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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A review of low-rank methods for time-dependent kinetic simulations

Time-dependent kinetic models are ubiquitous in computational science and engineering. The underlying integro-differential equations in these models are high-dimensional, comprised of a six–dimensional phase space, making simulations of such phenomena extremely expensive. In this article we demonstrate that in many situations, the solution to kinetics problems lives on a low dimensional manifold that can be described by a low-rank matrix or tensor approximation. We then review the recent development of so-called low-rank methods that evolve the solution on this manifold. The two classes of methods we review are the dynamical low-rank (DLR) method, which derives differential equations for the low-rank factors, and a Step-and-Truncate (SAT) approach, which projects the solution onto the low-rank representation after each time step. Thorough discussions of time integrators, tensor decompositions, and method properties such as structure preservation and computational efficiency are included. We further show examples of low-rank methods as applied to particle transport and plasma dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thermal conductivity suppression in uranium-doped thorium dioxide due to phonon-spin interactions

In this work, impact of low level of uranium (U) atom substitution on thermal conductivity of thorium dioxide (ThO 2 ) is investigated. ThO 2 is an electronic insulator with a wide optical band-gap and no unpaired electrons whose thermal transport is governed by phonons. U-substitution introduces unpaired f-electrons resulting in paramagnetic behavior of U-ThO 2 at room temperature, which significantly suppresses its thermal conductivity. A single crystal of U-ThO 2 with graded composition of U is grown using a hydrothermal synthesis method, and thermal conductivity measurements are performed in regions with uniform composition of U at levels of 0%, 6%, 9% and 16%. Measured thermal conductivity profiles over 77–300 K temperature range are analyzed using an analytical expression for phonon-mediated thermal transport based on Klemens-Callaway model. Temperature dependent thermal conductivity is found to deviate significantly from the Rayleigh scattering trend expected for a simple substitutional point defect with a small perturbation to mass and interatomic forces. With the resonant scattering term, observed large suppression of thermal conductivity at low temperatures can be closely reproduced. Additionally, the extracted phonon-spin coupling constants imply a nonlinear relation of phonon-spin interaction intensity with respect to U doping percentage. Our study reveals how phonon-spin scattering contributed by unpaired f-electrons in U atoms influences thermal transport in the U-ThO 2 system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dynamic Boundary Layer Simulation of Pulsed CO 2 Electrolysis on a Copper Catalyst

Pulsed electrolysis has been demonstrated to improve the faradaic efficiency (FE) to C 2+ products during the electrochemical reduction of CO 2 over a Cu catalyst, but the nature of this enhancement is poorly understood. Herein, we developed a time-dependent continuum model of pulsed CO 2 electrolysis on Cu in 0.1 M CsHCO 3 that faithfully represents the experimentally observed effects of pulsed electrolysis. This work shows that pulsing results in dynamic changes in the pH and CO 2 concentration near the Cu surface, which lead to an enhanced C 2+ FE as a consequence of repeatedly accessing a transient state of heightened pH and CO 2 concentration at high cathodic overpotential. Using these insights, a variety of pulse shapes were explored to establish operating conditions that maximize the rate of C 2+ product formation and minimize the rates of H 2 and C 1 product formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Atomically Dispersed Zn/Co–N–C as ORR Electrocatalysts for Alkaline Fuel Cells

Hydrogen fuel cells have drawn increasing attention as one of the most promising next-generation power sources for future automotive transportation. Developing efficient, durable, and low-cost electrocatalysts, to accelerate the sluggish oxygen reduction reaction (ORR) kinetics, is urgently needed to advance fuel cell technologies. Herein, we report on metal–organic frameworks-derived nonprecious dual metal single-atom catalysts (SACs) (Zn/Co–N–C), consisting of Co–N 4 and Zn–N 4 local structures. These catalysts exhibited superior ORR activity with a half-wave potential (E 1/2 ) of 0.938 V versus RHE (reversible hydrogen electrode) and robust stability (ΔE 1/2 = –8.5 mV) after 50k electrochemical cycles. Moreover, this remarkable performance was validated under realistic fuel cell working conditions, achieving a record-high peak power density of ~1 W cm –2 among the reported SACs for alkaline fuel cells. Operando X-ray absorption spectroscopy was conducted to identify the active sites and reveal catalytic mechanistic insights. The results indicated that the Co atom in the Co–N 4 structure was the main catalytically active center, where one axial oxygenated species binds to form an O ads –Co–N 4 moiety during the ORR. In addition, theoretical studies, based on a potential-dependent microkinetic model and core-level shift calculations, showed good agreement with the experimental results and provided insights into the bonding of oxygen species on Co–N 4 centers during the ORR. Furthermore, this work provides a comprehensive mechanistic understanding of the active sites in the Zn/Co–N–C catalysts and will pave the way for the future design and advancement of high-performance single-site electrocatalysts for fuel cells and other energy applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Microcanonical Kinetics of Water-Mediated Proton Transfer in 4ABAH + ·(H 2 O) n = 4–6 Clusters (ABA = Aminobenzoic Acid): A Model System for Size-Dependent Relaxation to Ergodic Behavior

Here, we leverage the unique properties of the 4ABAH + · (H 2 O) n clusters (ABA = 4-aminobenzoic acid, n = 4−6) to quantitatively address how a finite, isolated system evolves into an ergodic condition starting from localized arrangements in configuration space. This system adopts two distinct structural isomers in which water molecules cluster around the cationic centers of its two protomers with widely separated positive charge centers. These isomers arise from excess proton attachment to either the acid (O) or amino (N) group on opposite sides of the benzene ring. Both forms are captured and kinetically trapped using cryogenic ion methods and then selectively vibrationally excited through their mutually exclusive IR bands involving NH and OH stretching fundamentals. Because the IR excitation lies below the water binding energy, the system can evolve to explore slow, rare events that lead to the interconversion between the two isomers. The rates of these intracluster reactions are determined by using a pump−probe scheme involving ∼5 ns IR pump and UV probe lasers. The rates occur on the microsecond time scale, leading to steady state populations of the isomers, thus revealing the cluster size-dependent fractionation between the two species at microcanonical equilibrium. The steady state distributions are correlated with the expected trend in the cluster size-dependent reaction energetics, which are in turn consistent with changes in the relative densities of states of the two species. These results thus provide an unusually clear example in which complex, protic-solvent-mediated chemical transformations are captured within a finite system at a precisely determined internal energy.

Rana, Abhijit [Yale Univ., New Haven, CT (United S↗
DOE OSTI2025

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

The Kβ X-ray emission spectrum of 3 d transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, and has become crucial for understanding their spin and oxidation states. The spectrum is commonly treated using crystal-field multiplet theory, a semi-empirical theory that uses tunable parameters to control the strength of the effects present in X-ray emission spectroscopy (XES). However, determining the experimental values of these parameters remains a challenge. We present a methodology that applies Bayesian optimization to crystal-field multiplet theory to determine parameter values. The algorithm is tested on the X-ray emission spectra of a collection of Mn, Co, and Ni oxides. We are able to find optimal values for the four most impactful parameters: Slater−Condon reduction factors F dd , F pd , and G pd , and crystal field splitting 10 Dq . The algorithm produces significantly improved accuracy compared to current analysis methods, and probes interparameter dependencies by modeling the error landscape. This advancement enhances XES analysis by offering an approach of obtaining quantitative electronic structural information on 3 d transition metal valence shells, facilitating applications across various scientific fields.

Bayesian optimization↗
DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chemical templates of the Central Molecular Zone

Context . The Central Molecular Zone (CMZ) of the Milky Way exhibits extreme conditions, including high gas densities, elevated temperatures, enhanced cosmic-ray ionization rates, and large-scale dynamics. This makes it a perfect laboratory for astrochemical studies. With large-scale molecular surveys revealing increasing chemical and physical complexity in the CMZ, it is essential to develop robust methods to decode the chemical information embedded in this extreme region. Aims . A key step to interpreting the molecular richness found in the CMZ is building chemical templates tailored to its diverse conditions. In particular, understanding how CMZ environments affect shock and protostellar chemistry is crucial. The combined impact of high ionization, elevated temperatures, and dense gas remains insufficiently explored for observable tracers. Methods . For this study, we utilized UCLCHEM , a gas-grain time-dependent chemical model, to link physical conditions with their corresponding molecular signatures and identify key tracers of temperature, density, ionization, and shock activity. To achieve this, we ran a grid of models of shocks and protostellar objects representative of typical CMZ conditions, focusing on 24 species, including complex organic molecules. Results . Shocked and protostellar environments show distinct evolutionary timescales (≲10 4 vs. ≳10 4 years); 300 K emerges as a key temperature threshold for chemical differentiation. We find that cosmic-ray ionization and temperature are the main drivers of chemical trends. HCO + , H 2 CO, and CH 3 SH trace ionization, while HCO, HCO + , CH 3 SH, CH 3 NCO, and HCOOCH 3 show consistent abundance contrasts between shocks and protostellar regions over similar temperature ranges. Conclusions . We characterized the behavior of 24 species in protostellar and shock-related environments. While our models underpredict some complex organics in shocks, they reproduce observed trends for most species, supporting scenarios involving a need for recurring shocks in Galactic Center clouds and enhanced ionization toward Sgr B2(N2). Future work should assess the role of shock recurrence and metallicity in shaping chemistry.

Galaxy: center↗
DOE OSTI2021

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles

The message passing neural network (MPNN) framework is a promising tool for modeling atomic properties but is, until recently, incompatible with directional properties, such as Cartesian tensors. We propose a modified Cartesian MPNN (CMPNN) suitable for predicting atom-centered multipoles, an essential component of ab initio force fields. The efficacy of this model is demonstrated on a newly developed dataset consisting of 46 623 chemical structures and corresponding high-quality atomic multipoles, which was deposited into the publicly available Molecular Sciences Software Institute QCArchive server. We show that the CMPNN accurately predicts atom-centered charges, dipoles, and quadrupoles and that errors in the predicted atomic multipoles have a negligible effect on multipole–multipole electrostatic energies. The CMPNN is accurate enough to model conformational dependencies of a molecule’s electronic structure. This opens up the possibility of recomputing atomic multipoles on the fly throughout a simulation in which they might exhibit strong conformational dependence.

Glick, Zachary L. (ORCID:0000000309002849)↗
DOE OSTI2024

Effect of a collapsing gas bubble on the shock-to-detonation transition in liquid nitromethane

We studied the shock-induced collapse of butane gas bubbles in the homogeneous explosive nitromethane (NM) to investigate the effects of hot spot formation on the detonation process. A butane bubble was injected into a sample of NM, and a shock wave from a flat plate impactor compressed the bubble, creating a localized hot spot. We measured shock and detonation wave speeds with optical velocimetry, and we used a high-speed camera to image the shock propagation and bubble collapse processes. A multiband optical fiber pyrometer measured the time-resolved thermal radiance, and we used the results and emissivity values extracted from spectral fits to estimate temperatures. We measured the characteristics of the shock-to-detonation transition in NM with and without a bubble. All experiments were performed at shock pressures near 8 GPa, where neat NM can detonate. A single bubble in this system was shown to sensitize NM, leading to a reduced run-to-detonation time. We used hydrodynamic modeling to predict shock wave propagation, the extent of chemical reaction, and subsequent temperature rise from the collapsing bubble. We used a temperature-dependent Arrhenius burn model for simulations, and it yielded much better results than reactive burn models that depend only on pressure and density.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Pedestal main ion particle transport inference through gas puff modulation with experimental source measurements

Abstract Transport in the DIII-D high confinement mode (H-mode) pedestal is investigated through a periodic edge gas puff modulation (GPM) which perturbs the deuterium density and source profiles. By using absolutely calibrated experimental edge ionization profile measurements, radial profiles of diffusion ( D ) and convection ( v ) are calculated into the pedestal region without depending on modeling the edge ionization source. An analytic approach with closed-form expressions for the D and v profiles and a more advanced Bayesian approach show evidence of an inward particle convection on the order of 1 m s −1 extending to normalized poloidal flux ( Ψ N ) of 0.98. Meanwhile, diffusion reaches a minimum value of ( 0.03 ± 0.02 ) m 2 s −1 in the pedestal region. Notably, the Bayesian approach, which utilizes the Aurora 1.5 D forward model inside the IMPRAD OMFIT module, provides radially resolved transport profiles with associated uncertainty without requiring an explicit form for the perturbation to the density profile or source. The combination of experimental ionization measurements and Bayesian inference provides an enhanced robust framework for investigating edge particle transport coefficients to experimentally test transport physics in order to improve predictive capabilities in the tokamak edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Robust inference of the Galactic Centre gamma-ray excess spatial properties

ABSTRACT The gamma-ray Fermi-LAT Galactic Centre excess (GCE) has puzzled scientists for over 15 yr. Despite ongoing debates about its properties, and especially its spatial distribution, its nature remains elusive. We scrutinize how the estimated spatial morphology of this excess depends on models for the Galactic diffuse emission, focusing particularly on the extent to which the Galactic plane and point sources are masked. Our main aim is to compare a spherically symmetric morphology – potentially arising from the annihilation of dark matter (DM) particles – with a boxy morphology – expected if faint unresolved sources in the Galactic bulge dominate the excess emission. Recent claims favouring a DM-motivated template for the GCE are shown to rely on a specific Galactic bulge template, which performs worse than other templates for the Galactic bulge. We find that a non-parametric model of the Galactic bulge derived from the VISTA Variables in the Via Lactea survey results in a significantly better fit for the GCE than DM-motivated templates. This result is independent of whether a galprop-based model or a more non-parametric ring-based model is used to describe the diffuse Galactic emission. This conclusion remains true even when additional freedom is added in the background models, allowing for non-parametric modulation of the model components and substantially improving the fit quality. When adopted, optimized background models provide robust results in terms of preference for a boxy bulge morphology for the GCE, regardless of the mask applied to the Galactic plane.

Astronomy & Astrophysics↗
DOE OSTI2023

Polyhexatic and polycrystalline states of skyrmion lattices

We report Monte Carlo studies of lattices of up to 10 5 skyrmions treated as particles with negative core energy and repulsive interaction obtained from a microscopic spin model. Temperature dependence of translational and orientational correlations has been investigated for different experimental protocols and initial conditions. Cooling the skyrmion liquid from a fully disordered high-temperature state results in the formation of a skyrmion polycrystal. A perfect skyrmion lattice prepared at T = 0, on raising temperature undergoes a first-order melting transition into a polyhexatic state that consists of large orientationally ordered domains of fluctuating shape. On the further increasing temperature, these domains decrease in size, leading to a fully disordered liquid of skyrmions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effective J -factors for Milky Way dwarf spheroidal galaxies with velocity-dependent annihilation

We calculate the effective J-factors, which determine the strength of indirect detection signals from dark matter annihilation, for 25 dwarf spheroidal galaxies (dSphs). We consider several well-motivated assumptions for the relative velocity dependence of the dark matter annihilation cross section: σAv: s -wave (velocity independent), p -wave (σ A v∝v 2 ), d-wave (σ A v∝v 4 ), and Sommerfeld-enhancement in the Coulomb limit (σ A v∝1/v). As a result we provide the largest and most updated sample of J-factors for velocity-dependent annihilation models. For each scenario, we use Fermi-LAT gamma-ray data to constrain the annihilation cross section. Due to the assumptions made in our gamma-ray data analysis, our bounds are comparable to previous bounds on both the p-wave and Sommerfeld-enhanced cross sections using dSphs. Finally, our bounds on the d-wave cross section are the first such bounds using indirect detection data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Dark energy with a triplet of classical U(1) fields

Here, we present a new mechanism for cosmic acceleration consisting of a scalar field coupled to a triplet of classical U(1) gauge fields. The gauge fields are arranged in a homogeneous, isotropic configuration, with both electric- and magneticlike vacuum expectation values. The gauge fields provide a masslike term via a Chern-Simons interaction that suspends the scalar away from its potential minimum, thereby enabling potential-dominated evolution. We show this mechanism can drive a brief period of acceleration, such as dark energy, without the need for fine-tunings. We obtain simple analytic results for the dark energy equation of state and dependence on model parameters. In this model, the presence of the gauge field generically leads to a suppression of long-wavelength gravitational waves, with implications for the experimental search for cosmic microwave background B modes and direct detection of a stochastic gravitational wave background.

79 ASTRONOMY AND ASTROPHYSICS↗