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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Differential cross-section measurements of the production of four charged leptons in association with two jets using the ATLAS detector

Differential cross-sections are measured for the production of four charged leptons in association with two jets. These measurements are sensitive to final states in which the jets are produced via the strong interaction as well as to the purely-electroweak vector boson scattering process. The analysis is performed using proton-proton collision data collected by ATLAS at $\sqrt{s}$ = 13 TeV and with an integrated luminosity of 140 fb –1 . The data are corrected for the effects of detector inefficiency and resolution and are compared to state-of-the-art Monte Carlo event generator predictions. The differential cross-sections are used to search for anomalous weak-boson self-interactions that are induced by dimension-six and dimension-eight operators in Standard Model effective field theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Physical properties of intergrowth compound Eu 2 CuZn 2 P 3

Here, the physical properties of single-crystalline Eu 2 ⁢CuZn 2 ⁢P 3 were inspected via thermodynamic, transport, and neutron diffraction measurements. Eu 2 ⁢ CuZn 2⁢ P 3 is composed of EuCuP and EuZn 2 ⁢ P 2 motifs stacked in a one-to-one ratio. The magnetic response of Eu 2 ⁢ CuZn 2 ⁢ P 3 is similar to that of EuZn 2 ⁢ P 2 in its A-type antiferromagnetic (semiconducting) state. A Néel temperature of T N = 40.3K is obtained from the specific heat capacity. Single-crystal neutron diffraction reveals a (001) magnetic propagation vector, with primarily in-plane moments coupled along [001] in an up-up-down-down pattern. This spin structure retains the ferromagnetic EuCuP motifs and antiferromagnetic coupling across the EuZn 2 ⁢P 2 motifs. The electrical resistivity is characterized by metallic behavior above 100 K, an increase in ρ upon cooling below ≈ 75K, and a large negative magnetoresistance below T N . First principles calculations evidence a narrow band gap for antiferromagnetic ordering and semimetallic behavior for the ferromagnetic state. This is consistent with the observed effect of magnetic field on the electrical resistivity. Hall effect measurements reveal a field-induced, negative anomalous Hall effect that increases on cooling down to 2 K. Also, an unconventional contribution to the Hall effect dominates the low-field Hall signal, particularly near T N , and is similar in appearance to a topological Hall effect. However, further work is necessary to understand if any of the transport properties have topological origins or if magnetism-induced modifications to the Fermi surface induce these unusual Hall effect signals in this and related materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exploring the parameter space of MagLIF implosions using similarity scaling. I. Theoretical framework

Magneto-inertial fusion concepts, such as the magnetized liner inertial fusion (MagLIF) platform, constitute an alternative path for achieving ignition and significant fusion yields in the laboratory. The space of experimental input parameters defining a MagLIF load is highly multi-dimensional, and the implosion itself is a complex event involving many physical processes. In the first paper of this series, we develop a simplified analytical model that identifies the main physical processes at play during a MagLIF implosion. Using non-dimensional analysis, we determine the most important dimensionless parameters characterizing MagLIF implosions and provide estimates of such parameters using typical fielded or experimentally observed quantities for MagLIF. Here, we then show that MagLIF loads can be “incompletely” similarity scaled, meaning that the experimental input parameters of MagLIF can be varied such that many (but not all) of the dimensionless quantities are conserved. Based on similarity-scaling arguments, we can explore the parameter space of MagLIF loads and estimate the performance of the scaled loads. Then, in the follow-up papers of this series, we test the similarity-scaling theory for MagLIF loads against simulations for two different scaling “vectors,” which include current scaling and rise-time scaling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Consistent truncations in higher derivative supergravity

We consider the torus reduction of heterotic supergravity in the presence of four-derivative corrections. In particular, the reduction on T n generically leads to a half-maximal supergravity coupled to n vector multiplets, and we show that it is consistent to truncate out said vector multiplets. This is done by the analysis of both the bosonic equations of motion and the Killing spinor equations. As an application of the consistent truncation, we examine the four-derivative corrected BPS black string that reduces to a black hole in minimal nine-dimensional supergravity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Magnetic Diffuse Scattering of YBaCo 4 O 7 and LuBaCo 4 O 7 on Kagome and Triangular Lattices

In this work, the magnetic diffuse scattering of YBaCo 4 O 7 and LuBaCo 4 O 7 with alternating kagome and triangular lattices has been studied through neutron scattering measurements on single-crystal samples. In YBaCo 4 O 7 , the line-shape-magnetic-diffuse scattering is observed around the magnetic transition temperature, which is consistent with the Monte Carlo simulation. In LuBaCo 4 O 7 , the line-shape-magnetic-diffuse scattering has not been detected. At low temperature, the propagation vectors of the observed magnetic reflections are different between YBaCo 4 O 7 and LuBaCo 4 O 7 . The structural distortion in RBaCo 4 O 7 is suggested to play a critical role for the magnetic properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Spatially resolved polarization swings in the supermassive binary black hole candidate OJ 287 with first Event Horizon Telescope observations

We present the first Event Horizon Telescope 1.3 mm observations of the supermassive binary black hole candidate OJ 287. The observations achieved an unprecedented angular resolution of 18 μas and reveal significant structural and polarization variability over just five days, marking the shortest timescale on which such changes have been directly imaged in this source. The inner jet exhibits a twisted ridgeline structure, with features displaying apparent superluminal motions up to about 22 c. The linear polarization maps reveal three main polarized features whose electric-vector position angles (EVPAs) change substantially over the time span of our observations, including a component with a radial polarization consistent with being produced by a recollimation shock. Most notably, we directly resolved two innermost jet components whose EVPAs rotate in opposite directions. The faster component, moving at 2.4 ± 0.9 μas/day (17.4 ± 6.5 c), exhibits counterclockwise EVPA swings of roughly 3.7° per day, while the slower component, with a proper motion of 1.4 ± 0.3 μas/day (10.2 ± 2.2 c), rotates clockwise at approximately 2.5° per day. Previous studies inferred helical magnetic fields in AGN jets from time-resolved or integrated polarization variability but lacked the angular resolution to directly image this effect. Our results provide spatially resolved evidence that a helical magnetic field threads the jet’s collimation and acceleration zone, ruling out models based on the superposition of unresolved components. Our analysis suggests that propagating shocks interact with a Kelvin–Helmholtz plasma instability, illuminating different phases of the helical magnetic field and producing the observed polarization spatial and temporal variability. Moreover, our model naturally accounts for the more rapid polarization rotation observed in the faster moving component. Our model predicts even more rapid swings in polarization, which could be tested with future observations featuring a more densely sampled time coverage.

OJ 287↗
DOE OSTI2022

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

3⁢𝑄 magnetic order with spatially alternating spin scalar chirality in overdoped C⁢o 0.336⁢ Ta⁢S 2

C⁢o 𝑥 ⁢Ta⁢S 2 (𝑥≈1/3) exhibits a spontaneous Hall effect from spin texture in antiferromagnets, with a tetrahedral triple-Q (3Q) order and uniform spin scalar chirality. Upon Co overdoping (𝑥>1/3), it undergoes a shift in magnetic ordering vectors from 𝐐 m =(1/2, 0, 0) to (1/3, 0, 0). Interestingly, the spontaneous Hall effect disappeared in the overdoped regime, which was originally attributed to the loss of 3Q order. However, a question remains whether a new type of 3Q order can exist with alternating chirality in the overdoped regime. To address this, we investigated C⁢o 0.336⁢ Ta⁢S 2 using inelastic neutron scattering (INS), neutron diffraction, and optical dichroism, and found that INS data and spin-wave simulations support a 3Q order with alternating chirality. Moreover, neutron diffraction data show field-independent Bragg peaks, while linear dichroism detects no in-plane anisotropy, consistent with threefold rotation symmetry. Our data support the scenario of an alternating-chirality 3Q order in C⁢o 0.336 ⁢Ta⁢S 2 , canceling the spontaneous Hall effect. Furthermore, this study highlights a combined neutron-optical approach to identify complex spin textures.

Landau-Lifschitz-Gilbert equation↗
DOE OSTI2020

Structural distortion and incommensurate noncollinear magnetism in EuAg 4 As 2

Layered pnictide materials have provided a fruitful platform to study various emergent phenomena, including superconductivity, magnetism, charge density waves, etc. In this work, we report the observation of structural distortion and noncollinear magnetism in layered pnictide EuAg 4 As 2 via transport, magnetization, single crystal x-ray, and neutron diffraction data.EuAg 4 As 2 single crystal shows a structural distortion at 120 K, where two sets of superlattice peaks with the propagation vectors of q 1 =±(0, 0.25, 0.5) and q 2 =±(0.25, 0, 1) emerge. Between 9–15 K, the hexagonal Eu 2+ sublattice enters an unpinned incommensurate magnetic state, with magnetic Bragg reflections pictured as circular sectors. Furthermore, below 9 K, it orders in an incommensurate noncollinear antiferromagnetic state with a well-defined propagation wavevector of (0, 0.1, 0.12) and a very rare magnetic structure, which is helical along the c axis and cycloidal along the b axis with a moment of 6.4 μB/Eu 2+ . Furthermore, rich magnetic phases under magnetic fields, large magnetoresistance, and strong coupling between charge carriers and magnetism in EuAg 4 As 2 are revealed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nonrelativistic nuclear reduction for tensor couplings in dark matter direct detection and μ → e conversion

The nonrelativistic effective field theory (NRET) is widely used in dark matter direct detection and charged-lepton flavor violation studies through μ → e conversion. However, existing literature has not fully considered tensor couplings. This study fills this gap by utilizing an innovative tensor decomposition method, extending NRET to incorporate previously overlooked tensor interactions. This development is expected to have a significant impact on ongoing experiments seeking physics beyond the Standard Model and on our understanding of the new-physics interactions. Notably, we identify additional operators in μ → e conversion that are absent in scalar and vector couplings. To support further research and experimental analyses, comprehensive tables featuring tensor matrix elements and their corresponding operators are provided. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Machine Learned Empirical Numerical Integrator from Simulated Data

Recently, a number of state-of-the-art surrogate machine learning (ML) models have been designed for global weather and climate prediction, which have been trained using reanalysis data products. Reanalysis data products are constructed using numerical model simulations that combine numerical integration of partial differential equations and parameterization schemes. These products are typically only archived and made available using coarsened spatial and temporal resolutions. This study explores the impact of the numerical generation methods used to produce the training datasets and the temporal resolution of those datasets on machine learning surrogate models. Using the nonlinear vector autoregression (NVAR) machine as an explainable ML technique, simple dynamical systems are emulated with ML models trained on data produced by three classical numerical integration schemes. NVAR is validated as a skillful ML method, capable of producing accurate predictions and, more importantly, reconstructing both the underlying dynamics and the numerical integration scheme used to generate the training data. However, the machine fails to generalize predictions on unseen test data generated by different numerical integration schemes, despite the underlying dynamical system being the same. This result provides a word of caution for the growing field of machine learning emulation of weather and climate dynamics. Furthermore, we illustrate using NVAR that training on temporally coarsened data may increase the required complexity of ML models and potentially introduce new numerical challenges. Finally, we discover that empirical integration schemes with arbitrary time-stepping sizes can be constructed directly from the data, which implies a potential for the development of empirical numerical integration schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dark photon and dark Z mediated B meson decays

We study flavor changing neutral current decays of B and K mesons in the dark U(1) D model, with the dark photon/dark Z mass between 10 MeV and 2 GeV. Although the model provides an improved fit (compared to the standard model) to the differential decay distributions of B → K ($\ast$) ℓ + ℓ – , with ℓ = μ, e, and B s → $\phi$μ + μ – , the allowed parameter space is ruled out by measurements of atomic parity violation, K + → μ + + invisible decay, and ${B}_s-{\overline{B}}_s$ mixing, among others. To evade constraints from low energy data, we extend the model to allow for (1) additional invisible Z D decay, (2) a direct vector coupling of Z D to muons, and (3) a direct coupling of Z D to both muons and electrons, with the electron coupling fine-tuned to cancel the Z D coupling to electrons via mixing. We find that only the latter case survives all constraints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Applying Quantum Computing to Simulate Power System Dynamics

Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations due to a large number of generators, loads, and transmission lines. Thus, its computational complexity grows exponentially with the system size. This paper demonstrates the potential use of quantum computing algorithms to model the power system dynamics. Leveraging a symbolic programming framework, we equivalently convert the power system dynamics’ differential algebraic equations (DAEs) into ordinary differential equations (ODEs), where the data of the state vector can be encoded into quantum computers via amplitude encoding. The system's nonlinearity is captured by Taylor polynomial expansion, the quantum state tensor, and Hamiltonian simulation, whereas state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can simulate the dynamics of the power system with high accuracy, whereas its complexity is polynomial in the logarithm of the system dimension. Our work also illustrates the use of scientific machine learning tools for implementing scientific computing concepts, e.g., Taylor expansion, DAEs/ODEs transform, and quantum computing solver, in the field of power engineering.

Tran, Huynh↗
DOE OSTI2024

Searches for Higgs boson production through decays of heavy resonances

The discovery of the Higgs boson has led to new possible signatures for heavy resonance searches at the LHC. Since then, search channels including at least one Higgs boson plus another particle have formed an important part of the program of new physics searches. In this report, the status of these searches by the CMS Collaboration is reviewed. Searches are discussed for resonances decaying to two Higgs bosons, a Higgs and a vector boson, or a Higgs boson and another new resonance. All analyses use proton–proton collision data collected at $\sqrt{s} = 13$ TeV in the years 2016–2018. A combination of the results of these searches is presented together with constraints on different beyond-the-standard model scenarios, including scenarios with extended Higgs sectors, heavy vector bosons and extra dimensions. Studies are shown for the first time by CMS on the validity of the narrow-width approximation in searches for the resonant production of a pair of Higgs bosons. The potential for a discovery at the High Luminosity LHC is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A detailed analysis of the dynamics of fast neutrino flavor conversions with scattering effects

We calculate the dynamics of fast neutrino flavor conversions with Boltzmann collisions of neutrino scatterings in a homogeneous system. We find the enhancement or suppression of the flavor conversions in various setups of the collision terms. We analyze the mechanism of fast flavor conversions based on the motion of polarization vectors in the cylindrical coordinate analogous to a pendulum motion. The phases of the all the polarization vectors synchronize in the linear evolution phase, and the phase deviation from the Hamiltonian governs the neutrino flavor conversions. In a non-linear regime of flavor conversions, the collision terms induce a spiral motion of the polarization vector and gradually make the phase space smaller. The collision terms align all of the polarization vectors, and the flavor conversions eventually settle into equilibrium when the distributions of neutrinos become isotropic. Though our current analysis does not fully clarify the non-linear phenomena of fast flavor conversions, the framework of the pendulum motion gives a new insight into this complicated phenomenon that will be helpful in further studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Far-from-equilibrium slow modes and momentum anisotropy in expanding plasma

The momentum distribution of particle production in heavy-ion collisions encodes information about thermalization processes in the early-stage quark-gluon plasma. We use kinetic theory to study the far-from-equilibrium evolution of an expanding plasma with an anisotropic momentumspace distribution. We identify slow and fast degrees of freedom in the far-from-equilibrium plasma from the evolution of moments of this distribution. At late times, the slow modes correspond to hydrodynamic degrees of freedom and are naturally gapped from the fast modes by the inverse of the relaxation time, $τ$$^{–1}_{R}$. At early times, however, there are an infinite number of slow modes with a gap inversely proportional to time, τ –1 . From the evolution of the slow modes we generalize the paradigm of the far-from-equilibrium attractor to vector and tensor components of the energy momentum tensor, and even to higher moments of the distribution function that are not part of the hydrodynamic evolution. We predict that initial-state momentum anisotropy decays slowly in the far-from-equilibrium phase and may persist until the relaxation time.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Electroweak $\mathrm{ALP}$ searches at a muon collider

A high-energy muon collider with center-of-mass energy around and above 10 TeV is also a vector boson fusion (VBF) machine, due to the significant virtual electroweak (EW) gauge boson content of high-energy muon beams. This feature, together with the clean environment, makes it an ideal collider to search for TeV-scale axion-like particles (ALP) coupling to Standard Model EW gauge bosons, which current and other future colliders have limited sensitivities to. We present detailed analyses of heavy ALP searches in both the VBF and associated production channels at a muon collider with different running benchmarks. We also show projected constraints on the ALP couplings in the effective field theory, including an operator with its coefficient not determined by the mixed Peccei-Quinn anomaly. We demonstrate that a muon collider could probe new ALP parameter space and push the sensitivities of the couplings between the ALP and EW gauge bosons by one order of magnitude compared to HL-LHC. The projected limits and search strategies for ALPs could also be applied to other types of resonances coupling to EW gauge bosons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗