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DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗

DOE OSTI2025

Electron-Proton Scattering Event Generation using Structured Tokenization

Recent work such as Omnijet-$\alpha$ has demonstrated that effective tokenization combined with transformer-based architectures can produce effective foundation models for jet physics. While tokenization may help models capture generalizable event characteristics, it also introduces discretization errors that may compromise the precision required for downstream physics analyses. As the number and complexity of the particle features grow, these errors are likely to grow proportionally. In this study, we investigate new tokenization strategies to improve the application of generative transformer models to \textsc{Pythia8} simulations of electron-proton scattering at the Electron-Ion Collider. Specifically, we propose a feature-based structured tokenization approach that utilizes multiple tokens per particle, improving expressivity, while reducing the total number of unique tokens needed. We evaluate this method against grid-based binning, K-means clustering, and vector-quantized variational auto-encoders on the event simulations. Our results show that feature-based structured tokenization reduces discretization error, leading to more accurate generative modeling of particle-level events.

Goldenberg, Steven [Thomas Jefferson National Acce↗

DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗

NASA NTRS1951

Preliminary results of a determination of temperatures of flames by means of K-band microwave attenuation

The temperature effects on the attenuation of K-band microwaves, at a frequency of 26,500 plus or minus 30 megacycles per second, through natural-gas and propane flames containing added alkali halide salts, were investigated over a temperature range from 1900 to 2500 K. The preliminary data of this investigation indicated that the attenuation varies appreciably with the sodium-line-reversal temperatures of the flames and is independent of the particular hydrocarbon fuels that were used for temperature sources and of the particular halide components of the compounds used in the concentrations employed to produce easily measurable attenuation. A reproducibility of plus or minus 25 K was obtainable.

RESEARCH EQUIPMENT AND TECHNIQUES↗

NASA NTRS2020

Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature using Aquarius Observations over Non-Frozen Land across North America

L-band brightness temperature (T(sub b)) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in T(sub b) observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, T(sub b) estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily T(sub b) observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed T(sub b) were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed T(sub b) with a relatively low, domain-average bias (0.79 K (ascending) and -2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled T(sub b) due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

L-band radiometry↗

NASA NTRS1989

Surface modification of Monel K-500 as a means of reducing friction and wear in high-pressure oxygen

A study is conducted of the tribological characteristics of Monel K-500 during rubbing in a high pressure oxygen atmosphere, upon surface treatment by ion-implanted oxygen, chromium, lead, and silver, as well as electrolyzed chromium and an electroless nickel/SiC composite. The electrolyzed chromium dramatically increased total sample wear, while other surface treatments affected sample wear only moderately. Although the ion-implant treatments reduced the average coefficient of friction at low contact pressure, higher contact pressures eliminated this improvement.

Gunaji, Mohan↗

NASA NTRS1994

On the measurement of the crack tip stress field as a means of determining Delta K(sub eff) under conditions of fatigue crack closure

The optical method of caustics has been successfully extended to enable stress intensity factors as low as 1MPa square root of m to be determined accurately for central fatigue cracks in 2024-T3 aluminium alloy test panels. The feasibility of using this technique to study crack closure, and to determine the effective stress intensity factor range, Delta K(sub eff), has been investigated. Comparisons have been made between the measured values of stress intensity factor, K(sub caus), and corresponding theoretical values, K(sub theo), for a range of fatigue cracks grown under different loading conditions. The values of K(sub caus) and K(sub theo) were in good agreement at maximum stress, where the cracks are fully open, while K(sub caus) exceeded K(sub theo) at minimum stress, due to crack closure. However, the levels of crack closure and values of Delta K(sub eff) obtained could not account for the variations of crack growth rate with loading conditions. It is concluded that the values of Delta K(sub eff), based on caustic measurements in a 1/square root of r stress field well outside the plastic zone, do not fully reflect local conditions which control crack tip behavior.

Wallhead, Ian R.↗

NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.

Memarsadeghi, Nargess↗

NASA NTRS1979

Thermal infrared properties of the Martian atmosphere. II - The 15-micron band measurements

Viking infrared thermal mapper observations of Mars in the 15-micron CO2 band reveal global atmospheric thermal behavior at the 0.3to 0.6-mbar level. Dust entrained by storms produces major modification of diurnal and latitudinal structure in the brightness temperature. In the dust-laden atmosphere of southern spring and summer 1977, the brightness temperature was a maximum in late afternoon at a latitude well south of the subsolar latitude. Diurnal amplitude was as great as 30 K, while diurnal mean temperatures exceeded 220 K. Over the northern winter polar cap, the brightness temperature increased dramatically following the second global dust storm of 1977; even in regions of polar night the change was up to 80 K. Inversions of similar magnitude resulted, and the change in downward radiance was sufficient to modify substantially the rate of CO2 condensation at the surface.

Martin, T. Z.↗

NASA NTRS1985

On the construction of the Voronoi mesh on a sphere

The primary feature of the novel construction of the Voronoi (1908) mesh presented is the algorithm's adding of points one-at-a-time, until the final mesh is achieved. The addition of one point to an existing Voronoi mesh of K points means that only local changes are needed to construct a mesh of K + 1 points; this construction is especially suited to time-dependent problems in virtue of the ability to reduce the construction to O(N) operations when configurations are close, while the algorithm does not break down in cases of configurations that are far apart. Illustrative numerical experiments substantiate the O(N) operation count for a typical case.

Augenbaum, J. M.↗

NASA NTRS2010

Magnesium Diboride Current Leads

A recently discovered superconductor, magnesium diboride (MgB2), can be used to fabricate conducting leads used in cryogenic applications. Dis covered to be superconducting in 2001, MgB2 has the advantage of remaining superconducting at higher temperatures than the previously used material, NbTi. The purpose of these leads is to provide 2 A of electricity to motors located in a 1.3 K environment. The providing environment is a relatively warm 17 K. Requirements for these leads are to survive temperature fluctuations in the 5 K and 11 K heat sinks, and not conduct excessive heat into the 1.3 K environment. Test data showed that each lead in the assembly could conduct 5 A at 4 K, which, when scaled to 17 K, still provided more than the required 2 A. The lead assembly consists of 12 steelclad MgB2 wires, a tensioned Kevlar support, a thermal heat sink interface at 4 K, and base plates. The wires are soldered to heavy copper leads at the 17 K end, and to thin copper-clad NbTi leads at the 1.3 K end. The leads were designed, fabricated, and tested at the Forschungszentrum Karlsruhe - Institut foer Technische Physik before inclusion in Goddard's XRS (X-Ray Spectrometer) instrument onboard the Astro-E2 spacecraft. A key factor is that MgB2 remains superconducting up to 30 K, which means that it does not introduce joule heating as a resistive wire would. Because the required temperature ranges are 1.3-17 K, this provides a large margin of safety. Previous designs lost superconductivity at around 8 K. The disadvantage to MgB2 is that it is a brittle ceramic, and making thin wires from it is challenging. The solution was to encase the leads in thin steel tubes for strength. Previous designs were so brittle as to risk instrument survival. MgB2 leads can be used in any cryogenic application where small currents need to be conducted at below 30 K. Because previous designs would superconduct only at up to 8 K, this new design would be ideal for the 8-30 K range.

Panek, John↗

NASA NTRS2012

The Formation Heritage of Jupiter Family Comet 10P/Tempel 2 as Revealed by Infrared Spectroscopy

We present spectral and spatial information for major volatile species in Comet 10P/Tempel 2, based on high-dispersion infrared spectra acquired on UT 2010 July 26 (heliocentric distance R h = 1.44 AU) and September 18 (R h = 1.62 AU), following the comet’s perihelion passage on UT 2010 July 04. The total production rate for water on July 26 was (1.90 ± 0.12) × 10 28 molecules s −1 , and abundances of six trace gases (relative to water) were: CH 3 OH (1.58% ± 0.23%), C 2 H 6 (0.39% ± 0.04%), NH 3 (0.83% ± 0.20%), and HCN (0.13% ± 0.02%). A detailed analysis of intensities for water emission lines provided a rotational temperature of 35 ± 3 K. The mean OPR is consistent with nuclear spin populations in statistical equilibrium (OPR = 3.01 ± 0.18), and the (1σ) lower bound corresponds to a spin temperature >38 K. Our measurements were contemporaneous with a jet-like feature observed at optical wavelengths. The spatial profiles of four primary volatiles display strong enhancements in the jet direction, which favors release from a localized vent on the nucleus. The measured IR continuum is much more sharply peaked and is consistent with a dominant contribution from the nucleus itself. The peak intensities for H 2 O, CH 3 OH, and C 2 H 6 are offset by ∼200 km in the jet direction, suggesting the possible existence of a distributed source, such as the release of icy grains that subsequently sublimed in the coma. On UT September 18, no obvious emission lines were present in our spectra, nevertheless we obtained a 3σ upper limit Q(H 2 O) < 2.86 × 10 27 molecules s −1 .

Organic Chemistry↗

DOE OSTI2022

Surface warming in global cities is substantially more rapid than in rural background areas

Warming trends in cities are influenced both by large-scale climate processes and by local-scale urbanization. However, little is known about how surface warming trends of global cities differ from those characterized by weather observations in the rural background. Here, through statistical analyses of satellite land surface temperatures (2002 to 2021), we find that the mean surface warming trend is 0.50 ± 0.20 K·decade –1 (mean ± one S.D.) in the urban core of 2000-plus city clusters worldwide, and is 29% greater than the trend for the rural background. On average, background climate change is the largest contributor explaining 0.30 ± 0.11 K·decade –1 of the urban surface warming. In city clusters in China and India, however, more than 0.23 K·decade –1 of the mean trend is attributed to urban expansion. We also find evidence of urban greening in European cities, which offsets 0.13 ± 0.034 K·decade –1 of background surface warming.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1984

The temperature of C II emission-line formation regions in cool stars

An investigation has been conducted of the temperature of C II emission-line formation regions in the outer atmospheres of late-type giant and supergiant stars. A distinct dichotomy is seen in the C II lambda 2325/lambda 1335 ratio between coronal and noncoronal stars. It is found that C II emission from noncoronal giant and supergiant stars comes from regions with temperatures of 7000-9000 K, with the mean temperature being approximately 8500 K, whereas the C II emission from coronal stars likely comes from hotter regions. The C II ratio provides a powerful empirical tool for estimating the chromospheric temperatures of cool giants and supergiants.

Brown, A.↗

DOE OSTI2020

Low-temperature spin dynamics in the TmFeO 3 orthoferrite with a non-Kramers ion

We investigate the magnetic dynamics of the orthorhombic perovskite TmFeO 3 at low temperatures, below the spin reorientation transition at TSR≈80 K, by means of time-of-flight neutron spectroscopy. We find that the magnetic excitation spectrum combines two emergent collective modes associated with different magnetic sublattices. The Fe subsystem orders below TN~632 K into a canted antiferromagnetic structure and exhibits sharp, high-energy magnon excitations. We describe them using linear spin-wave theory, and reveal a pronounced anisotropy between in- and out-of-plane exchange interactions, which was mainly neglected in previous reports on the spin dynamics in orthoferrites. At lower energies, we find two crystalline electrical field (CEF) excitations of Tm3+ ions at energies of ~2 and 5 meV. In contrast to the sister compound YbFeO 3 , where the Yb 3+ ions form quasi-one-dimensional chains along the c axis, the Tm excitations show dispersion along both directions in the (0KL) scattering plane. Analysis of the neutron scattering polarization factor reveals a longitudinal polarization of the 2 meV excitation. To evaluate the effect of the CEF on the Tm 3+ ions, we perform point-charge model calculations, and their results quantitatively capture the main features of Tm single-ion physics, such as energies, intensities, and polarization of the CEF transitions, and the type of magnetic anisotropy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Dynamics and Structure of Molten Neodymium Titanate

Binary rare earth titanates can be vitrified using levitation melt quenching, and their glasses are promising for optical applications. To better understand their thermophysical properties and dynamics, we have used molecular dynamics (MD) and empirical potential structure refinement (EPSR) to simulate molten neodymium titanate, 83TiO 2 –17Nd 2 O 3 . The models are compared with high-energy synchrotron X-ray diffraction of the melt structure over 1060–1960 K and thermophysical property measurements of electrostatically levitated droplets over 1500–2200 K. As temperature increases, the network periodicity increases and the correlation length decreases, both in monotonic trends that suggest continuous structural evolution through the melt and supercooled liquid regimes. At 1900 K, the mean Ti–O and Nd–O coordination numbers are 5.8(5) and 6.7(5), compared to the glass, 5.72(6) and 7.70(26). The equilibrium melting point is measured as 1860(20) K, over 100 K higher than previous estimates. Viscosity data from MD are in good agreement with experimental measurements. The dynamic propensity of Nd is similar to that of the network-forming Ti, suggesting that Nd does not participate as a conventional modifier. The viscosity of this fragile liquid is likely controlled by the breaking of cation–oxygen bonds and rearrangements of Ti–O polyhedra.

molecular dynamics↗