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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 OSTI2023

Mechanistic Insights into Aldehyde Production from Electrochemical CO 2 Reduction on CuAg Alloy via Operando X-ray Measurements

CO 2 electrolysis converts the greenhouse gas CO 2 into valuable fuels and chemicals, such as carbon monoxide, ethylene, ethanol, etc. Currently, Cu is the only known monometallic catalyst capable of producing multicarbon products from electrochemical CO 2 reduction reaction (eCO2RR), while the poor selectivity limits its further use. It has been found that introducing Ag atoms into the Cu lattice can modulate product preference. However, the synergistic effects between Cu and Ag, and thus, the catalytic performance, are strongly influenced by catalyst morphology, electrolyzer configuration, reaction conditions, etc. Operando measurements can provide explicit information on the catalyst dynamic variation during the reaction, but their operation and analysis are challenging. Herein, we prepared CuAg multiphase alloy catalysts by magnetron sputtering, which allowed for investigating the intrinsic interaction between Cu and Ag. eCO2RR performance exhibited an improved selectivity toward carbonyls at the expense of hydrogen and hydrocarbons. The partially alloyed Cu and Ag phases were confirmed by operando X-ray diffraction. By means of combining operando X-ray measurements and density functional theory (DFT) calculations, the preferred carbonyl production is attributed to the reduced electron density and compressive strain of Cu due to Ag incorporation, which leads to a deeper d-band center and therefore weakened intermediate adsorption and oxophilicity. In conclusion, this work provides evidence of the intrinsic structural and electronic interaction between Cu and Ag during eCO2RR. The obtained information will facilitate the design of bi/multi-phase metallic or alloy electrocatalysts.

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

Asphericity Can Cause Nonuniform Lithium Intercalation in Battery Active Particles

Uniform intercalation is desired to enable next-generation Li-ion batteries. While we expect nonuniformity in materials undergoing a phase change, single-phase intercalation materials such as nickel manganese cobalt oxide are believed to lithiate uniformly at the particle/electrolyte interface. However, recent imaging reveals nonuniform lithiation. Motivated by this discrepancy, we examine if aspherical particle shape can cause such nonuniformity since the conventional belief is based on spherical particle theory. We obtain real particle geometries using rapid lab-based X-ray computed tomography and subsequently perform physics-based calculations accounting for electrochemical reactions at the particle/electrolyte interface and lithium transport inside the particle bulk. The aspherical geometry breaks the symmetry and causes nonuniform reaction distribution. Such nonuniformity is exacerbated as the particle becomes more aspherical. We report the proposed mechanism represents a fundamental limit on achievable lithiation uniformity in aspherical particles in the absence of other mechanisms causing inhomogeneity, such as grain structure, nonuniform carbon-binder coating, etc.

25 ENERGY STORAGE↗
DOE OSTI2020

Metal Organic Frameworks for Xenon Storage Applications

The demand for cheap and convenient xenon storage continues to rise due to its wide spectrum of applications. It is expected that solid-state adsorbents can provide significant advantages over the current isolated stainless-steel tank-based storage technologies. In this context, we investigated metal organic frameworks for use as adsorbents for xenon. Initially, three representative MOFs were synthesized and characterized in terms of Xe storage. The results were used to validate a computational modeling approach, which was later extended to a larger set of materials. The collected results allowed us to rationalize the key parameters (pore volume, surface area, void fraction etc.), which are important for good performance and selection of the best materials for xenon storage

Noble gas, Storage, MOFs↗
DOE OSTI2023

Evaluating the Simulation of CONUS Precipitation by Storm Type in E3SM

Abstract Conventional low‐resolution (LR) climate models, including the Energy Exascale Earth System Model (E3SMv1), have well‐known biases in simulating the frequency, intensity, and timing of precipitation. Approaches to next‐generation E3SM, whether the high‐resolution (HR) or multiscale modeling framework (MMF) configuration, improve the simulation of the intensity and frequency of precipitation, but regional and seasonal deficiencies still exist. Here we apply a methodology to assess the contribution of tropical cyclones (TCs), extratropical cyclones (ETCs), and mesoscale convective systems (MCSs) to simulated precipitation in E3SMv1‐HR and E3SMv1‐MMF relative to E3SMv1‐LR. Across the United States, E3SMv1‐MMF provides the best simulation in terms of precipitation accumulation, frequency and intensity from MCSs and TCs compared to E3SMv1‐LR and E3SMv1‐HR. All E3SMv1 configurations overestimate precipitation amounts from and the frequency of ETCs over CONUS, with conventional E3SMv1‐LR providing the best simulation compared to observations despite limitations in precipitation intensity within these events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessment of Storm-Associated Precipitation and Its Extremes Using Observational Data Sets and Climate Model Short-Range Hindcasts

Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio-economic loss. Here, in this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short-range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short-range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm-associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon-based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy-precipitating storms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model

Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Colossal switchable photocurrents in topological Janus transition metal dichalcogenides

Nonlinear optical properties, such as bulk photovoltaic effects, possess great potential in energy harvesting, photodetection, rectification, etc. To enable efficient light–current conversion, materials with strong photo-responsivity are highly desirable. In this work, we predict that monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T' phase possess colossal nonlinear photoconductivity owing to their topological band mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T'JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally responsive to light in the terahertz (THz) range. By first-principles calculations, we reveal that 1T' JTMDs possess shift current (SC) conductivity as large as 2300 nm μA V -2 , equivalent to a photo-responsivity of 2800 mA/W. The circular current (CC) conductivity of 1T' JTMDs is as large as ~10 4 nm μA V -2 . These remarkable photo-responsivities indicate that the 1T' JTMDs can serve as efficient photodetectors in the THz range. We also find that external stimuli such as the in-plane strain and out-of-plane electric field can induce topological phase transitions in 1T' JTMDs and that the SC can abruptly flip their directions. The abrupt change of the nonlinear photocurrent can be used to characterize the topological transition and has potential applications in 2D optomechanics and nonlinear optoelectronics.

Chemistry↗
DOE OSTI2022

New quantum physics, solving puzzles of Wheeler’s delayed choice and a particle’s passing N slits simultaneously and quantum oscillator in experiments

Abstract This paper discovers new quantum physics, and gives solutions to puzzles of Wheeler’s delayed choice and a particle’s passing many slits simultaneously by exact quantum physics expressions. We further show new quantum control, new quantum oscillation, new quantum control experiments and new quantum oscillator being able to be installed in quantum communication network etc. We discover that the ability of a photon to hit electrons out in photoelectric effect is complementarily equivalent to the ability of wave of a photon to simultaneously pass through many slits in wave-particle duality. Objective criterion for distinguishing classical and quantum particles is found, and this paper gives applicable realm of quantum theories and new quantum physics expressions of wave-particle duality. All these studies above should be classified as classical and quantum particles, then classical particle and quantum particle wave cannot and can pass many slits, respectively. This paper discovers wave-particle duality’s origin of displaying both wave property from plane wave part of the general Fourier expansion and particle property from the general Fourier expansion coefficients with the particle’s global property and spins etc. We give the superposition state representation of wave-particle duality, further find the collapse of the duality superposition state to wave or particle state. The collapsed wave or particle state is related to the measure of wave or particle property. Then, we explain why sometimes it's a wave or a particle. Our achieved results are truly tested, and we discover new measured attractive state and quantum wave collapse velocity expression.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Transforming energy using quantum dots

Transforming and directing the flow of energy from one form (e.g., sunlight, electricity, etc.) to other useful forms of energy (e.g., electricity, chemical bonds, light, etc.) in an efficient and controllable manner is critical to meet the increasing energy demands and build a sustainable society. In search of such energy mediators, colloidal semiconductor nanocrystals, or quantum dots (QDs) are promising building blocks for building and designing systems that can efficiently capture light and convert and direct that energy into other useful forms of energy. In this work, we summarize recent advances using QDs in energy conversion architectures with the express goal of converting optical energy to other forms of energy, including electricity (i.e., photovoltaics) , photons with different energies (i.e., photo up- or downconversion), and chemical bonds (i.e., photocatalysis). The advantages of employing QDs over molecular chromophores in absorbing and then directing and converting optical energy are highlighted. Finally, we discuss ongoing challenges as well as unique opportunities associated with the use of QDs for transforming energy.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions

Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO 2 reduction reactions (CO 2 RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO 2 RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Further, among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO 2 RR catalysts, products, and performances, revealing the potentially effective material space in CO 2 RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO 2 RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Advanced Modeling and Simulation Methods for Evaluation of Thermal Neutron Scattering Materials

With the rise of interest in thermal neutron scattering data for advanced reactor, criticality safety, and shielding applications, new experimental data are required for evaluation of new materials or for re-evaluation (or validations) of previously evaluated materials. New experimental data are evaluated in a three-step process: (1) computing the phonon characteristics, (2) computing the dynamic structure factor (DSF) from the data, and (3) using the experimental setup to simulate the experimental data. All three steps have challenges, ranging from the need for a sufficiently general material simulation code—a processing code that can compute the corresponding DSF—to having a detailed layout of the instrument/beamline/facility where the data were measured. Whereas phonon characteristics of materials can be computed using various methods (molecular dynamics, density functional theory, etc.), a high-fidelity computation of the DSF and the simulation of the experiment based on the DSF is vital to the accuracy of the evaluation. The latter two steps can be achieved by using the two corresponding code systems developed by instrument scientists at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory: (1) OCLIMAX, a program that calculates the dynamic structure factor from DFT and MD simulation results, and (2) MCViNE, a Monte Carlo neutron ray-tracing program designed to simulate neutron scattering experiments. Recently, polyethylene and yttrium hydride were measured at the Wide Angular-Range Chopper (ARCS) and SEQUOIA instrument stations of the SNS. These experiments are simulated using the density functional theory code, the Cambridge Serial Total Energy Package (CASTEP), to compute its phonon characteristics (eigenvalues/vectors and PDOS), which is then processed using OCLIMAX to yield the DSF, and finally the data at each instrument station are simulated by the MCViNE for comparison to the measured data for evaluation. For comparison to conventional evaluation methods, the scattering data processed from OCLIMAX are compared against those processed from the LEAPR module of NJOY, and the results from MCViNE simulations are compared against previously used simplified beamline models implemented in the Monte Carlo N-Particle (MCNP) code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Stability analysis of the Eulerian–Lagrangian finite volume methods for nonlinear hyperbolic equations in one space dimension

In this paper, we construct a novel Eulerian–Lagrangian finite volume (ELFV) method for nonlinear scalar hyperbolic equations in one space dimension. It is well known that the exact solutions to such problems may contain shocks though the initial conditions are smooth, and direct numerical methods may suffer from restricted time step sizes. To relieve the restriction, we propose an ELFV method, where the space-time domain was separated by the partition lines originated from the cell interfaces whose slopes are obtained following the Rakine–Hugoniot junmp condition. Unfortunately, to avoid the intersection of the partition lines, the time step sizes are still limited. To fix this gap, we detect effective troubled cells (ETCs) and carefully design the influence region of each ETC, within which the partitioned space-time regions are merged together to form a new one. Then with the new partition of the space-time domain, we theoretically prove that the proposed first-order scheme with Euler forward time discretization is total-variation-diminishing and maximum-principle-preserving with at least twice larger time step constraints than the classical first order Eulerian method for Burgers’ equation. Numerical experiments verify the optimality of the designed time step sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Oscillatory Spreading and Inertia in Power Grids

The increase in variable renewable generators (VRGs) in power systems has altered the dynamics from a historical experience. VRGs introduce new sources of power oscillations, and the stabilizing response provided by synchronous generators (SGs, e.g., natural gas, coal, etc.), which help avoid some power fluctuations, will lessen as VRGs replace SGs. These changes have led to the need for new methods and metrics to quickly assess the likely oscillatory behavior for a particular network without performing computationally expensive simulations. This work studies the impact of a critical dynamical parameter - the inertia value - on the rest of a power system's oscillatory response to representative VRG perturbations. We use a known localization metric in a novel way to quantify the number of nodes responding to a perturbation and the magnitude of those responses. This metric allows us to relate the spread and severity of a system's power oscillations with inertia. We find that as inertia increases, the system response to node perturbations transitions from localized (only a few close nodes respond) to delocalized (many nodes across the network respond). We introduce a heuristic computed from the network Laplacian to relate this oscillatory transition to the network structure. We show that our heuristic accurately describes the spread of oscillations for a realistic power-system test case. Using a heuristic to determine the likely oscillatory behavior of a system given a set of parameters has wide applicability in power systems, and it could decrease the computational workload of planning and operation.

dynamical systems↗
DOE OSTI2025

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗
DOE OSTI2022

Preventing Reverse Engineering of Critical Industrial Data with DIOD

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk-assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse-engineering, stolen technology, etc. that often have disastrous consequences for businesses and their stakeholders alike. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data which are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse-engineering by discarding proprietary information and only preserving AI/ML-relevant information. Additionally, constraints of the original DIOD manuscript are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. As an example, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Influence of liquid conductance on the temporal evolution of self-organization patterns in atmospheric pressure DC glow discharges

Abstract Self-Organized Patterns (SOPs) at plasma-liquid interface in atmospheric pressure plasma discharges refer to the formation of intricate and puzzling structures due to the interplay of electrodynamic and hydrodynamic processes. Studies conducted to date have shown that this phenomenon results in the formation of distinctive patterns such as circular ring, star, gear, dots, spikes, etc., and primarily depends on working gas, electrolyte type, gap distance, current, conductivity, etc. However, an adequate understanding of how these patterns change from one type to another is still not available. This study aims to elucidate the influence of initial liquid conductance ( σ i ) on the temporal evolution of SOPs in liquid-anode discharges. The discharge was generated in a pin-to-liquid anode configuration at a constant helium (He) flow rate of 500 sccm and DC applied voltage of 6 kV at a gap distance of 12 mm. Through the gradual increment of σ i from 1.8 μ S to 4820 μ S, we observe that the trend in the evolution of SOPS takes place as solid discs, spikes, dots, rings, double rings, and stars. The continuous formation of reactive species onto the liquid anode in all conductive solutions results in a decrease in pH, an increase in bulk liquid temperature, and an increase in total dissolved solutes, and these have been confirmed through experimental measurements. Observations using optical emission spectroscopy show that the electrons at the plasma-liquid interface participate in the reduction of cations followed by their excitation & ionization due to which electron density as well as emissions from excited species (mainly hydroxyl radicals & excited nitrogen) decrease with time. Our investigation provides experimental evidence on the presence of cations at the plasma-liquid interface required for SOP formation.

Physics↗
DOE OSTI2022

Identifying Transient Candidates in the Dark Energy Survey Using Convolutional Neural Networks

The ability to discover new transient candidates via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient candidate identification on images with CNNs for an extant data set from the Dark Energy Survey Supernova program, whose main focus was on using Type Ia supernovae for cosmology. By performing an architecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g., supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results, lowering the false positive rate by 27% at a fixed missed detection rate of 0.05.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Recent advance progress of HL-3 experiments

Since the first plasma realized in 2020, a series of key systems on HL-3 (known as HL-2M before) tokamak have been equipped/upgraded, including in-vessel components (the first wall, lower divertor, and toroidal cryogenic/water-cooling/baking/glow discharge systems, etc.), auxiliary heating system of 11 MW, and 28 diagnostic systems (to measure the plasma density, electron temperature, radiation, magnetic field, etc.). Magnet field systems were commissioned firstly for divertor plasma discharges. During the 2nd experimental campaign of HL-3 tokamak, several great progresses have been achieved. Firstly, the successful operation with plasma current larger than 1 MA was achieved under a divertor configuration. Secondly, the advanced divertor concept with two distinct snowflake configurations was realized. It is found that the distribution of ion saturation current and heat flux on bottom plate becomes wide due to magnetic surface expansion, demonstrating the advantage of such configuration in the heat flux mitigation. In addition, using the combination of NBI, ECRH and LHCD, the standard sawtoothing high confinement mode of megampere plasma was firstly accessed on the HL-3. The successful commissioning of HL-3 is beneficial for the initial operation of ITER.

1 MA plasma↗