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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

Abstract Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐grid topographic representations in ELM were compared: (a) the default sub‐grid structure (D), (b) the recently developed sub‐grid topographic structure (T), and (c) high spatial resolution (1KM). Additionally, two different solar radiation schemes in ELM were compared: (a) the default plane‐parallel radiative transfer scheme (PP) and (b) the parameterization scheme (TOP) that accounts for sub‐grid topographic effects on solar radiation. A series of offline simulations with the three grid discretization structures (D, T, and 1KM) and two schemes of solar radiation (TOP and PP) were carried out using the Sierra Nevada, California. 1KM simulations with TOP well capture the spatial heterogeneity of surface fluxes compared to Moderate Resolution Imaging Spectroradiometer remote sensing data. There are significant differences between TOP and PP in the 1‐km simulated surface energy balance, but the differences in mean values and standard deviations become small when aggregated to the grid scale (i.e., 0.5°). The T configuration better mimics the 1KM simulations with TOP than the D configuration and better captures the sub‐grid topographic effects on surface energy balance and boundary conditions. These results underline the importance of representing sub‐grid topographic heterogeneities in LSMs and motivate future research to understand the sub‐grid topographic effects on land‐atmosphere interactions over mountainous areas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Monitoring Seismic Velocity Changes Across the San Jacinto Fault Using Train‐Generated Seismic Tremors

Abstract Microseismic noise has been used for seismic velocity monitoring. However, such signals are dominated by low‐frequency surface waves that are not ideal for detecting changes associated with small tectonic processes. Here we show that it is possible to extract stable, high‐frequency body waves using seismic tremors generated by freight trains. Such body waves allow us to focus on small velocity perturbations in the crust with high spatial resolution. We report on 10 years of seismic velocity temporal changes at the San Jacinto Fault. We observe and map a two‐month‐long episode of velocity changes with complex spatial distribution and interpret the velocity perturbation as produced by a previously undocumented slow‐slip event. We verify the hypothesis through numerical simulations and locate this event along a fault segment believed to be locked. Such a slow‐slip event stresses its surroundings and may trigger a major earthquake on a fault section approaching failure.

58 GEOSCIENCES↗
DOE OSTI2023

High–Resolution Maps of Near–Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter–scale maps of near–surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near–surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k–fold cross validation, and the modeled extents of near–surface permafrost were compared to the observed extents at each site. At–a–site near–surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at–a–site prediction (70%–83%), yet they had greater accuracy when transferred to the non–training site (62%–78%). These models demonstrate the potential for integrating high–resolution spatial data and machine learning models to develop maps of near–surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying Seasonal and Diurnal Cycles of Solar‐Induced Fluorescence With a Novel Hyperspectral Imager

Abstract Solar‐induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra‐hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf‐level physiology and gross primary productivity under well‐watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

Abstract Atmospheric CO 2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO 2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Uncertainties in Simulating Flooding During Hurricane Harvey Using 2D Shallow Water Equations

Abstract Flooding is one of the most impactful weather‐related natural hazards. Numerical models that solve the two dimensional (2D) shallow water equations (SWE) represent the first‐principles approach to simulate all types of spatial flooding, such as pluvial, fluvial, and coastal flooding, and their compound dynamics. High spatial resolution (e.g., () m) is needed in 2D SWE simulations to capture flood dynamics accurately, resulting in formidable computational challenges. Thus, relatively coarser spatial resolutions are used for large‐scale simulations of flooding, which introduce uncertainties in the results. It is unclear how the uncertainty associated with the model resolution compares to the uncertainties in precipitation data sets and assumptions regarding boundary conditions when channelized flows interact with other water bodies. In this study, we compare these three sources of uncertainties in 2D SWE simulations for the 2017 Houston flooding event. Our results show that precipitation uncertainty and mesh resolution have more significant impacts on the simulated streamflow and inundation dynamics than the choice of the downstream boundary condition at the watershed outlet. We point out the viability to confine the uncertainty of coarsening mesh resolution by using the variable resolution mesh (VRM) which refines critical topographic features with far fewer grid cells. Specifically, in simulations with VRM, the simulated inundation depths over the refined region are comparable to that use the finest uniform mesh. This study contributes to understanding the challenges and pathways for applying 2D SWE models to improve the realism of flood simulations over large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2022

Dark matter from axion strings with adaptive mesh refinement

Abstract Axions are hypothetical particles that may explain the observed dark matter density and the non-observation of a neutron electric dipole moment. An increasing number of axion laboratory searches are underway worldwide, but these efforts are made difficult by the fact that the axion mass is largely unconstrained. If the axion is generated after inflation there is a unique mass that gives rise to the observed dark matter abundance; due to nonlinearities and topological defects known as strings, computing this mass accurately has been a challenge for four decades. Recent works, making use of large static lattice simulations, have led to largely disparate predictions for the axion mass, spanning the range from 25 microelectronvolts to over 500 microelectronvolts. In this work we show that adaptive mesh refinement simulations are better suited for axion cosmology than the previously-used static lattice simulations because only the string cores require high spatial resolution. Using dedicated adaptive mesh refinement simulations we obtain an over three order of magnitude leap in dynamic range and provide evidence that axion strings radiate their energy with a scale-invariant spectrum, to within ~5% precision, leading to a mass prediction in the range (40,180) microelectronvolts.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Structural basis for breadth development in the HIV-1 V3-glycan targeting DH270 antibody clonal lineage

Antibody affinity maturation enables adaptive immune responses to a wide range of pathogens. In some individuals broadly neutralizing antibodies develop to recognize rapidly mutating pathogens with extensive sequence diversity. Vaccine design for pathogens such as HIV-1 and influenza has therefore focused on recapitulating the natural affinity maturation process. Here, we determine structures of antibodies in complex with HIV-1 Envelope for all observed members and ancestral states of the broadly neutralizing HIV-1 V3-glycan targeting DH270 antibody clonal B cell lineage. These structures track the development of neutralization breadth from the unmutated common ancestor and define affinity maturation at high spatial resolution. By elucidating contacts mediated by key mutations at different stages of antibody development we identified sites on the epitope-paratope interface that are the focus of affinity optimization. Thus, our results identify bottlenecks on the path to natural affinity maturation and reveal solutions for these that will inform immunogen design aimed at eliciting a broadly neutralizing immune response by vaccination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Non-linear enhancement of ultrafast X-ray diffraction through transient resonances

Diffraction-before-destruction imaging with ultrashort X-ray pulses can visualize non-equilibrium processes, such as chemical reactions, with sub-femtosecond precision in the native environment. Here, a nanospecimen diffracts a single X-ray flash before it disintegrates. The sample structure can be reconstructed from the coherent diffraction image (CDI). State-of-the-art X-ray snapshots lack high spatial resolution because of weak diffraction signal. Bleaching effects from photo-ionization significantly restrain image brightness scaling. We find that non-linear transient ion resonances can overcome this barrier if X-ray laser pulses are shorter than in most experiments. We compared snapshots from individual ≈ 100 nm Xe nanoparticles as a function of pulse duration and incoming X-ray fluence. Our experimental results and Monte Carlo simulations suggest that transient resonances can increase ionic scattering cross sections significantly beyond literature values. This provides a novel avenue towards substantial improvement of the spatial resolution in CDI in combination with sub-femtosecond temporal precision at the nanoscale.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Strain-associated nanoscale fluctuating lithium transport within single-crystalline LiNi 1/3 Mn 1/3 Co 1/3 O 2 cathode particles

Solid-state lithium diffusion dynamics are critical for the rate capability and longevity of Li-ion batteries. Conventionally, nanoscale lithium diffusion within individual battery particles has been simplified as being primarily driven by concentration gradients, despite the associated processes inducing local lattice expansion, contraction, and strain fields. Using operando scanning transmission soft X-ray microscopy with high spatial resolution and chemical sensitivity to track nanoscale intraparticle lithium transport, and post-cycling Bragg coherent diffraction X-ray imaging to directly reveal three-dimensional intraparticle strain fields, we uncover strain-associated lithium transport dynamics within single-crystalline LiNi 1/3 Mn 1/3 Co 1/3 O 2 (scNMC) particles during cycling. Contrary to the expected thermodynamic solid-solution behavior of scNMC, our observations reveal near-uniform but fluctuating regions of lithium-dense and lithium-dilute areas during cycling. These fluctuations suggest that nanoscale lithium diffusion can proceed counter to concentration gradients. Additionally, we demonstrate that an increased presence of lithium-dilute regions near the surface enhances lithium surface insertion kinetics, emphasizing the importance of controlling surface lithium distribution to improve rate performance. Our study provides insights into nanoscale solid-state ion transport, with potential applications in batteries, solid-state fuel cells, and memristors.

Lee, Danwon [Seoul National Univ. (Korea, Republic↗
DOE OSTI2026

Multi-modal characterization of nitrate reduction nano-catalysts with periodic strain distribution

Strain engineering serves as a pivotal strategy to optimize catalytic activity in electrocatalysis. However, the catalyst sizes under industrial conditions are usually large and even beyond nanometer regime. The critical methodological limitations on strain imaging of such catalysts with both large field of view and high spatial resolution obscure the mechanistic understanding of strain-performance correlations. Here, we present an optimized four-dimensional scanning transmission electron microscopy (4D-STEM) method to acquire strain mapping of both bulk and surface across particles up to 500 nm with 0.6 nm spatial resolution and 0.55% precision. We observe the ripple-like periodic strain coupled with elemental fluctuations inside a perovskite-type hydroxide CuCoSn(OH) 6 and find it correlated to electrocatalytic nitrate reduction (NO 3 – RR) absorption energy to achieve the 92.6% Faradaic efficiency and long-term test over 1000 h at membrane electrode assembly (MEA) for ammonia electrosynthesis. This universal framework design offers a practical method that not only develops an advanced measurement combining multi-modal characterization techniques but also reveals the intrinsic structure-property constitutive law of industry-level catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Differential programming enabled functional imaging with Lorentz transmission electron microscopy

Abstract Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Symmetric carbon tetramers forming spin qubits in hexagonal boron nitride

Abstract Point defect quantum bits in semiconductors have the potential to revolutionize sensing at atomic scales. Currently, vacancy-related defects are at the forefront of high spatial resolution and low-dimensional sensing. On the other hand, it is expected that impurity-related defect structures may give rise to new features that could further advance quantum sensing in low dimensions. Here, we study the symmetric carbon tetramer clusters in hexagonal boron nitride and propose them as spin qubits for sensing. We utilize periodic-DFT and quantum chemistry approaches to reliably and accurately predict the electronic, optical, and spin properties of the studied defect. We show that the nitrogen-centered symmetric carbon tetramer gives rise to spin state-dependent optical signals with strain-sensitive intersystem crossing rates. Furthermore, the weak hyperfine coupling of the defect to their spin environments results in a reduced electron spin resonance linewidth that can enhance sensitivity.

Chemistry↗
DOE OSTI2024

Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

Abstract Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary interest. Hexagonal boron nitride (hBN) hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing foils. The sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal, which is broadened due to strong hyperfine couplings. Here, we report on a vacancy-related spin qubit with an inherently low symmetry configuration, the VB2 center, giving rise to a reduced magnetic resonance linewidth at zero magnetic fields. The VB2 center is also equipped with a classical memory that can be utilized for storing population information. Using scanning transmission electron microscopy imaging, we confirm the existence of the VB2 configuration in free-standing monolayer hBN.

Chemistry↗
DOE OSTI2023

Expanded modelling scenarios to understand the role of offshore wind in decarbonizing the United States

An assessment of decarbonization pathways in energy models reveals fundamental limitations in representing factors that are relevant for practical decision-making. Although these modelling limitations are widely acknowledged, their impact on the deployment of individual power generation types is not well understood. As a result, the societal value from such generation types could be vastly misrepresented. Here we explore a wide spectrum of factors that impact offshore wind deployment in the United States using a detailed capacity expansion model. Many factors prescribe a large future role for offshore wind, yet this diverges from what models often show. We extend the typically narrow modelling context through high spatial resolution, several cost and transmission possibilities and various energy-sector policies. Further, we estimate offshore wind to constitute 1-8% (31-256 gigawatts) of total US generation by 2050. This wide range suggests an uncertain but potentially important regional role. Our expansive scenarios demonstrate how to address many limitations of decarbonization modelling.

17 WIND ENERGY↗
DOE OSTI2021

Single-defect phonons imaged by electron microscopy

Crystal defects affect the thermal and heat-transport properties of materials by scattering phonons and modifying phonon spectra. To appreciate how imperfections in solids influence thermal conductivity and diffusivity, it is thus essential to understand phonon–defect interactions. Sophisticated theories are available to explore such interactions, but experimental validation is limited because most phonon-detecting spectroscopic methods do not reach the high spatial resolution needed to resolve local vibrational spectra near individual defects. In this paper, we demonstrate that space- and angle-resolved vibrational spectroscopy in a transmission electron microscope makes it possible to map the vibrational spectra of individual crystal defects. We detect a red shift of several millielectronvolts in the energy of acoustic vibration modes near a single stacking fault in cubic silicon carbide, together with substantial changes in their intensity, and find that these changes are confined to within a few nanometres of the stacking fault. These observations illustrate that the capabilities of a state-of-the-art transmission electron microscope open the door to the direct mapping of phonon propagation around defects, which is expected to provide useful guidance for engineering the thermal properties of materials.

42 ENGINEERING↗