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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 OSTI2023

A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscatter Diffraction

The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. Here, we show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High Throughput Mapping of Single Molecules’ Redox Potentials on Electrode

By synchronizing electrochemical potential scanning with a single-molecule localization super-resolution fluorescence microscope, kinetic fluorescence changes of hundreds of single molecular redox events were tracked simultaneously with high throughput, and subsequent cross-correlation function analysis mapped single molecules’ redox potentials (times) out on the imaging area from site to site in unprecedented detail by extracting electrochemically induced fluorescence change from apparently random fluorescence on/off blinking. This work paves the way toward mapping redox states at single-molecule levels in high throughput in chemical and biological systems.

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

Quantum Imaging of Ferromagnetic van der Waals Magnetic Domain Structures at Ambient Conditions

Recently discovered 2D van der Waals magnetic materials, and specifically iron–germanium–telluride (Fe5GeTe2), have attracted significant attention both from a fundamental perspective and for potential applications. Key open questions concern their domain structure and magnetic phase transition temperature as a function of sample thickness and external field, as well as implications for integration into devices such as magnetic memories and logic. Here we address key questions using a nitrogen-vacancy center based quantum magnetic microscope, enabling direct imaging of the magnetization of Fe5GeTe2 at submicrometer spatial resolution as a function of temperature, magnetic field, and thickness. This quantum imaging technique provides noninvasive, high-sensitivity measurements with high spatial resolution under ambient conditions, making it particularly well suited for probing 2D magnets. We employ spatially resolved measures, including magnetization variance and cross-correlation, and find a significant spread in transition temperature yet with no clear dependence on thickness down to 15 nm. We also identify previously unknown stripe features in the optical as well as magnetic images, which we attribute to modulations of the constituting elements during crystal synthesis and subsequent oxidation. Our results suggest that the magnetic anisotropy in this material does not play a crucial role in their magnetic properties, leading to a magnetic phase transition of Fe5GeTe2 which is largely thickness-independent down to 15 nm. Our findings could be significant in designing future spintronic devices, magnetic memories, and logic with 2D van der Waals magnetic materials.

Bindu, Bindu [Hebrew University of Jerusalem, Isra↗
DOE OSTI2021

Role of Mineralogy in Controlling Fracture Formation

The presence of fractures in caprocks can pose increased risks in subsurface energy systems and processes like CO 2 sequestration by introducing high-permeability leakage paths. Fracture apertures and permeability can be altered through mineral dissolution and precipitation reactions, but the reactive evolution of fractures is not well understood. In fractures, minerals that are otherwise inaccessible to reactive fluids can become exposed, resulting in mineral reactions unpredicted by bulk formation data. This work seeks to understand the relationship between mineralogy and fracture formation to enhance our understanding of reactive fracture evolution and CO 2 leakage potential. Here, the mineral compositions of mechanically induced fracture surfaces in samples of the Mancos and Marcellus shales have been quantified and compared to those of the near-fracture matrices using imaging and bulk X-ray diffraction (XRD) data. In the Mancos shale, the concentrations of clay minerals are enhanced along fracture surfaces with respect to the bulk, and the fracture is most likely to form at kaolinite–kaolinite interfaces. Further, evaluation of the mineralogical spatial variability through cross-correlation analysis of the surrounding matrix in images of samples cut perpendicular to the fracture shows that clay is 16.7 times more likely to be present than carbonate minerals near the fracture surface. The high correlation persists roughly 200 μm into the surrounding matrix for the Mancos sample and implies that the fracture formed within a defined clay-rich lithofacies.

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

Scale-Dependent Bedform Migration and Deformation in the Physical and Spectral Domains

Multi-scale bathymetries observed in laboratory channels and natural rivers have posed several challenges to the description of bedform geometry and kinematics. To better quantify scale-dependent migration velocity, high resolution spatio-temporal bed evolution data are analyzed using three independent methods: (i) a bedform tracking method that identifies individual bedforms in longitudinal bed elevation profiles, (ii) a cross-correlation based, image matching technique in consecutive bathymetry scans similar to Particle Image Velocimetry, and (iii) two-dimensional frequency-wavenumber spectra marking a dispersion relation between bedform period and length, allowing estimation of Fourier-based, scale-dependent migration velocities. The comparative studies show that the spectral and bedform tracking methods present small differences in the migration velocity of medium-large bedforms. However, significant deviations occur in the small bedforms, mostly due to bedform sheltering or amplified exposure to the flow, which also contribute to their enhanced scale-dependent deformation. The analysis also shows that some form of scale separation is needed to extract the large and slowly migrating bedforms as they are masked by secondary features when several bedform orders coexist. Bathymetric Image Velocimetry reasonably estimates the averaged migration velocity and exhibits potential to study relations between spanwise bed surface velocity and local bedform kinematics.

58 GEOSCIENCES↗
DOE OSTI2021

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗
DOE OSTI2023

Multivariate Drought Monitoring, Propagation, and Projection Using Bias-Corrected General Circulation Models

Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological-hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought-related consequences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2025

Multitaper Magnitude‐Squared Coherence for Time Series With Missing Data: Understanding Oscillatory Processes Traced by Multiple Observables

To explore the hypothesis of a common source of variability in two time series, observers may estimate the magnitude-squared coherence (MSC), which is a frequency-domain view of the cross correlation. For time series that do not have uniform observing cadence, MSC can be estimated using Welch's overlapping segment averaging. However, multitaper has superior statistical properties to Welch's method in terms of the tradeoff between bias, variance, and bandwidth. The classical multitaper technique has recently been extended to accommodate time series with underlying uniform observing cadence from which some observations are missing. This situation is common for solar and geomagnetic data sets, which may have gaps due to breaks in satellite coverage, instrument downtime, or poor observing conditions. We demonstrate the scientific use of missing-data multitaper magnitude-squared coherence by detecting known solar mid-term oscillations in simultaneous, missing-data time series of solar Lyman α flux and geomagnetic Disturbance Storm Time index. Due to their superior statistical properties, we recommend that multitaper methods be used for all heliospheric time series with underlying uniform observing cadence.

Astro-statistics techniques (1886)↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

X-ray study of ferroic octupole order producing anomalous Hall effect

Recently found anomalous Hall, Nernst, magnetooptical Kerr, and spin Hall effects in the antiferromagnets Mn 3 X (X = Sn, Ge) are attracting much attention for spintronics and energy harvesting. Since these materials are antiferromagnets, the origin of these functionalities is expected to be different from that of conventional ferromagnets. Here, we report the observation of ferroic order of magnetic octupole in Mn 3 Sn by X-ray magnetic circular dichroism, which is only predicted theoretically so far. The observed signals are clearly decoupled with the behaviors of uniform magnetization, indicating that the present X-ray magnetic circular dichroism is not arising from the conventional magnetization. We have found that the appearance of this anomalous signal coincides with the time reversal symmetry broken cluster magnetic octupole order. Our study demonstrates that the exotic material functionalities are closely related to the multipole order, which can produce unconventional cross correlation functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers

The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientific breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efficient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the Nuclear-physics and Multi-Messenger Astrophysics framework NMMA. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. In previous works, the NMMA code has allowed us to constrain the equation of state of supranuclear dense matter, to measure the Hubble constant, and to compare dense-matter physics probed in neutron-star mergers and in heavy-ion collisions, and to classify electromagnetic observations and perform model selection. Here, we show an extension of the NMMA code as a first attempt of analyzing the gravitational-wave signal, the kilonova, and the gamma-ray burst afterglow simultaneously. Incorporating all available information, we estimate the radius of a 1.4M ⊙ neutron star to be $R$ $=$ $11.98^{+0.35}_{-0.04}$ km.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Validation of the IRI-2016 model with Indian NavIC data for future navigation applications

The position accuracy of Navigation with Indian Constellation (NavIC) system is affected by several sources of errors. Among them, the Ionospheric Time Delay (ITD) error is the most predominant one which depends upon the total electron content (TEC) present in the ionosphere. The ITD variations are more intense over low latitude regions due to the equatorial anomaly effects. Hence, modelling of ITD error is necessary. The International Reference Ionosphere (IRI)-2016 model is one of the standard global ionospheric models to estimate the Vertical TEC (VTEC). This paper discusses about the VTEC deviations due to the IRI-2016 model over low latitude Hyderabad station, Indian region using NavIC, Global Positioning System and Global Navigation Satellite System signals at corresponding Ionospheric Pierce Point latitude and longitudes for all months and various Kp indices during the low solar activity year 2017. In this work, cross correlation coefficient, the metric norm (L2N), Symmetric Kullbacke Leibler Distance metrics are used to evaluate the performance of IRI-2016 model TEC with NavIC, GPS and GLONASS data. From the results, it is found that TEC predicted by the IRI-2016 model produced smaller estimation errors with NavIC data over Indian region. The obtained results will be helpful for future updates of IRI model.

42 ENGINEERING↗
DOE OSTI2026

Emulating galaxy and peculiar velocity clustering on non-linear scales

We explore the potential of cross-correlating galaxies and peculiar velocities on non-linear scales to enhance cosmological constraints. Leveraging the ABACUSSUMMIT simulation suite and the halo occupation distribution (HOD) formalism, we trained emulator models to describe the non-linear clustering of galaxies and velocities in redshift space. Our analysis demonstrates that combining galaxy and peculiar velocity clustering provides tighter constraints on both HOD and cosmological parameters, particularly on σ8 and w0. We further applied our models to realistic mock catalogues, reproducing the expected density and peculiar velocity errors of type-Ia supernovae, Tully-Fisher and fundamental plane measurements for the combined ZTF and DESI measurements. While systematic biases arise in the HOD parameters, the cosmological constraints remain unbiased, yielding a 3.8% precision measurement on fσ 8 compared to 4.7% when using galaxy clustering alone. We demonstrate that while combining tracers with realistic velocity measurements still yields an improvement, the gains are diminished, highlighting the need for further efforts to reduce velocity measurement uncertainties and correct observational systematics on small scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A compact electron source for the dielectric laser accelerator

In this work, we design and demonstrate a compact electron source that combines an integrated silicon nanotip photoemitter with a compact silicon-based electrostatic lens. The lens simultaneously accelerates electrons to 30 keV and focuses the resulting beam to a 0.4 μm (RMS) beam diameter with 62 pm-rad normalized emittance at a distance of 20 mm from the cathode. The compact nature of this lens provides a compelling source for dielectric laser accelerator (DLA) beamlines, ultrafast electron diffraction, or ultrafast electron microscopy. Driven by a 220 fs, 1960 nm pulsed laser beam, electron currents up to 28 electrons/pulse at 100 kHz are demonstrated. The electron bunch length is 540 ± 50 fs for photocurrents of <1 electron/pulse, increasing to 700 ± 80 fs for 28 electrons/pulse, as measured by cross correlation with a 220 fs pulsed laser beam. The maximum 5D peak brightness is measured to be 6.8 × 10 13 A/(m 2 rad 2 ) at 28 electrons/pulse. These results represent a significant step toward developing practical benchtop-sized linear accelerators based on DLA technology or compact ultrafast electron microscopy and diffraction applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A large field-of-view high-resolution hard x-ray microscope using polymer optics

Here, we present an effective approach using a matched pair of polymer-based condenser-objective lenses to build a compact full-field X-ray microscope with a high spatial resolution. A unique condenser comprising arrays of high-aspect-ratio prisms with equilateral cross-section is used for uniformly illuminating samples over a large field of view (FoV) from all angles, which match the acceptance of an objective made of interdigitated orthogonal rows of one-dimensional lenses. State-of-the-art Talbot grating interferometry used to characterize these lenses for the first time revealed excellent focusing properties and minimal wave-front distortions. Using a specific lens-pair designed for 20 keV X-rays, short-exposure times, and image registration with a cross-correlation technique, we circumvent vibrational instabilities to obtain distortion-free images with a uniform resolution of 240 nm (smallest resolvable line pair) over a large FoV, 80 × 80 µm 2 in extent. The results were contrasted with those collected under illumination using commercial two-dimensional parabolic lenses with a smaller FoV. This approach implemented on a diffractometer would enable diffraction-contrast or dark-field microscopy for fast observations of ‘mesoscopic’ phenomena in real space complementing reciprocal-space studies using diffraction on the same instrument.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Simultaneous inference of the compressibility and inelastic response of tantalum under extreme loading

We study the deformation of tantalum under extreme loading conditions. Experimental velocity data are drawn from both ramp loading experiments on Sandia’s Z-machine and gas gun compression experiments. The drive conditions enable the study of materials under pressures greater than 100 GPa. We provide a detailed forward model of the experiments including a model of the magnetic drive for the Z-machine. Utilizing these experiments, we simultaneously infer several different types of physically motivated parameters describing equation of state, plasticity, and anelasticity via the computational device of Bayesian model calibration. Characteristics of the resulting calculated posterior distributions illustrate relationships among the parameters of interest via the degree of cross correlation. The calibrated velocity traces display good agreement with the experiments up to experimental uncertainty as well as improvement over previous calibrations. Examining the Z-shots and gun-shots together and separately reveals a trade-off between accuracy and transferability across different experimental conditions. Implications for model calibration, limitations from model form, and suggestions for improvements are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗