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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Verification Suite of Test Cases for the Barotropic Solver of Ocean Models

Abstract The development of any atmosphere or ocean model warrants a suite of test cases (TCs) to verify its spatial and temporal discretizations, order of accuracy, stability, reproducibility, portability, scalability, etc. In this paper, we present a suite of shallow water TCs designed to verify the barotropic solver of atmosphere and ocean models. These include the non‐dispersive coastal Kelvin wave; the dispersive inertia‐gravity wave; the dispersive planetary and topographic Rossby waves; the barotropic tide; and a non‐linear manufactured solution. These TCs check the implementation of the linear pressure gradient term; the linear constant or variable‐coefficient Coriolis and bathymetry terms; and the non‐linear advection terms. Simulation results are presented for a variety of time‐stepping methods as well as two spatial discretizations: a mimetic finite volume method based on the TRiSK scheme, and a high‐order discontinuous Galerkin spectral element method. The experimental procedure for conducting these numerical experiments is detailed. It underscores several key considerations that vary depending on the chosen spatial discretization method. Finally, convergence studies of every TC are conducted with refinement in both space and time, only in space, and only in time. The convergence slopes match the expected theoretical predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Introducing the SLICE Method for estimating pebble-bed reactor inventories at equilibrium operation with SCALE

This paper introduces the SCALE Leap-In method for Cores at Equilibrium (SLICE) for estimating pebble-bed reactor equilibrium core isotopic inventories using capabilities in the SCALE code system, requiring only a small computational cluster and a few days of computation. This method uses an iterative approach that relies on (1) a surrogate spectrum model that captures spatial and time-dependent spectral conditions, (2) a multi-pass model that captures the pebble’s evolving nuclide inventory as a function of location and time in the core, and (3) a full-core model that captures the core’s spatial neutron flux distribution. The SLICE approach is applied to a generic fluoride salt–cooled high-temperature reactor, demonstrating fuel inventory convergence through nuclide concentration inspection across iterations and comparisons for core realizations with varying discretizations. Results agree within ~5% with another state-of-the-art code, with differences attributed to input parameter or modeling assumption variations in the equilibrium generation methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Performance of microchannel plate based detectors for <25 keV x rays: Monte Carlo simulations and comparisons with experimental results

We present the results of Monte Carlo simulations of the microchannel plate (MCP) response to x rays in the 250 eV to 25 keV energy range as a function of both x-ray energy and impact angle and their comparisons with the experimental results from the X8A beamline at the National Synchrotron Light Source at Brookhaven National Laboratory. Incoming x rays interact with the lead glass of the microchannel plate, producing photoelectrons. Transport of the photoelectrons is neglected in this model, and it is assumed that photoelectrons deposit all their energy at the point they are created. This deposition leads to the generation of many secondary electrons, some fraction of which diffuse to the MCP pore surface where they can initiate secondary electron cascades in the pore under an external voltage bias. X-ray penetration through multiple MCP pore walls is increasingly important above 5 keV, and the effect of this penetration on MCP performance is studied. In agreement with past measurements, we find that the dependence of MCP sensitivity with angle relative to the pore bias changes from a cotangent dependence to angular independence and then proceeds to a secant dependence as the x-ray energy increases. We also find that with the increasing x-ray energy, the MCP gain sensitivity as a function of bias voltage decreases. The simulations also demonstrate that for x rays incident normal to the MCP surface, spatial resolution shows little dependence on the x-ray energy but degrades with the increasing x-ray energy as the angle of incidence relative to the surface normal increases. This agrees with experimental measurements. Simulation studies have also been completed for MCPs gated with a subnanosecond voltage pulse. In this work, we find that the optical gate profile width increases as the x-ray energy is increased above 5 keV, a consequence of increased x-ray penetration at energies >5 keV. Simulations of the pulsed dynamic range show that the dynamic range varies between ~100 and 1000 depending on x-ray energy and peak voltage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Nonlinear control of the minimum safety factor in tokamaks by optimal allocation of spatially moving electron cyclotron current drive

The minimum value of the safety factor profile is related to the magnetohydrodynamic (MHD) stability of the plasma confined in a tokamak. Therefore, active control of the minimum safety factor may mitigate MHD instabilities that can degrade or even terminate plasma confinement. Typically, in most tokamak scenarios, the minimum safety factor evolves spatially with time, i.e., the location at which the safety factor achieves the minimum value changes with time. In addition to the inherent nonlinearities in the minimum safety factor evolution, its spatial variation makes the control design challenging. In particular, complexity in control design may arise from the need for time-dependent nonlinear models that account for spatial variation of the minimum safety factor. Furthermore, the minimum safety factor may drift to locations where the actuator authority is low. The problem of minimum safety factor control with target location tracking and moving electron cyclotron current drive (ECCD) is addressed in this work. A nonlinear time-dependent model that incorporates the spatial variation of the minimum safety factor is presented. A nonlinear controller based on optimal feedback linearization is developed to track a target minimum safety factor. The proposed controller treats the ECCD position as a controllable variable. In other words, the controller prescribes the ECCD position (in addition to the non-inductive powers) in real time based on an optimal criterion that is defined a priori. This work also presents the steps necessary to integrate the minimum safety factor controller with a total energy controller to achieve multiple control objectives simultaneously. In conclusion, the proposed integrated control algorithm is tested using nonlinear simulations in the Control Oriented Transport SIMulator (COTSIM) for a DIII-D tokamak scenario.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization

Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.

Defect project↗
DOE OSTI2023

Nano-infrared imaging of metal insulator transition in few-layer 1T-TaS 2

Abstract Among the family of transition metal dichalcogenides, 1T-TaS 2 stands out for several peculiar physical properties including a rich charge density wave phase diagram, quantum spin liquid candidacy and low temperature Mott insulator phase. As 1T-TaS 2 is thinned down to the few-layer limit, interesting physics emerges in this quasi 2D material. Here, using scanning near-field optical microscopy, we perform a spatial- and temperature-dependent study on the phase transitions of a few-layer thick microcrystal of 1T-TaS 2 . We investigate encapsulated air-sensitive 1T-TaS 2 prepared under inert conditions down to cryogenic temperatures. We find an abrupt metal-to-insulator transition in this few-layer limit. Our results provide new insight in contrast to previous transport studies on thin 1T-TaS 2 where the resistivity jump became undetectable, and to spatially resolved studies on non-encapsulated samples which found a gradual, spatially inhomogeneous transition. A statistical analysis suggests bimodal high and low temperature phases, and that the characteristic phase transition hysteresis is preserved down to a few-layer limit.

42 ENGINEERING↗
DOE OSTI2020

Environmental impact of national and subnational carbon policies in China based on a multi-regional dynamic CGE model

Carbon policies are important not only for mitigating global climate change, but also for controlling local and regional air pollution. The large regional disparities in economic development and air pollution across China calls for regionally differentiated policies. Previous studies have not systematically investigated the environmental impacts of regional carbon policies in China covering different spatial scales. This study constructs a multi-regional dynamic computable general quilibrium (CGE) model covering 30 provinces of China to assess the impacts of national and subnational carbon polices on CO2 emissions and co-emitted air pollutants from 2020 to 2050. We consider one national carbon policy which aims to achieve China’s 2030 national CO2 abatement target, as well as three regional policies with the same policy stringency as the national one but only applied to eastern China (EP), the Jiangsu-Shanghai-Zhejiang area (JSZP), and the Beijing-Tianjin-Hebei area (BTHP), respectively. We find that regional policies (EP, JSZP, and BTHP) are as effective in reducing CO2 emissions in their targeted regions as the national policy. However, they lead to an increase in CO2 emissions in untargeted regions (the so-called “emissions leakage”). The CO2 leakage rates, which depend on the policy spatial coverage, are 4%, 13%, and 65% for EP, JSZP, and BTHP, respectively, in 2050. Compared with CO2, changes in air pollutant emissions, including sulfur dioxide (SO2), nitrogen oxides (NOx), volatile organic compounds (VOC), and fine particulate matter (PM2.5), exhibit a similar pattern under all policy scenarios, but the magnitude of change is significantly smaller. Transportation, thermal power, and some energy intensive industries are the three largest contributors to CO2 and air pollutant emission reductions. Our results suggest that regional carbon policy is effective in reducing CO2 and air pollutant emissions in the targeted regions, and extending the spatial coverage or increasing policy stringency can largely inhibit emissions leakage.

Zhang, Wenwen↗
DOE OSTI2022

Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. Here, this paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Advanced Characterization Techniques for Overcoming Challenges of Perovskite Solar Cell Materials

Abstract In the last 10 years, organic–inorganic hybrid perovskite solar cells have achieved unprecedented advances, to the point where they now exhibit extremely high efficiency. However, long‐term stability and areal scalability limitations impede the commercial application of perovskite materials, and appropriate diagnosistic tools have become necessary to evaluate perovskite materials. Characterization of perovskite materials is regularly misinterpretated, due to unique intrinsic and extrinsic factors: degradation from the measurement source, ion migration, phase transition, and separation. Herein, studies on perovskites are reviewed that have used advanced characterization tools to overcome characterization challenges. Cryogenic temperature assisted measurements mitigate degradation or phase transitions induced by the measurement source. In situ measurements can track the variation of perovskite materials depending on external stimuli. Spatial material properties are able to be evaluated by the use of multidimensional mapping techniques. An overview of these advanced characterization tools that can overcome the challenges associated with established tools provides the opportunity for further understanding perovskite materials and solving the remaining challenges on the road to commercialization.

Kim, Min‐cheol↗
DOE OSTI2020

Visualizing Charge Transport and Nanoscale Electrochemistry by Hyperspectral Kelvin Probe Force Microscopy

Charge-transport and electrochemical processes are heavily influenced by the local microstructure. Kelvin probe force microscopy (KPFM) is a widely used technique to map electrochemical potentials at the nanometer scale; however, it offers little information on local charge dynamics. In this study, we implement a hyperspectral KPFM approach for spatially mapping bias-dependent charge dynamics in timescales ranging from the sub-millisecond to the second regime. As a proof of principle, we investigate the role mobile surface charges play in a three-unit-cell LaAlO 3 /SrTiO 3 oxide heterostructure. We explore machine learning approaches to assist with visualization, pattern recognition, and interpretation of the information-rich data sets. Linear unmixing methods reveal hidden bias-dependent interfacial processes, most likely water splitting, which are essentially unnoticed by functional fitting of the dynamic response alone. Hyperspectral KPFM will be beneficial for investigating nanoscale charge transport and local reactivity in systems involving a possible combination of electronic, ionic, and electrochemical phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Autonomous adaptive data acquisition for scanning hyperspectral imaging

Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Mapping Critical Vulnerabilities in Natural Gas Pipeline Systems through Network Centrality and GIS Analytics

Natural gas plays a central role in the US energy landscape, providing 43% of electricity generation in 2023. Its exclusive recovery ability on pipelines for transmission underscores the importance of understanding the disruption recovery ability of this infrastructure. This study employs a network-based analytical framework integrating geographic information systems (GIS) with multiple centrality measures—betweenness, closeness, degree, and eigenvector—to pinpoint key segments and evaluate the structural robustness of the national pipeline network. Pipelines are grouped by System ID and Operator ID to capture variations across organizational and physical structures. The analysis reveals uneven patterns of network influence, where certain pipelines function as critical connectors or dominant hubs. Spatial mapping highlights geographic dependencies and potential chokepoints, offering a clear view of where targeted risk prevention measures would be most effective. The findings provide practical guidance for prioritizing maintenance, enhancing system robustness, and mitigating risks to ensure a stable and secure energy supply. Future research will expand the framework to incorporate dynamic operational data and real-time network behavior.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
DOE OSTI2021

Explaining an unusual electromigration behavior—A comprehensive experimental and theoretical analysis using finite element method

In metallic interconnects, it is generally assumed that electromigration (EM) failure location is independent of the applied electrical current and always occurs at the highest-current-density area. Our experiments show otherwise. We designed an Al interconnect that alters its failure location by only varying the applied current density. The failure occurs near the high for a current above 2 × 10 7 A/cm 2 , but at a location with 59% of the maximum for lower current densities. Thermoreflectance thermal imaging is employed to gather time-dependent high-resolution spatial temperature distributions of the Al interconnect during EM. More importantly, we propose a computationally inexpensive 2D finite element method that tracks EM evolution in time and matches well with the observations from different experimental conditions. A detailed analysis covering the major driving forces of EM is carried out to understand the complex physics behind EM. The atomic depletion rate contributed by each force is quantitatively studied. By examining the results from every tested experimental condition, the model reveals that the temperature gradient is the key reason causing atomic depletion near the failure location. Graphical illustrations and qualitative analysis are provided to intuitively show the key findings of our work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Spectral and polarization based imaging in deep-ultraviolet excited photoelectron microscopy

Using photoelectron emission microscopy, nanoscale spectral imaging of atomically thin MoS2 buried between Al2O3 and SiO2 is achieved by monitoring the wavelength and polarization dependence of the photoelectron signal excited by deep-ultraviolet light. Although photons induce the photoemission, images can exhibit resolutions below the photon wavelength as electrons sense the response. To validate this concept, the dependence of photoemission yield on the wavelength and polarization of the exciting light was first measured and then compared to simulations of the optical response quantified with classical optical theory. A close correlation between experiment and theory indicates that photoemission probes the optical interaction of UV-light with the material stack directly. The utility of this probe is then demonstrated when both the spectral and polarization dependence of photoemission observe spatial variation consistent with grains and defects in buried MoS2. Taken together, these new modalities of photoelectron microscopy allow mapping of optical property variation at length scales unobtainable with conventional light-based microscopy.

Beechem, Thomas E. (ORCID:0000000275360719)↗
DOE OSTI2021

The reflection of a blast wave by a very intense explosion

We demonstrate that the geometric similarity of Taylor’s blast wave persists beyond reflection from an ideal surface. Upon impacting the surface, the spherical symmetry of the blast wave is lost but its cylindrical symmetry endures. As the flow acquires dependence on a second spatial dimension, an analytic solution of the Euler equations becomes elusive. However, the preservation of axisymmetry, geometric similarity and planar symmetry in the presence of a mirror-like surface causes all flow solutions to collapse when scaled by the height of burst (HOB) and the shock arrival time at the surface. The scaled blast volume for any yield, HOB and ambient air density follows a single universal trajectory for all scaled time, both before and after reflection.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Nearly optimal state preparation for quantum simulations of lattice gauge theories

Here, we present several improvements to the recently developed ground-state preparation algorithm based on the quantum eigenvalue transformation for unitary matrices (QETU), apply this algorithm to a lattice formulation of U(1) gauge theory in (2+1) dimensions, as well as propose an alternative application of QETU, a highly efficient preparation of Gaussian distributions. The QETU technique was originally proposed as an algorithm for nearly optimal ground-state preparation and ground-state energy estimation on early fault-tolerant devices. It uses the time-evolution input model, which can potentially overcome the large overall prefactor in the asymptotic gate cost arising in similar algorithms based on the Hamiltonian input model. We present modifications to the original QETU algorithm that significantly reduce the cost for the cases of both exact and Trotterized implementation of the time evolution circuit. We use QETU to prepare the ground state of a U(1) lattice gauge theory in two spatial dimensions, explore the dependence of computational resources on the desired precision and system parameters, and discuss the applicability of our results to general lattice gauge theories. We also demonstrate how the QETU technique can be utilized for preparing Gaussian distributions and wave packets in a way which outperforms existing algorithms for as little as n q ≳ 2–5 qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

58 GEOSCIENCES↗