. We finally show our benchmarking for GPyTorch and TAD performance on CPU vs. ThetaGPU and discuss the results and implications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Overview and Commissioning Status of the UCLA MITHRA Facility

Presented here are the first results of commissioning of the S-Band hybrid photoinjector and laser systems at the new accelerator and light source facility, MITHRA, at UCLA. The radiation bunker and capabilities of the facility are described with motivation for detailed measurement of beam parameters explained. Following thorough characterization of the photoinjector, a 1.5 m linac is to be installed and experiments up to 30 MeV will begin. These will include experiments in basic plasma physics, space plasma, terahertz production in dielectric structures, and inverse Compton scattering and applications for the X-rays produced.

Williams, Oliver (ORCID:0000000255874982)↗
DOE OSTI2022

Particle Physics of the Dark Sector

The mystery associated with a proposed Dark Sector of phenomena that are separate from the standard model of particle physics is described. A Dark Sector may possess matter particles, force carriers which mediate their interactions, and new interactions and symmetries that are beyond the standard model of particle physics. Various approaches for Dark Sector searches are described, including those at the energy frontier at the Large Hadron Collider, in astrophysical interactions with both terrestrial experiments and those in space-born platforms. Searches using low energy photons from microwave energies in cryogenic environments to x-ray energies are also described. While there is no noncontroversial evidence for Dark Sector phenomena presently, new searches with more modern equipment and analysis methods are exploring regions of phase space that have not been available before now, indicating ongoing interest and excitement in this research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

HL-LHC Computing Review: Common Tools and Community Software

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Accurate prediction of short-range order and its effect on thermodynamic, structural, and electronic properties of disordered alloys: exemplified in archetypical Cu 3 Au

Electronic-structure methods based on density-functional theory (DFT) were used to quantify the effect of chemical short-range order (SRO) on thermodynamic, structural, and electronic properties of archetypal face-centered-cubic (fcc) Cu3Au alloy. We showed that SRO can be tuned to alter bonding and lattice dynamics (i.e., phonons) and detail how these properties are changed with SRO. Thermodynamically favorable SRO significantly improved the phase stability of fcc Cu3Au from -0.0343 eV-atom -1 to –0.0682 eV-atom -1 . We used our DFT-based linear-response theory to predict SRO and its electronic origin, and accurately estimate the observed transition temperature, ordering instability (L1 2 ), and Warren-Cowley SRO parameters, in agreement with experiments. The accurate prediction of real-space SRO gives an edge over computationally and resource intensive approaches such as monte-carlo methods or experiments, which will enable large scale molecular dynamic simulations by providing supercells with optimized SRO. Here we also analyzed phonon dispersion and estimated the vibrational entropy change (from 9kB at 300 K to 6kB at 100 K) in fcc Cu3Au. We established from SRO analysis that exclusion of chemical interactions may lead to a skewed view of true properties in chemically complex alloys. The first-principles methods described in this work are generally applicable to any arbitrary solid-solution alloys, including multi-principal-element alloys, therefore, holds promise for designing technologically useful materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Interlayer spacing in pillared and grafted MCM-22 type silicas: density functional theory analysis versus experiment

Pillaring of synthetic layered crystalline silicates and aluminosilicates provides a strategy to enhance their adsorption and separation performance, and can facilitate the understanding of such behavior in more complex natural clays. In this study, we perform the first-principles density functional theory calculations for the pillaring of the pure silica polymorph of an MCM-22 type molecular sieve. Starting with a precursor material MCM-22P with fully hydroxylated layers, a pillaring agent, (EtO) 3 SiR, can react with hydroxyl groups (–OH) on adjacent internal surfaces, 2(–OH) + (EtO) 3 SiR + H 2 O → (–O) 2 SiOHR + 3EtOH, to form a pillar bridging these surfaces, or with a single hydroxyl, –OH + (EtO) 3 SiR + 2H 2 O → (–O)Si(OH) 2 R + 3EtOH, grafting to one surface. For computational efficiency, we replace the experimental organic ligand, R, by a methyl group. We find that the interlayer spacing in MCM-22 is reduced by 2.66 Å relative to weakly bound layers in the precursor MCM-22P. Including (–O) 2 SiR bridges for 50% (100%) of the hydroxyl sites in MCM-22P increases the interlayer spacing relative to MCM-22 by 2.52 Å (2.46 Å). For comparison, we also analyze the system where all –OH groups in MCM-22P are replaced by non-bridging grafted (–O)Si(OH) 2 R which results in a smaller interlayer spacing expansion of 2.17 Å relative to MCM-22. Our results for the interlayer spacing in the pillared materials are compatible with experimental observations for a similar MCM-22 type material with low Al content (Si : Al = 51 : 1) of an expansion relative to MCM-22 of roughly 2.8 Å and 2.5 Å from our x-ray diffraction and scanning transmission electron microscopy analyses, respectively. The latter analysis reveals significant variation in individual layer spacings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Composition design of high-entropy alloys with deep sets learning

High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Space-time crystalline order of a high-critical-temperature superconductor with intrinsic Josephson junctions

Abstract We theoretically demonstrate that the high-critical-temperature (high- T c ) superconductor Bi 2 Sr 2 CaCu 2 O 8+ x (BSCCO) is a natural candidate for the recently envisioned classical space-time crystal. BSCCO intrinsically forms a stack of Josephson junctions. Under a periodic parametric modulation of the Josephson critical current density, the Josephson currents develop coupled space-time crystalline order, breaking the continuous translational symmetry in both space and time. The modulation frequency and amplitude span a (nonequilibrium) phase diagram for a so-defined spatiotemporal order parameter, which displays rigid pattern formation within a particular region of the phase diagram. Based on our calculations using representative material properties, we propose a laser-modulation experiment to realize the predicted space-time crystalline behavior. Our findings bring new insight into the nature of space-time crystals and, more generally, into nonequilibrium driven condensed matter systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Coherent Scattering of Low Mass Dark Matter from Optically Trapped Sensors

In this work, we propose a search for low mass dark matter particles through momentum recoils caused by their scattering from trapped, nanometer-scale objects. Our projections show that even with a modest array of femtogram-mass sensors, parameter space beyond the reach of existing experiments can be explored. The case of smaller, attogram-mass sensors is also analyzed-where dark matter can coherently scatter from the entire sensor-enabling a large enhancement in the scattering cross-section relative to interactions with single nuclei. Large arrays of such sensors have the potential to investigate new parameter space down to dark matter masses as low as 10 keV. If recoils from dark matter are detected by such sensors, their inherent directional sensitivity would allow an unambiguous identification of a dark matter signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model

The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Transitions between electron emission and gas breakdown mechanisms across length and pressure scales

This Perspective outlines theoretical, simulation, and experimental studies linking electron emission and gas breakdown. Many studies have investigated field emission-driven microscale gas breakdown, including recent reviews [Garner et al., IEEE Trans. Plasma Sci. 48, 808–824 (2020); Fu et al., Plasma Res. Express 2, 013001 (2020)]. This Perspective focuses on generalizing field emission-driven microscale gas breakdown to consider the contribution of other forms of electron emission, specifically thermionic and space-charge limited. Recent theoretical studies have unified thermionic, field, and space-charge limited emission with and without collisions to derive “nexuses” where the individual solutions match, indicating transitions in the mechanisms. Reducing device size to nanoscale at atmospheric pressure leads to a transition from field emission to space-charge limited emission for nitrogen at ~250 nm. This Perspective summarizes the derivation of these nexuses and future extensions. We next describe simulation and theoretical studies for field emission-driven microscale gas breakdown and highlight how the nexus theory may be integrated to account for temperature, space-charge, and pulse parameters. Finally, we summarize the development of optical techniques to assess microscale gas breakdown and recent nanoscale experiments at atmospheric pressure that suggest that space-charge may begin to contribute to field emission prior to gas breakdown. We highlight the combination of theory, simulation, and experiment to link electron emission and gas breakdown mechanisms across length, pressure, and temperature scales for applications that include vacuum electronics, pulsed power, and medicine.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Critical Unresolved Region Integral Experiment Execution

Integral Experiments are a key aspect of validating nuclear data behavior and simulation capabilities. Further, integral experiments are the earliest form of validation of equations and codes. They have been used extensively since the 1940s. Today, simulation and predictive capabilities have improved greatly from 75 years ago, but nonetheless, integral experiments are still needed. As simulation capabilities improve, their uncertainties get much smaller. The current focus of many new experiments is understanding the intermediate energy range. The intermediate energy range does not have a wide application space and it can be challenging to design experiments that are sensitive to neutron energies in this region. The Zeus experiments evaluated highly enriched uranium (HEU) in the intermediate energy region. Building on the success and knowledge gained from the Zeus experiments, the Critical Unresolved Region Integral Experiment (CURIE) experiments were designed to evaluate HEU in the narrower unresolved resonance region (URR). These experiments were executed during June and July 2020 at the National Criticality Experiments Research Center (NCERC). The National Criticality Experiments Research Center (NCERC), operated by Los Alamos National Laboratory, is the only general purpose critical experiments facility in the United States of America. NCERC regularly designs and executes critical experiments and other measurements useful to a wide variety of fields including nuclear criticality safety, commercial nuclear energy, nonproliferation, and nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A dehydrated space-weathered skin cloaking the hydrated interior of Ryugu

Abstract Without a protective atmosphere, space-exposed surfaces of airless Solar System bodies gradually experience an alteration in composition, structure and optical properties through a collective process called space weathering. The return of samples from near-Earth asteroid (162173) Ryugu by Hayabusa2 provides the first opportunity for laboratory study of space-weathering signatures on the most abundant type of inner solar system body: a C-type asteroid, composed of materials largely unchanged since the formation of the Solar System. Weathered Ryugu grains show areas of surface amorphization and partial melting of phyllosilicates, in which reduction from Fe 3+ to Fe 2+ and dehydration developed. Space weathering probably contributed to dehydration by dehydroxylation of Ryugu surface phyllosilicates that had already lost interlayer water molecules and to weakening of the 2.7 µm hydroxyl (–OH) band in reflectance spectra. For C-type asteroids in general, this indicates that a weak 2.7 µm band can signify space-weathering-induced surface dehydration, rather than bulk volatile loss.

Astronomy & Astrophysics↗
DOE OSTI2026

Final Technical Report for DoE award DE‐SC0023367 “Energetic Electron Transport in Magnetized Plasma with Magnetic Islands”

This project investigated how plasmas interact with energetic particles and solid materials under extreme conditions relevant to fusion energy, space plasmas, and planetary environments. Using experiments on the DIII-D National Fusion Facility, the research first examined how high-energy electrons move, become trapped, and are released in plasmas containing magnetic islands—structures commonly found in fusion reactors and Earth’s magnetosphere—providing new insight into particle transport and acceleration processes. The project also explored plasma-driven chemical reactions that can occur during meteoroid entry into planetary atmospheres, demonstrating that simple molecules such as ammonia can be produced and survive in high-temperature plasma conditions. Together, these results improve understanding of plasma behavior across laboratory, space, and planetary systems while informing fusion plasma control and plasma–material interaction studies. The project additionally contributed to workforce development by training graduate students, undergraduates, and early-career researchers and by disseminating results through peer-reviewed publications and international scientific conferences.

Orlov, Dmitri Mikhailovich [UC San Diego] (ORCID:0↗
DOE OSTI2024

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

36 MATERIALS SCIENCE↗