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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Gravitational waves from supercooled phase transitions in conformal Majoron models of neutrino mass

We study supercooled first-order phase transitions above the QCD scale in a wide class of conformal Majoron-like U(1)′ models that explain the totality of active neutrino oscillation data and produce a detectable stochastic gravitational wave background (SGWB) at LIGO, LISA and ET. We place constraints on the U(1)′ breaking scale and gauge coupling using current LIGO-Virgo-Kagra data. We find that strong supercooling can be ruled out in large regions of parameter space if a SGWB is not detected by these experiments. A null signal at LIGO and ET will disfavor a type-I seesaw scale above 10 14 GeV, while a positive signal is a signature of heavy right-handed neutrinos. On the other hand, LISA will be sensitive to seesaw scales as low as a TeV, and could detect a SGWB even if the right-handed neutrinos are decoupled.

early universe particle physics↗
DOE OSTI2024

Role of bath-induced many-body interactions in the dissipative phases of the Su-Schrieffer-Heeger model

The Su-Schrieffer-Heeger chain is a prototype example of a symmetry-protected topological insulator. Coupling it nonperturbatively to local thermal environments, either through the intercell or the intracell fermion tunneling elements, modifies the topological window. To understand this effect, we employ the recently developed reaction-coordinate polaron transform (RCPT) method, which allows treating system-bath interactions at arbitrary strengths. The effective system Hamiltonian, which is obtained via the RCPT, exposes the impact of the baths on the SSH chain through renormalization of tunneling elements and the generation of many-body interaction terms. By performing exact diagonalization and computing the ensemble geometric phase, a topological invariant, which is applicable even to systems at finite temperature, we distinguish the trivial band insulator (BI) from the topological insulator (TI) phases. Furthermore, through the RCPT mapping, we are able to pinpoint the main mechanism behind the extension of the parameter space for the TI or the BI phases (depending on the coupling scheme, intracell or intercell), which is the bath-induced, dimerized, many-body interaction. In conclusion, we also study the effect of on-site staggered potentials on the SSH phase diagram and discuss extensions of our method to higher dimensions.

1-dimensional systems↗
DOE OSTI2023

Microstructural Pattern Formation during Far-from-Equilibrium Alloy Solidification

We introduce a new phase-field formulation of rapid alloy solidification that quantitatively in- corporates non-equilibrium effects at the solid-liquid interface over a very wide range of interface velocities. Simulations identify a new dynamical instability of dendrite tip growth driven by solute trapping at velocities approaching the absolute stability limit. They also reproduce the formation of the widely observed banded microstructures, revealing how this instability triggers transitions between dendritic and microsegregation-free solidification. Predicted band spacings agree quantita- tively with observations in rapidly solidified Al-Cu thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Circuit Modeling, Simulation, and Experimental Validation of a 100-kW Polyphase Wireless Power Transfer System for EV Applications

Polyphase wireless power transfer (WPT) systems can achieve much higher surface power densities (kW/m2) and specific power levels (kW/kg) compared to the conventional circular single phase WPT systems. Therefore, polyphase WPT systems can reduce the size, weight, volume, and cost of the WPT systems and can simplify the electric vehicle integration with less demand for space. This study presents the high-performance and compact 100-kW WPT development using polyphase electromagnetic coupling coils with rotating magnetic fields.

Onar, Omer [ORNL] (ORCID:0000000292028857)↗
DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantum to classical parton evolution in the QGP

We study the time evolution of the density matrix of a high energy quark in the presence of a dense QCD background that is modeled as a stochastic Gaussian color field. At late times, we find that only the color singlet component of the quark’s reduced density matrix survives the in-medium evolution and that the density matrix becomes asymptotically diagonal in both transverse position and momentum spaces. In addition, we observe an accelerated entropy growth due to the larger phase space being explored by the quark and that the quantum and classical quark entropies converge at late times. We further observe that the quark state loses all memory of the initial condition. Combined with the fact that the reduced density matrix satisfies Boltzmann-diffusion transport, we conclude that the quark reduced density matrix can be interpreted as a classical phase space distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Multi-Scale Simulation of Wind Farm Performance during a Frontal Passage

Predicting the response of wind farms to changing flow conditions is necessary for optimal design and operation. Here in this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine–flow interaction, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atmospheric conditions and turbine response. Starting with mesoscale forcing, the atmosphere is dynamically downscaled to the region of interest, where the interaction between turbulent flows and individual wind turbines is simulated with 10 m grid spacing. Several improvements are made to the GAD model to mimic realistic turbine operation, including a yawing capability and a power output calculation. Ultimately, the model is able to capture both the dynamics of the frontal passage and the turbine response; predictions show good agreement with observed background velocity, turbine wake structure, and power output after accounting for a phase shift in the mesoscale forcing. This study demonstrates the utility of the WRF-GAD model framework for simulating wind farm performance under complex atmospheric conditions.

17 WIND ENERGY↗
DOE OSTI2021

High-accuracy waveforms for black hole-neutron star systems with spinning black holes

The availability of accurate numerical waveforms is an important requirement for the creation and calibration of reliable waveform models for gravitational wave astrophysics. For black hole-neutron star binaries, very few accurate waveforms are however publicly available. Most recent models are calibrated to a large number of older simulations with good parameter space coverage for low-spin non-precessing binaries but limited accuracy, and a much smaller number of longer, more recent simulations limited to non-spinning black holes. In this paper, we present long, accurate numerical waveforms for three new systems that include rapidly spinning black holes, and one precessing configuration. Here, we study in detail the accuracy of the simulations, and in particular perform for the first time in the context of BHNS binaries a detailed comparison of waveform extrapolation methods to the results of Cauchy Characteristic Extraction. The new waveforms have <0.1rad phase errors during inspiral, rising to ~(0.2-0.4)rad errors at merger, and ≲1% error in their amplitude. We compute the faithfulness of recent analytical models to these numerical results, and find that models specifically designed for BHNS binaries perform well (F>0.99) for binaries seen face-on. For edge-on observations, particularly for precessing systems, disagreements between models and simulations increase, and models that include precession and/or higher-order modes start to perform better than BHNS models that currently lack these features.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Structural and Optical Response of Polymer-Stabilized Blue Phase Liquid Crystal Films to Volatile Organic Compounds

Engineering useful mechanical properties into stimuli-responsive soft materials without compromising their responsiveness is, in many cases, an unresolved challenge. For example, polymer networks formed within blue-phase liquid crystals (BPs) have been shown to form mechanically robust films, but the impact of polymer networks on the response of these soft materials to chemical stimuli has not been explored. In this work, we report on the response of polymer-stabilized BPs (PSBPs) to volatile organic compounds (VOCs, using toluene as a model compound) and compare the response to BPs without polymer stabilization and to polymerized nematic and cholesteric phases. We find that PSBPs generate an optical response to toluene vapor (change in reflection intensity under crossed polars) that is sixfold greater in sensitivity than the polymerized nematic or cholesteric phases and with a limit of detection (140 ± 10 ppm at 25 °C) that is relevant to the measurement of permissible exposure limits for humans. Additionally, when compared to BPs that have not been polymerized, PSBPs respond to a broader range of toluene vapor concentrations (5000 vs <1000 ppm) over a wider temperature interval (25–45 vs 45–53 °C). We place these experimental observations into the context of a simple thermodynamic model to explore how the PSBP response reflects the effect of toluene on competing contributions of double-twisted LC cylinders, disclinations, and polymer network to the free energy that controls the PSBP lattice spacing. Overall, we conclude that the mechanical and thermal stability of PSBPs, when combined with their optical responsiveness to toluene, make this class of self-supporting LCs a promising one as the basis of passive and compact (e.g., wearable) sensors for VOCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Electrochemical Routes for the Valorization of Biomass-Derived Feedstocks: From Chemistry to Application

The drive to reduce consumption of fossil resources, coupled with expanding capacity for renewable electricity, invites the exploration of new routes to utilize this energy for the sustainable production of fuels, chemicals, and materials. Biomass represents a possible source of platform precursors for such commodities due to its inherent ability to fix CO2 in the form of multi-carbon organic molecules. Electrochemical methods for the valorization of biomass are thus intriguing, but there is a need to objectively evaluate this field and define the opportunity space by identifying pathways suited to electrochemistry. In this contribution we offer a comprehensive, critical review of recent advances in low-temperature (liquid phase), electrochemical reduction and oxidation of biomass-derived intermediates (polyols, furans, carboxylic acids, amino acids, and lignin), with emphasis on identifying the state-of-the-art for each documented reaction. Progress in computational modeling is also reviewed. We further suggest a number of possible reactions that have not yet been explored but which are expected to proceed based on established routes to transform specific functional groups. We conclude with a critical discussion of technological challenges for scale-up, fundamental research needs, process intensification opportunities (e.g., by pairing compatible oxidations and reductions), and new benchmarking standards that will be necessary to accelerate progress toward application in this still-nascent field.

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

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pairing Correlations across the Superfluid Phase Transition in the Unitary Fermi Gas

In the two-component Fermi gas with a contact interaction, a pseudogap regime can exist at temperatures between the superfluid critical temperature $T_c$ and a temperature $T^\ast > T_c$. This regime is characterized by pairing correlations without superfluidity. However, in the unitary limit of infinite scattering length, the existence of this regime is still debated. To help address this, we have applied finite-temperature auxiliary-field quantum Monte Carlo (AFMC) methods to study the thermodynamics of the superfluid phase transition and signatures of the pseudogap in the spin-balanced homogeneous unitary Fermi gas. We present results at finite filling factor $\textit{ν}$ ≃ 0.06 for the condensate fraction, an energy-staggering pairing gap, the spin susceptibility, and the heat capacity, and compare them to experimental data when available. In contrast to previous AFMC simulations, our model space consists of the complete first Brillouin zone of the lattice, and our calculations are performed in the canonical ensemble of fixed particle number. The canonical ensemble AFMC framework enables the calculation of a model-independent gap, providing direct information on pairing correlations without the need for numerical analytic continuation. We use finite-size scaling to estimate $T_c$ at the corresponding filling factor. In this work, we find that the energy-staggering pairing gap vanishes above $T_c$, showing no pseudogap effects, and that the spin susceptibility shows a substantially reduced signature of a spin gap compared to previously reported AFMC simulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Profusion of symmetry-protected qubits from stable ergodicity breaking

We show how combining a discrete symmetry with topological Hilbert space fragmentation can give rise to exponentially many topologically stable qubits protected by a single discrete symmetry. We illustrate this explicitly with the example of the CZ𝑝 model, where the encoded qubits are prethermally stable to arbitrary symmetry-respecting perturbations for parametrically long times, substantially enhancing the robustness of a recently proposed construction based on nontopological fragmentation. In this model, the encoded qubits naturally come in pairs for which a universal set of transversal logical gates can be performed, ruling out (by the Eastin-Knill theorem) the possibility of using them for quantum error correction. We also comment on the combination of symmetry enrichment and topological fragmentation more generally, and the implications for use of systems exhibiting Hilbert space fragmentation as quantum memories.

kinetically constrained models↗
DOE OSTI2024

Stochastic modeling of x-ray superfluorescence

An approach to modeling the dynamics of x-ray amplified spontaneous emission and superfluorescence, the phenomenon of collective x-ray emission initiated by intense pulses of x-ray free-electron lasers, is developed based on stochastic partial differential equations. The equations are derived from first principles, and the relevant approximations, derivation steps, and extensions specific to stimulated x-ray emission are presented. The resulting equations take the form of three-dimensional generalized Maxwell-Bloch equations augmented with noise terms for both field and atomic variables. The derived noise terms possess specific correlation properties that enable the correct reconstruction of spontaneous emission. Consequently, the developed theoretical formalism is universally suitable for describing all stages of stimulated x-ray emission: spontaneous emission, amplified spontaneous emission, and superfluorescence. Here, we present numerical examples that illustrate various properties of the emitted field, including spatiotemporal coherence and spectral-angular and polarization characteristics. We anticipate that the proposed theoretical framework will establish a robust foundation for interpreting measurements in stimulated x-ray emission spectroscopy, modeling x-ray laser oscillators, and describing other experiments leveraging x-ray superfluorescence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Nonlinear dynamics and quantum chaos of a family of kicked p -spin models

Herein we introduce kicked p-spin models describing a family of transverse Ising-like models for an ensemble of spin-1/2 particles with all-to-all p-body interaction terms occurring periodically in time as delta-kicks. This is the natural generalization of the well-studied quantum kicked top (p = 2) [Haake, Kus', and Scharf, Z. Phys. B 65, 381 (1987)]. We fully characterize the classical nonlinear dynamics of these models, including the transition to global Hamiltonian chaos. The classical analysis allows us to build a classification for this family of models, distinguishing between p = 2 and p > 2, and between models with odd and even p's. Quantum chaos in these models is characterized in both kinematic and dynamic signatures. For the latter, we show numerically that the growth rate of the out-of-time-order correlator is dictated by the classical Lyapunov exponent. Finally, we argue that the classification of these models constructed in the classical system applies to the quantum system as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Phase-curve Pollution of Exoplanet Transit Depths

The next generation of space telescopes will enable transformative science to understand the nature and origin of exoplanets. In particular, transit spectroscopy will reveal the chemical composition of the exoplanet atmospheres with unprecedented detail thanks to precise measurements of the visible-to-infrared transit depths down to 10 parts per million. Such a level of instrumental precision raises the challenge to obtain even more precise astrophysical models so as not to significantly influence the interpretation of the observed data. We must therefore critically revisit some of the commonly accepted assumptions that were adequate for analyzing past and current observations. A common approximation in the analysis of exoplanetary primary transits is that the planet does not contribute to the recorded flux, so-called dark planet hypothesis. In this paper, we investigate the impact of the dark planet hypothesis on the parameters obtained from the analysis of transits with particular attention to the transit depth. We develop mathematical formulae and release new software to estimate the magnitude of the potential bias. These tools will be useful in the preparation of observing proposals, as well as within the scientific consortia of the James Webb Space Telescope (JWST) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) missions. We probe the accuracy of the mathematical formulae through the analysis of synthetic observations with the JWST Mid-InfraRed Instrument. We find that self-blending from nightside emission attenuates the transit depth by >3σ for some of the known exoplanet systems, in agreement with previous work. An additional unreported effect caused by the nightside rotating into view can also impart a significant effect, but in the opposite direction (increasing the transit depth); this effect can largely be removed with conventional detrending practices, at the expense of a slight increase in noise, and mixing astrophysical variations and instrumental drifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, in this study, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp 2 -hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp 2 -hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 10 5 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R 2 ) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.

Detonation nanodiamond remodeling↗