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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Emergent Inductance from Chiral Orbital Currents in a Bulk Ferrimagnet

Here, we report the discovery of a new form of inductance in the bulk ferrimagnet Mn 3 ⁢Si 2 ⁢Te 6 , which features strong spin-orbit coupling, large magnetic anisotropy, and pronounced magnetoelastic interactions. Below its Curie temperature (𝑇 𝐶 ≈ 78 K), Mn 3 ⁢Si 2 ⁢Te 6 hosts chiral orbital currents (COC) that circulate within the crystal lattice and give rise to collective electronic behavior [Control of chiral orbital currents in a colossal magnetoresistance material, Nature (London) 611, 467 (2022).]. By applying a magnetic field along the hard 𝑐 axis and driving the system with low-frequency currents, we uncover a giant inductive response up to millhenry scale, originating from first-order reconfigurations of COC domains. These domains act as coherent mesoscopic inductive elements that resist reversal upon current reduction, producing a large electromotive force and sharply increasing voltage. This emergent inductance defies classical models, occurs without superconductivity or engineered nanostructures, and opens a new frontier in orbital-based quantum functionality and device concepts.

complex materials↗
DOE OSTI2024

Performance of explicit and IMEX MRI multirate methods on complex reactive flow problems within modern parallel adaptive structured grid frameworks

Large-scale multiphysics simulations are computationally challenging due to the coupling of multiple processes with widely disparate time scales. The advent of exascale computing systems exacerbates these challenges since these systems enable ever-increasing size and complexity. In recent years, there has been renewed interest in developing multirate methods as a means to handle the large range of time scales, as these methods may afford greater accuracy and efficiency than more traditional approaches of using implicit-explicit (IMEX) and low-order operator splitting schemes. However, to date there have been few performance studies that compare different classes of multirate integrators on complex application problems. In this work, we study the performance of several newly developed multirate infinitesimal (MRI) methods, implemented in the SUNDIALS solver package, on two reacting flow model problems built on structured mesh frameworks. The first model revisits prior work on a compressible reacting flow problem with complex chemistry that is implemented using BoxLib but where we now include comparisons between a new explicit MRI scheme with the multirate spectral deferred correction (SDC) methods in the original paper. The second problem uses the same complex chemistry as the first problem, combined with a simplified flow model, but runs at a large spatial scale where explicit methods become infeasible due to stability constraints. Two recently developed IMEX MRI multirate methods are tested. These methods rely on advanced features of the AMReX framework on which the model is built, such as multilevel grids and multilevel preconditioners. The results from these two problems show that MRI multirate methods can offer significant performance benefits on complex multiphysics application problems and that these methods may be combined with advanced spatial discretization to compound the advantages of both.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-temperature thermal conductivity measurements of macro-porous graphite

Graphite is a unique material for high temperature applications and will likely become increasingly important as we attempt to electrify industrial applications. Here, we investigate the thermal properties of low-quality, macro-porous graphite to determine the tradeoff between quality and thermal performance. We use laser flash analysis (LFA) to measure the thermal diffusivity of graphite at high temperatures. Due to the large pores in the graphite samples preventing uniform laser flash heating, we first apply a thick coating to achieve the required flat, parallel surfaces for LFA measurements. We then develop a methodology to extract properties of the sample from the diffusivity measurements, based on finite element modeling of a variety of sample/coating interface profiles. Validating the methodology against a reference sample demonstrates a mean absolute percentage error of 8.5%, with potential improvement with better sample characterization. We show low-quality graphite has a thermal conductivity of ~10 W/m/K up to 1000 °C, which is an order of magnitude lower than high-quality graphite, but contributions from photon conductivity may result in higher conductivities at higher temperatures. Overall, we demonstrate an approach for measuring thermal properties of macro-porous materials at high temperatures, and apply the approach to measuring thermal conductivity of porous graphite, which will aid in the design of hightemperature systems for cost-competitive decarbonization.

Verma, Shomik↗
DOE OSTI2026

Assessing shellfish water exposure to fecal bacteria pollution in Salish Sea: three-dimensional modeling and implications for monitoring

Fecal bacteria (FB) contamination poses significant risks to shellfish safety and management in coastal and estuarine waters. Despite extensive pollution identification and correction efforts, FB contamination in shellfish-growing areas persists in the Salish Sea, highlighting the need to identify overlooked sources and better understand FB transport from riverine and shoreline inputs to shellfish beds. To address this, a high-resolution three-dimensional hydrodynamic model coupled with FB kinetics was developed and applied to a case study site in Salish Sea—Portage Bay—to simulate freshwater plume circulation, flushing dynamics, and bacterial transport. Daily FB loading from the major freshwater inflow—Nooksack River was generated by both linear interpolation and integrating a machine learning approach (XGBoost), trained on historical hydrological and meteorological data. The model successfully reproduced both the magnitude and seasonal variation of FB concentrations in Portage Bay for the year of 2021, demonstrating that simplified FB kinetics with first-order decay due to mortality was effective in this dynamic coastal environment with short flushing time. Model results identified the Nooksack River as the dominant far-field FB source, while scenario simulations showed that near-field coastal stormwater outfalls elevated local FB levels following rainfall, particularly under low-flow conditions. The XGBoost prediction provided comparable or superior accuracy to linear interpolation, particularly during periods of missing observational data, by capturing short-term variability and event-driven loading more effectively. Integrating data-driven riverine FB inputs with mechanistic coastal numerical modeling provides a robust framework for operational forecasting of shellfish bed exposure risk and supports adaptive monitoring and management of shellfish growing areas in the Salish Sea and similar coastal systems.

Salish Sea↗
DOE OSTI2025

Glimpsing Physics of Nano-Hz Gravitational Waves in Neutrinos from Core-Collapse Supernovae

The growing evidence for nano-hertz gravitational waves, from NANOGrav and other observations, may be pointing to a cosmological first-order phase transition at temperatures of $\mathcal{O}(10-100)\;\mathrm{MeV}$. Such an interpretation requires dynamics beyond the Standard Model in this energy range. If so, it may well be the case that core-collapse supernova explosions would recreate the first-order phase transition leaving a unique imprint on the spectrum of neutrinos emitted in the initial few seconds. This scenario is also suggestive of a low-mass seesaw mechanism to explain neutrino masses. We outline the prospects for future observations of Galactic supernovae to uncover the signals of this scenario, which could get further confirmation with additional pulsar timing array data establishing the primordial origin of the observed nano-hertz gravitational waves.

Davoudiasl, Hooman [Brookhaven] (ORCID:00000003348↗
DOE OSTI2021

Finite-rate entrainment effects on nitrogen oxide (NO x ) emissions in staged combustors

Axially staged combustors offer the potential for achieving low NO x emissions from gas turbines at the elevated temperatures required to achieve at least 65% combined cycle efficiencies. While there is evidence that premixing between the main burner products and secondary stream reduces NO x , it is important to characterize the specific requirements needed to achieve low emissions at gas turbine conditions. This study examines the sensitivity of NO x to finite-rate, large-scale entrainment of the main and secondary streams under the simplifying assumption of infinitely-fast small-scale mixing. Essentially, this allows us to isolate the effects of limited large-scale entrainment rates by using a homogeneous/uniform condition for the entrained and reacting gases. We use a reduced-order reactor network model to examine a generic staged-combustor whose inputs are physical time scales that embody the finite-rate entrainment characteristics. With simulations conducted over a large parameter space at typical operating conditions (25 atm, 650 K air), the results show that, even when small-scale mixing is infinitely fast and the reaction zone is uniform, entrainment significantly affects NO x emissions due to its influence on the equivalence ratio — and thus the temperature and time — at which the entrained mixture ignites. Fuel-air staging is shown to be vital to NO x reduction in most practical cases where entrainment times exceed roughly 1 ms and the secondary stream finishes entraining before the main burner products. A constrained NO x minimization indicates that using the leanest possible main burner and re-routing as much air as possible to the secondary stage is key to low NO x with finite-rate entrainment; for example, less than 10 ppm NO x can be achieved if the secondary stage contains 20% of the total mass flow.

03 NATURAL GAS↗
DOE OSTI2023

Defect-specific strength factors and superposition model for predicting strengthening of ion irradiated Fe18Cr alloy

Here, a high-purity binary alloy Fe18Cr was subjected to heavy-ion irradiation in order to provide improved understanding of the irradiation effect on radiation hardening associated with dislocation loop and network formation, α’ precipitation. The specimens were irradiated with 8 MeV Fe ions (~2 μm ion range) to midrange doses of 0.37 and 3.7 dpa at 300, 350 and 450 °C using dose rates of ~10 –5 –10 –3 dpa/s. Nanoindentation testing was performed to extract the bulk equivalent hardness at low depth from these ion irradiated specimens. High-quality transmission electron microscopy (TEM) images were acquired by utilizing a flash electropolishing method on the Focused Ion Beam (FIB) prepared liftouts. An accurate calculation of the strength factor was provided based on detailed TEM characterization of the irradiated microstructures. A newly refined hardening superposition method was applied to combine the strengthening components from each microstructure. The good agreement between microstructure predicted strength and measured strength further demonstrated the fidelity to use hardening model to quantify the mechanical properties of ion irradiated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Non-perturbative renormalization scheme for the C P -odd three-gluon operator

We define a regularization-independent momentum-subtraction scheme for the C P -odd three-gluon operator at dimension six. This operator appears in effective field theories for heavy physics beyond the Standard Model, describing the indirect effect of new sources of C P -violation at low energies. In a hadronic context, it induces permanent electric dipole moments. The hadronic matrix elements of the three-gluon operator are non-perturbative objects that should ideally be evaluated with lattice QCD. We define a non-perturbative renormalization scheme that can be implemented on the lattice and we compute the scheme transformation to \( \overline{\mathrm{MS}} \) at one loop. Our calculation can be used as an interface to future lattice-QCD calculations of the matrix elements of the three-gluon operator, in order to obtain theoretically robust constraints on physics beyond the Standard Model from measurements of the neutron electric dipole moment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2022

Catalytic co-pyrolysis of LDPE and PET with HZSM-5, H-beta, and HY: experiments and kinetic modelling

In this study, the catalytic pyrolysis of low-density polyethylene (LDPE), polyethylene terephthalate (PET) and their mixture (1 : 1 wt/wt) with three zeolite catalysts (HZSM-5, H-beta, HY) was investigated using a thermogravimetric analyzer (TGA) and a Pyroprobe® micro-reactor coupled to a gas chromatograph mass spectrometer (Py-GC/MS). The TGA results demonstrated that during pyrolysis at 10 °C min –1 , on average, zeolite catalysts decreased the maximum decomposition temperature by 149 °C for LDPE while only decreasing by 8 °C for PET. The derivative thermogravimetric (DTG) curve evidenced interactions when the two polymers were catalytically co-pyrolyzed for all the three catalysts. A lumped nth order reaction scheme was able to accurately model both non-catalytic and catalytic pyrolysis and co-pyrolysis by using the least squares fitting approach for determining the kinetic parameters. The kinetic model was able to model well the interaction effects observed during catalytic co-pyrolysis of LDPE and PET with HZSM-5, H-beta, and HY (Fit% Wt% > 96%, Fit% DTG > 94%). Py-GC/MS experiments for the catalytic fast pyrolysis of LDPE revealed that HZSM-5 resulted in the highest selectivity to aromatic hydrocarbons (31.6%) and HY resulted in the highest selectivity to gasoline range C 5 –C 10 paraffins and olefins (40.9%). Catalytic fast pyrolysis of PET showed high selectivity to benzene for all the catalysts (>43%) and that HZSM-5 resulted in the highest selectivity to polyaromatic hydrocarbons (24.7%). Finally, the catalytic fast co-pyrolysis of LDPE and PET revealed interaction effects for all the three catalysts evidenced by a positive synergy% for alkylated benzenes (3–142%) and polyaromatics (105–187%) with a concomitant negative synergy% for benzene (24–36%) and C 5 –C 10 paraffins and olefins (27–53%).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Proton Selective Nanoporous Atomically Thin Graphene Membranes for Vanadium Redox Flow Batteries

Angstrom-scale proton-selective pores in atomically thin 2D materials present fundamentally new opportunities for advancing proton exchange membranes (PEMs). Vanadium Redox Flow Batteries (VRFBs) for grid-scale energy storage require PEMs with high areal proton conductance (>1 S cm −2 ) and minimal vanadium ion (VO 2+ ) crossover. However, state-of-the-art Nafion 212 membranes (N212 ≈50 µm thick), suffer from persistent VO 2+ crossover reducing performance and efficiency. Here, a layered PEM is demonstrated, comprising monolayer CVD graphene with Angstrom-scale proton-selective pores introduced via Ar plasma, integrated with an ultra-thin ≈300 nm polybenzimidazole (PBI) layer and sandwiched between two Nafion 211 (25 µm thick) layers. The layered architecture facilitates scalable membrane fabrication by mitigating defects while processing and facile stacking of graphene layers allows stochastic non-selective defect isolation enabling exceptionally low VO 2+ crossover (selectivity (H + areal conductance / VO 2+ permeability) ≈6709 × 10 6 S min cm −4 ), with proton conductance >8 S cm −2 . Systematic transport experiments supported by resistance-based transport modelling elucidate the role of defect size, defect isolation, and sealing, as well as layering/stacking, to enable orders of magnitude (>671× over N212) improvements in selectivity, along with areal proton conductance >8 S cm −2 . This work highlights the potential of atomic-scale proton-selective defect engineering in 2D materials, in conjunction with facile stacking and layering of materials as strategies for scalable, high-performance advances in PEMs for energy, electrochemical, and separation applications beyond VRFBs.

Chaturvedi, Pavan [Vanderbilt Univ., Nashville, TN↗
DOE OSTI2022

Large-eddy simulation of non-vaporizing sprays using the spectral-element method

Predictive simulations of high-pressure sprays require accurate representation of the turbulent gaseous flow field generated by liquid jet. Typically, the accuracy that can be obtained with low-order numerical methods (e.g. finite volume, finite element) is limited by stability issues in fine grids and the order of convergence of the method. In this work, we resolve the turbulent flow field in an Eulerian manner using the high-order spectral element method, coupled with a Lagrangian parcels approach to model the atomizing liquid jet. Large eddy simulations of single-hole sprays under non-evaporative conditions were conducted and compared against experimental data from Margot et al. (2008) and Spray A data from the Engine Combustion Network. The sensitivity of liquid penetration and droplet sizes to different breakup model parameters was studied. The effect of different numerical parameters, such as polynomial order of the solution (grid resolution), on liquid penetration was also analyzed. The method achieved grid-independent results using p-refinement, achieving finer resolution (by a factor of x 1.7 - x 3.5) in the gas-phase solution than in state-of-the-art simulations using the finite-volume method. Here the results showed good agreement with experimental data, demonstrating the ability of the current method to accurately capture liquid penetration and the shape of the spray.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantitative Account of the Bonding Properties of a Rubredoxin Model Complex [Fe(SCH3)4]q, q = -2, -1, +2, +3

Iron-sulfur clusters play important roles in biology as parts of electron transfer chains and catalytic cofactors. Here, we report a detailed computational analysis of a structural model of the simplest natural iron-sulfur cluster of rubredoxin and its cationic counterparts. Specifically, we report results for the ground and low-lying electronically excited states of the complex [Fe(SCH3)4]2-/1-/2+/3+, using Multi-Reference (CASSCF, MRCISD), and Coupled Cluster [CCSD(T)] methodology in order to provide accurate adiabatic reduction energies, dissociation energies and insights into the bonding analysis. The nature of the Fe-S chemical bond and the magnitude of the ionization potentials in the anionic and cationic [Fe(SCH3)4] complexes offer a physical rationale for the relative stabilization, structure and speciation of these complexes. Anionic and cationic complexes present different types of chemical bonds: prevalently ionic in [Fe(SCH3)4]2-/1- complexes and covalent in [Fe(SCH3)4]2+/3+ complexes. The ionic bonds result in an energy gain for the transition [Fe(SCH3)4]2-®[Fe(SCH3)4]- (i.e., FeII®FeIII) of 1.5 eV, while the covalent bonds result in an energy loss for the transition [Fe(SCH3)4]2+®[Fe(SCH3)4]3+ of 16.6 eV, almost half of the IP of Fe2+. The ionic vs covalent bond character influences the Fe-S bond strength and length, i.e., ionic Fe-S bonds are longer than covalent ones by about 0.2 Å (for FeII) and 0.04 Å (for FeII). Finally, the average Fe-S heterolytic bond strength is 6.7 eV (FeII) and 14.6 (FeIII) eV at the RCCSD(T) level of theory.

Tzeli, Demeter↗
DOE OSTI2025

High harmonic generation in Haldane model quantum dots

We study theoretically the nonlinear electron dynamics of Haldane model quantum dots placed in the field of an ultrashort optical pulse. One of the tuning parameters of the Haldane model is the phase accumulated by an electron during its transfer between the next nearest neighbor sites. We study how this parameter affect the nonlinear electron dynamics and the generation of high optical harmonics. With increasing the phase from its zero value to 90°, the low-energy electron states in the conduction and valence bands become more localized near the edges of the quantum dot resulting in suppression of the band gap, enhancement of the dipole inter-band coupling, and strong suppression of the average low-energy electron density of the states. As a result, the nonlinear response of the electron system of a Haldane model quantum dot is the strongest at intermediate values of the phase, ≈ 30− 40°. At these values of the phase, the electron dynamics is irreversible and a few first high-order harmonics have the largest intensities. Here, when the phase approaches 90° value the generation of high harmonics is strongly suppressed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Fractons from Vector Gauge Theory

Motivated by the prediction of fractonic topological defects in a quantum crystal, we utilize a reformulated elasticity duality to derive a description of a fracton phase in terms of coupled vector U(1) gauge theories. The fracton order and restricted mobility emerge as a result of an unusual Gauss law where electric field lines of one gauge field act as sources of charge for others. At low energies this vector gauge theory reduces to the previously studied fractonic symmetric tensor gauge theory. Here, we construct the corresponding lattice model and a number of generalizations, which realize fracton phases via a condensation of stringlike excitations built out of charged particles, analogous to the p-string condensation mechanism of the gapped X-cube fracton phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

The NANOGrav 15 yr Data Set: Search for Signals from New Physics

The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗