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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum fisher information reveals UV-IR mixing in the strange metal

The density-density response in optimally doped Bi 2 Sr 2 CaCu 2 O 8+$x$ has recently been shown to exhibit conformal symmetry. Using, the experimentally inferred conformal dynamic susceptibility, we compute the resultant quantum Fisher information (QFI), a witness to multi-partite entanglement. For a Fermi liquid, we find that the QFI grows quadratically as the temperature increases, consistent then with the phase space available for scattering in the standard theory of metals. By contrast, the QFI in a strange metal increases as a power law at as the temperature decreases, but ultimately extrapolates to a constant at T=0. The constant is of the form, ω$^{2Δ}_g$, where Δ is the conformal dimension and ω g is the UV cutoff which is on the order of the pseudogap. As this constant depends on both UV and IR properties, it illustrates that multipartite entanglement in a strange metal exhibits UV-IR mixing, a benchmark feature of doped Mott insulators as exemplified by dynamical spectral weight transfer. We conclude with a discussion of the implication of our results for low-energy reductions of the Hubbard model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Prediction of Performance Variation Caused by Manufacturing Tolerances and Defects in Gas Diffusion Electrodes of Phosphoric Acid (PA)–Doped Polybenzimidazole (PBI)-Based High-Temperature Proton Exchange Membrane Fuel Cells

The automated process of coating catalyst layers on gas diffusion electrodes (GDEs) for high-temperature proton exchange membrane fuel cells results inherently into a number of defects. These defects consist of agglomerates in which the platinum sites cannot be accessed by phosphoric acid and which are the consequence of an inconsistent coating, uncoated regions, scratches, knots, blemishes, folds, or attached fine particles—all ranging from μm to mm size. These electrochemically inactive spots cause a reduction of the effective catalyst area per unit volume (cm2/cm3) and determine a drop in fuel cell performance. A computational fluid dynamics (CFD) model is presented that predicts performance variation caused by manufacturing tolerances and defects of the GDE and which enables the creation of a six-sigma product specification for Advent phosphoric acid (PA)-doped polybenzimidazole (PBI)-based membrane electrode assemblies (MEAs). The model was used to predict the total volume of defects that would cause a 10% drop in performance. It was found that a 10% performance drop at the nominal operating regime would be caused by uniformly distributed defects totaling 39% of the catalyst layer volume (~0.5 defects/μm2). The study provides an upper bound for the estimation of the impact of the defect location on performance drop. It was found that the impact on the local current density is higher when the defect is located closer to the interface with the membrane. The local current density decays less than 2% in the presence of an isolated defect, regardless of its location along the active area of the catalyst layer.

Gurau, Vladimir (ORCID:0000000327429061)↗
DOE OSTI2025

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Dynamic Modeling of Near Isothermal Compressor for Transcritical Carbon Dioxide Cycle

Compressors are the major energy consumption components in vapor compression systems, drawing much research effort in reducing carbon emissions. The isothermal compressor integrates the compressor chamber and gas cooler to achieve near isothermal compression, reaching up to 30% energy reduction compared to the traditional isentropic compression work. This paper presents a detailed isothermal compressor model combined with a generalized liquid piston model to account for the carbon dioxide (CO2) isothermal compression process. The model is established based on MATLAB environment. The model uses the real experimental data as boundary conditions and initial settings, which also considers the CO2 solubility in liquid piston (mineral oil) for designing, optimizing and customizing the compression chambers. The validation was carried out with experimental data using a prototype with 3.5 kW capacity. The results have demonstrated the accuracy of the dynamic model (6.2% relative error for chamber pressure and 0.5 K deviation for chamber temperature), which provide a guideline for designing and customizing the isothermal compression cycle.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of Martini 2.2 parameters for N -glycans: a case study of the HIV-1 Env glycoprotein dynamics

N-linked glycans are ubiquitous in nature and play key roles in biology. For example, glycosylation of pathogenic proteins is a common immune evasive mechanism, hampering the development of successful vaccines. Due to their chemical variability and complex dynamics, an accurate molecular understanding of glycans is still limited by the lack of effective resolution of current experimental approaches. Here, we have developed and implemented a reductive model based on the popular Martini 2.2 coarse-grained force field for the computational study of N-glycosylation. We used the HIV-1 Env as a direct applied example of a highly glycosylated protein. Finally, our results indicate that the model not only reproduces many observables in very good agreement with a fully atomistic force field but also can be extended to study large amount of glycosylation variants, a fundamental property that can aid in the development of drugs and vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Reconceptualizing the Ir III Role in Metallaphotoredox Catalysis: From Strong Photooxidant to Potent Energy Donor

Dual Ir III /L n Ni II metallaphotoredox catalyzed C(sp 3 )–C(sp 2 ) cross-coupling reactions are widely assumed to proceed by photoinduced single electron transfer steps due to the highly oxidizing Ir III * excited state (Ir III = [Ir(dF(CF 3 )ppy) 2 (dtbbpy)] + [PF 6 ] – ; dF(CF 3 )ppy = 2-(2,4-difluorophenyl)-5-(trifluoromethyl)pyridine; L n = dtbbpy = 4,4'-di-tert-butyl-2,2'-bipyridine). Using time-resolved absorption and emission spectroscopy, we reveal that energy transfer between Ir III * and various LnNi II precatalysts and intermediates with k q ≥ 10 8 M –1 s –1 also drives catalysis. Specifically, the excited states of L n Ni II dihalide precatalysts/organometallic intermediates accessible by energy transfer appear to drive bond homolysis, halogen radical elimination, and reductive elimination reactions that facilitate formation of cross-coupled products. Energy transfer dynamics consequently circumvent the need for photoinduced electron transfer, thereby extending substrate scopes to coupling partners that cannot be oxidized by Ir III *. Within a cross-electrophile coupling model reaction between 4-bromobenzotrifluoride and bromocyclohexane, energy transfer activates the L n Ni II precatalyst at early reaction times before nucleophilic reductants are present. In the absence of Ir III , direct excitation of L n Ni II (Br) 2 also activates the precatalyst to form a L n Ni II (Br)(Aryl) intermediate. To compare energy transfer and electron transfer kinetics, we determined rate constants for reductive quenching by Br – (k SET = 4.1 × 10 8 M –1 s –1 ) and for the subsequent electron transfer from reduced Ir III•– to L n Ni II (Br) 2 (k SET = 4.1 × 10 7 M –1 s –1 ) using Stern-Volmer analysis and pulse radiolysis, respectively. Energy transfer rate constants are competitive with the electron transfer rate constants and energy transfer is a parallel pathway within metallaphotoredox catalysis. Exploiting the energy transfer mechanism, we demonstrate highly selective cross-electrophile coupling between 4-chlorobenzotrifluoride and bromocyclohexane to form exclusively cross-coupled product. Here, with alkyl-trifluoroborate nucleophiles that do not reductively quench IrIII* emission, transmetalation with L n Ni II (Br/Cl)(Aryl) followed by energy transfer also drives excited state reductive elimination to form C(sp 3 )–C(sp 2 ) cross-coupled product. Similarly, energy transfer rather than Ni II oxidation drives C(sp 2 )–OR reductive elimination, despite the strongly oxidizing ability of Ir III *. In total, these reactions demonstrate energy transfer processes from Ir III * to L n Ni II in metallaphotoredox catalysis that can unlock alternative reactive pathways.

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

Advanced modeling and simulation of research reactors using dynamic mode decomposition

Full text of publication follows. Due to the ever-increasing safety requirements, the current trend of nuclear reactor analysis is shifting towards high-fidelity multi-physics models, which have a very high computational cost and modelling complexity. As the cost of even a single model run makes it impossible to analyse the behaviour and performance of these models on large-scale commercial plants, it has become even more significant to provide suitable benchmarks to validate and test them extensively. In this sense, research reactors offer a promising solution for the initial validation of high-fidelity models, as they are significantly smaller than commercial reactors and their characteristics are well known. In particular, the reactors of the TRIGA family have been used to assess and validate models and methods for Generation-IV designs, as they have some similar features (such as the dominance of natural convection as cooling mechanism and the difficulties in performing sub-channel analysis using standard codes). Still, the computational requirements of high-fidelity models make them unsuitable for real-time analysis, even following their assessment on research reactors. In this sense, Model Order Reduction (MOR) techniques give an additional strategy to reduce the computational cost of high-fidelity models (whilst preserving sufficient accuracy). In particular, this work focuses on Dynamic Mode Decomposition (DMD), a non-intrusive MOR technique that aims at representing models with explicit temporal dynamics by extracting the time-varying characteristics and the governing structures based only on a set of available data, thus without needing any underlying knowledge of the governing equations. In addition, DMD also computes a low-dimensional surrogate of the dynamic matrix of the system, making it suited for stability analysis and real-time evaluations. This work focuses on the application and validation of the DMD method on the Computational Fluid-Dynamics (CFD) model TRIGA Mark II reactor, also discussing in detail the potentiality of this algorithm as an advanced modelling tool for nuclear reactor analysis. (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Modeling of Liquid Hydrogen Tank Cooled with Para-Orthohydrogen Conversion

With the accelerating development of liquid-hydrogen storage facilities, the problem of boil-off hydrogen losses becomes very important. A promising method to reduce these losses is to utilize the endothermic para-orthohydrogen conversion of vented hydrogen, which can effectively decrease heat loads on a hydrogen tank. To model such a process, a hybrid computational model has been developed, based on the application of computational fluid dynamics for an ullage space, where knowledge of thermal stratification is important, and reduced-order models for other system elements. The simulation results for a spheroidal tank in selected conditions indicated a 10–25% reduction of boil-off losses due to cooling produced by vented hydrogen undergoing para-ortho-conversion.

08 HYDROGEN↗
DOE OSTI2025

Physics-informed transformation toward improving the machine-learned NLTE models of ICF simulations

The integration of machine-learning techniques into inertial confinement fusion (ICF) simulations has emerged as a powerful approach for enhancing computational efficiency. By replacing the costly nonlocal thermodynamic equilibrium (NLTE) model with machine-learning models, significant reductions in calculation time have been achieved. However, determining how to optimize machine-learning-based NLTE models in order to match ICF simulation dynamics remains challenging, underscoring the need for physically relevant error metrics and strategies to enhance model accuracy with respect to these metrics. Thus, we propose novel physics-informed transformations designed to emphasize energy transport, use these transformations to establish new error metrics, and demonstrate that they yield smaller errors within reduced principal-component spaces compared to conventional transformations. Published by the American Physical Society 2025

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Turbulence model reduction by deep learning

A defining problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Microscale mechanical property variations of Al-substituted LLZO: insights from compression testing and molecular dynamics simulations

Ceramic solid electrolytes based on LLZO (Li 7 La 3 Zr 2 O 12 ) are promising candidates for all-solid-state batteries due to their high ionic conductivity and good apparent stability vs. lithium metal, however they are prone to mechanical failure. Lithium metal intrusions, alongside cell stack pressure, transition polycrystalline solid electrolyte grains into a compressed state that promotes crack propagation and fracture. Here this work examines the mechanical response of Al-substituted LLZO to compressive forces by measuring ultimate strength under pillar compression with a flat punch tip. Failure modes characterized by in situ scanning electron microscopy show diverse splitting patterns arising from internal porosity, grain boundaries, and slip planes. Large correlated variations in compressive strength (0.93–2.63 GPa) and Young's modulus (72.1–150.97 GPa) are observed across microscale regions of the solid electrolyte. Molecular dynamics simulations of LLZO with different porosities describe the variation of compressive strength and Young's modulus, and enable a microscale porosity model to be fit accounting for Young's modulus reduction across the solid electrolyte. Overall, the results indicate the importance of microscale mechanical testing of ceramic solid electrolytes to identify preferential sites for mechanical degradation and Li intrusion, and ensure the robust design of all-solid-state lithium metal batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physics-informed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LS-ROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers' equations. A speedup of up to 2.6 for 1D Burgers' and a speedup of 11.7 for 2D Burgers' equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Lastly, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

EPR and 31 P ENDOR Characterization of Pseudo-Jahn–Teller Dynamics and N 2 Activation in Functional Nitrogenase Models, P 3 E M(N 2 ) (M = Fe, Co; E = Si, B, C)

Here, the nominally trigonal, pseudo-Jahn-Teller (PJT)-active, S = ½ N 2 -bound transition-metal complexes, P 3 E M(N 2 ), M = Fe, Co, with three in-plane phosphine-ligands and axial donors, E = Si, B, C, include functional nitrogenase models that catalyze reduction of N 2 to NH 3 . We applied EPR, 31 P ENDOR spectroscopy and DFT computations to characterize the PJT-induced distortions of four selected P 3 E M(N 2 ), revealing how the metal-ion and axial ligand E together tune both PJT dynamics and N 2 activation for reduction. Comparisons reveal an unrecognized correlation between PJT distortion, M-E bond elasticity, and N 2 activation, providing guidelines for designing bioinspired N 2 -reduction catalysts.

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

Modeling Isothermal Reduction of Iron Ore Pellet Using Finite Element Analysis Method: Experiments & Validation

Iron ore pellet reduction experiments were performed with pure hydrogen (H2) and mixtures with carbon monoxide (CO) at different ratios. For direct reduction processes that switch dynamically between reformed natural gas and hydrogen as the reductant, it is important to understand the effects of the transition on the oxide reduction kinetics to optimize the residence time of iron ore pellets in a shaft reactor. Hence, the reduction rates were studied by varying experimental parameters such as the temperature (800, 850 & 900 °C), reactant gas flow rate (100, 150 & 200 cm3/min), pellet size and composition of the reactant gas mixture. The rate of reduction was observed to increase with an increase in temperature and reactant gas flow rate, but it decreased with an increase in pellet size. SEM greyscale analysis was performed to analyze the porosity and phase composition of partially reduced pellets. The porosity of the pellets was observed to increase from 0.3 for unreacted pellet to 0.42 for a completely reduced pellet. Energy-dispersive X-ray spectroscopy (EDAX) analysis was performed to identify the phases observed in the SEM images. The fraction of iron phase was observed to increase from the shell region of the pellet to the core region with an increase in the degree of reduction. A 2D-axisymmetric numerical model was developed on COMSOL Multiphysics, and it was validated using the conversion (X) vs. time curves obtained from each experiment. The model was able to accurately predict the total time needed for the complete conversion of a single iron ore pellet for multiple experiments. Effects of changes in the porosity and tortuosity of the pellet on the model were also studied and the rate of reduction was observed to be sensitive to changes in both porosity and tortuosity. The SEM analysis and the model results show that tortuosity is higher for pellets reduced with H2 than for pellets reduced with H2-CO gas mixtures.

08 HYDROGEN↗
DOE OSTI2020

Forest responses to simulated elevated CO 2 under alternate hypotheses of size- and age-dependent mortality

Elevated atmospheric carbon dioxide (eCO 2 ) is predicted to increase growth rates of forest trees. The extent to which increased growth translates to changes in biomass is dependent on the turnover time of the carbon, and thus tree mortality rates. Size- or age-dependent mortality combined with increased growth rates could result in either decreased carbon turnover from a speeding up of tree life cycles, or increased biomass from trees reaching larger sizes, respectively. However, most vegetation models currently lack any representation of size- or age-dependent mortality and the effect of eCO 2 on changes in biomass and carbon turnover times is thus a major source of uncertainty in predictions of future vegetation dynamics. Using a reduced-complexity form of the vegetation demographic model the Functionally Assembled Terrestrial Ecosystem Simulator to simulate an idealised tropical forest, we find increases in biomass despite reductions in carbon turnover time in both size- and age-dependent mortality scenarios in response to a hypothetical eCO 2 -driven 25% increase in woody net primary productivity (wNPP). Carbon turnover times decreased by 9.6% in size-dependent mortality scenarios due to a speeding up of tree life cycles, but also by 2.0% when mortality was age-dependent, as larger crowns led to increased light competition. Increases in aboveground biomass (AGB) were much larger when mortality was age-dependent (24.3%) compared with size-dependent (13.4%) as trees reached larger sizes before death. In simulations with a constant background mortality rate, carbon turnover time decreased by 2.1% and AGB increased by 24.0%, however, absolute values of AGB and carbon turnover were higher than in either size- or age-dependent mortality scenario. The extent to which AGB increases and carbon turnover decreases will thus depend on the mechanisms of large tree mortality: if increased size itself results in elevated mortality rates, then this could reduce by about half the increase in AGB relative to the increase in wNPP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Quench dynamics of two-component dipolar fermions subject to a quasiperiodic potential

Motivated by recent experiments in fermionic polar gases, we study the nonequilibrium dynamics of two-component dipolar fermions subject to a quasiperiodic potential. We investigate the localization of charge and spin degrees of freedom time evolving with a long-range spin-SU(2) symmetric fermionic Hamiltonian by calculating experimentally accessible dynamical observables. To study the nonequilibrium dynamics, we start the time evolution with two initial states at half-filling: (i) product state with doublons |↑↓0↑↓0↑↓0↑↓0↑↓$\rangle$ and (ii) product state with singlons |↑↓↑↓↑↓↑↓↑↓$\rangle$. We carried out the real-time evolution via the fermionic Hamiltonian using exact diagonalization (ED) and the time-dependent variational principle (TDVP) for finite matrix product states (MPS), within experimentally relevant timescales. For the product state with doublons, we observe a delocalized to localized phase transition varying disorder strengths by monitoring the decay of charge imbalance with time. For the long-range interacting Hamiltonian of our focus, and in the presence of strong enough disorder, starting the time evolution with singlons we find a strong reduction in the spin delocalization, contrary to results of previous studies using the disordered short-range (on-site) Hubbard model with SU(2) symmetry. In addition to the quench dynamics, we also demonstrate the localization of charge and spin in the full energy spectrum of the long-range spin-SU(2) symmetric Hamiltonian by monitoring spin and charge autocorrelation functions. Finally, our predictions for localization of both charge and spin should be observable in ultracold experiments with fermionic dipolar atoms subject to a quasiperiodic potential.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗