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At least 415 records · Page 23

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DOE OSTI2022

A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks

We compare various methods for resolving steady flow within three-dimensional discrete fracture networks, including direct methods, Krylov subspace methods with and without preconditioning, and multi-grid methods. We compared the performance of the methods based on compute times and scaling of the solution as a function of the number of grid nodes and log-variance of the hydraulic aperture. The methods are applied to three test cases: (a) variable density of networks with a truncated power-law distribution of fracture lengths, (b) a fixed network composed of monodisperse fracture sizes but varied permeability/aperture heterogeneity, (c) and a network based on field site in Nevada, US. We chose these cases to allow us to study the impact of the mesh size and flow properties, as well as to demonstrate our conclusions on a large-scale, realistic problem (more than 40 million mesh nodes). A direct solution using Cholesky factorization outperformed other methods for every example but was closely followed in performance by some algebraic multigrid (AMG) preconditioned Krylov subspace methods. Among the Krylov methods, conjugate gradients (CG) with an AMG preconditioner performs the best. Generally, Cholesky factorization is recommended, but CG with an AMG preconditioner may be suitable for very large problems beyond 40 million nodes where the entire linear system cannot reside in memory.

58 GEOSCIENCES↗

DOE OSTI2023

Towards an astronomical foundation model for stars with a transformer-based model

ABSTRACT Rapid strides are currently being made in the field of artificial intelligence using transformer-based models like Large Language Models (LLMs). The potential of these methods for creating a single, large, versatile model in astronomy has not yet been explored. In this work, we propose a framework for data-driven astronomy that uses the same core techniques and architecture as used by LLMs. Using a variety of observations and labels of stars as an example, we build a transformer-based model and train it in a self-supervised manner with cross-survey data sets to perform a variety of inference tasks. In particular, we demonstrate that a single model can perform both discriminative and generative tasks even if the model was not trained or fine-tuned to do any specific task. For example, on the discriminative task of deriving stellar parameters from Gaia XP spectra, we achieve an accuracy of 47 K in Teff, 0.11 dex in log g, and 0.07 dex in [M/H], outperforming an expert XGBoost model in the same setting. But the same model can also generate XP spectra from stellar parameters, inpaint unobserved spectral regions, extract empirical stellar loci, and even determine the interstellar extinction curve. Our framework demonstrates that building and training a single foundation model without fine-tuning using data and parameters from multiple surveys to predict unmeasured observations and parameters is well within reach. Such ‘Large Astronomy Models’ trained on large quantities of observational data will play a large role in the analysis of current and future large surveys.

Leung, Henry W. (ORCID:0000000200362752)↗

DOE OSTI2020

Asynchronous Ballistic Reversible Computing using Superconducting elements

Computing uses energy. At the bare minimum, erasing information in a computer increases the entropy. Landauer has calculated %7E k B T log(2) Joules is dissipated per bit of energy erased. While the success of Moores law has allowed increasing computing power and efficiency for many years, these improvements are coming to an end. This project asks if there is a way to continue those gains by circumventing Landauer through reversible computing. We explore a new reversible computing paradigm, asynchronous ballistic reversible computing or ABRC. The ballistic nature of data in ABRC matches well with superconductivity which provides a low-loss environment and a quantized bit encoding the fluxon. We discuss both these and our development of a superconducting fabrication process at Sandia. We describe a fully reversible 1-bit memory cell based on fluxon dynamics. Building on this model, we propose several other gates which may also offer reversible operation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

NREL’s Cyber-Energy Emulation Platform for Research and System Visualization

This paper presents NREL’s Cyber Energy Emulation (CEE) Platform. This is a novel emulation platform for achieving real-time visualization of large-scale environments involving cyber-physical devices. It allows for the environment to include real, physical hardware, along with emulated devices communicating with each other as part of the same system. The CEE Platform is also capable of streaming, collecting, storing, transporting, and visualizing all data within the emulated environment. By providing this capability, it enables high-fidelity visual analysis of events to be performed in real time as well as the use of historical data for forensic analysis. This paper presents the design of the CEE Platform as well as several potential use cases and applications. It also aims to highlight why this type of visualization tool has potential for research and education about cyber-physical systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Forest Structural Complexity and Biomass Predict First-Year Carbon Cycling Responses to Disturbance

The pre-disturbance vegetation characteristics that predict carbon (C) cycling responses to disturbance are not well known. To address this gap, we initiated the Forest Resilience Threshold Experiment, a manipulative study in which more than 3600 trees were stem girdled to achieve replicated factorial combinations of four levels (control, 45, 65, and 85% gross defoliation) of disturbance severity and two disturbance types (targeting upper or lower canopy strata). Applying a standardized stability framework in which initial C cycling resistance to disturbance was calculated as the first-year natural log response ratio of disturbance and control treatments, we investigated to what extent pre-disturbance levels of species diversity, aboveground woody biomass, leaf area index, and canopy rugosity—a measure of structural complexity—predict the initial responses of subcanopy light-saturated leaf CO 2 assimilation (A sat ), aboveground wood NPP (ANPP w ), and soil respiration (R s ) to phloem-disrupting disturbance. In the year following stem girdling, we found that above-ground C cycling processes, A sat and ANPP w , were highly resistant to increases in disturbance severity, while Rs resistance declined as severity increased. Disturbance type had no effect on first-year resistance. Pre-disturbance aboveground woody biomass, and canopy rugosity were positive predictors of ANPP w resistance and, conversely, negatively related to R s resistance. Subcanopy A sat resistance was not related to pre-disturbance vegetation characteristics. Stability of C uptake processes along with R s declines suggest the net C sink was sustained in the initial months following disturbance. Therefore, we conclude that biomass and complexity are significant, but not universal, predictors of initial C cycling resistance to disturbance. Moreover, our findings highlight the utility of standardized stability measures when comparing functional responses to disturbance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Melting temperature measurement of refractory oxide ceramics as a function of oxygen fugacity using containerless methods

An aerodynamic conical nozzle levitator (CNL) has been used to measure the melting temperatures of refractory ceramics. The well-established method of cooling traces has been adapted to allow measurements for air-sensitive samples using a controlled atmosphere, where the oxygen partial pressure can be adjusted and monitored in real time. In this study, we explain the details of a system that we developed for use at the Spallation Neutron Source (Oak Ridge National Laboratory). Measurements of the melting behavior of (Fe,Cr,Al) 3 O 4 spinels were made between log (PO 2 (atm)) of -0.7 and -5. The melting temperature of hercynite is found to be oxygen potential dependent, ranging between 1692 and 1767°C at log (PO 2 ) values from -4.42 to -0.70.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Gas hydrate petroleum systems: What constitutes the “seal”?

The gas hydrate petroleum system (GHPS) approach, which has been used to characterize gas hydrates in nature, uses three distinct components: a methane source, a methane migration pathway, and a reservoir that not only contains gas hydrate, but also acts as a seal to prevent methane loss. Unlike GHPS, a traditional petroleum system (PS) approach further distinguishes between the reservoir, a unit with generally coarser sediment grains, and a separate overlying seal unit with generally finer sediment grains. Adopting this traditional PS distinction in the GHPS approach facilitates assessments of reservoir growth and production potential. The significance of the seal for the formation of a gas hydrate reservoir as well as for efficiency in methane extraction from the reservoir as an energy resource is evident in findings from recent offshore field expeditions, such as India’s second National Gas Hydrate Program expedition (NGHP-02). In regard to gas hydrate-bearing reservoir formations, the NGHP-02 gas chemistry data indicate a primarily microbial methane source. Fine-grained seal sediment in contact with coarser grained reservoir sediment can facilitate that microbial methane production. Logging-while-drilling and sediment core data also indicate that the overlying fine-grained seal sediment is less permeable than the underlying, highly gas hydrate-saturated reservoir sediment. The overlying seal’s capacity to act as a low-permeability boundary is important not only for preventing methane migration out of the reservoir over time, but also for preventing water invasion into the reservoir during methane extraction from the reservoir. Ultimately, the presence of an overlying, fine-grained, low-permeability “seal” influences how gas hydrate initially forms in a coarse-grained reservoir and dictates how efficiently methane can be extracted as an energy resource from the gas hydrate reservoir via depressurization.

Geochemistry & Geophysics↗

DOE OSTI2020

Measurement of single-diffractive dijet production in proton-proton collisions at $\sqrt{s} =$ 8 TeV with the CMS and TOTEM experiments

Measurements are presented of the single-diffractive dijet cross section and the diffractive cross section as a function of the proton fractional momentum loss $\xi $ and the four-momentum transfer squared t. Both processes ${\text{ p }{}{}} {\text{ p }{}{}} \rightarrow {\text{ p }{}{}} {\text{ X }} $ and ${\text{ p }{}{}} {\text{ p }{}{}} \rightarrow {\text{ X }} {\text{ p }{}{}} $, i.e. with the proton scattering to either side of the interaction point, are measured, where ${\text{ X }} $ includes at least two jets; the results of the two processes are averaged. The analyses are based on data collected simultaneously with the CMS and TOTEM detectors at the LHC in proton–proton collisions at $\sqrt{s} = 8\,\text {Te}\text {V} $ during a dedicated run with $\beta ^{*} = 90\,\text {m} $ at low instantaneous luminosity and correspond to an integrated luminosity of $37.5{\,\text {nb}^{-1}} $. The single-diffractive dijet cross section $\sigma ^{{\text{ p }{}{}} {\text{ X }}}_{\mathrm {jj}}$, in the kinematic region $\xi < 0.1$, $0.03< |t | < 1\,\text {Ge}\text {V} ^2$, with at least two jets with transverse momentum $p_{\mathrm {T}} > 40\,\text {Ge}\text {V} $, and pseudorapidity $|\eta | < 4.4$, is $21.7 \pm 0.9\,\text {(stat)} \,^{+3.0}_{-3.3}\,\text {(syst)} \pm 0.9\,\text {(lumi)} \,\text {nb} $. The ratio of the single-diffractive to inclusive dijet yields, normalised per unit of $\xi $, is presented as a function of x, the longitudinal momentum fraction of the proton carried by the struck parton. The ratio in the kinematic region defined above, for x values in the range $-2.9 \le \log _{10} x \le -1.6$, is $R = (\sigma ^{{\text{ p }{}{}} {\text{ X }}}_{\mathrm {jj}}/\Delta \xi )/\sigma _{\mathrm {jj}} = 0.025 \pm 0.001\,\text {(stat)} \pm 0.003\,\text {(syst)} $, where $\sigma ^{{\text{ p }{}{}} {\text{ X }}}_{\mathrm {jj}}$ and $\sigma _{\mathrm {jj}}$ are the single-diffractive and inclusive dijet cross sections, respectively. The results are compared with predictions from models of diffractive and nondiffractive interactions. Monte Carlo predictions based on the HERA diffractive parton distribution functions agree well with the data when corrected for the effect of soft rescattering between the spectator partons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Temperature Uncertainty Modeling with Proxy Structural Data as Geostatistical Constraints for Well Siting: An Example Applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aim of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations . The data used to support the modeling are measured temperatures and structural proxies from 3D geologic modeling, the latter considered "secondary" data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally likely temperature models (i.e., realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: Vprior and entropy. Vprior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e., linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g., dilation versus distance to fault termination) should be used given the specific geothermal system. In general, Vprior from local varying mean results identify locations that are close to high values for the structural proxies: areas with highe r probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comp arison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

3D temperature modeling↗

DOE OSTI2022

Improving magnitude and phase comparison metrics for frequency response functions using cross-correlation and log-frequency shifting

This paper provides a new method for matching dominant features of Frequency Response Functions (FRFs). In particular, this paper proposes a slicing and shifting method where the key features (in this case, resonant amplitudes) of a baseline FRF are compared with a similar FRF using cross-correlation and a Log-Frequency Shift (LFS). Here, the goal is to provide an alternative to classic point-to-point methods for FRF comparison and instead to match the dominant features of two FRFs in a way similar to visual inspection. This enables existing FRF comparison metrics to then be applied with greater fidelity. Additionally, this paper introduces the Phase Similarity Metric (PSM) for comparing phases of two FRFs and illustrates the improvement of using LFS for phase comparisons as well. This paper uses a cantilever beam experiment in multiple configurations to provide a benchmark case for FRF comparison improvements.

42 ENGINEERING↗

DOE OSTI2022

SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library

The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Stellar Library (MaStar) is a large collection of high-quality empirical stellar spectra designed to cover all spectral types and ideal for use in the stellar population analysis of galaxies observed in the MaNGA survey. The library contains 59,266 spectra of 24,130 unique stars with spectral resolution R ~ 1800 and covering a wavelength range of 3622–10,354 Å. In this work, we derive five physical parameters for each spectrum in the library: effective temperature (T eff ), surface gravity ($\mathrm{log} g$), metallicity ([Fe/H]), microturbulent velocity ($\mathrm{log}({v}_{\mathrm{micro}})$), and alpha-element abundance ([α/Fe]). These parameters are derived with a flexible data-driven algorithm that uses a neural network model. We train a neural network using the subset of 1675 MaStar targets that have also been observed in the Apache Point Observatory Galactic Evolution Experiment (APOGEE), adopting the independently-derived APOGEE Stellar Parameter and Chemical Abundance Pipeline parameters for this reference set. For the regions of parameter space not well represented by the APOGEE training set (7000 ≤ T ≤ 30,000 K), we supplement with theoretical model spectra. We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

EGS Stimulation Design with Uncertainty Quantification at the EGS Collab Site

Engineering a robust hydraulic connection between wells is one of the most difficult aspects of enhanced geothermal systems (EGS). Designing and constructing such hydraulic connections requires and understanding of the in situ state of stress and the heterogeneities and discontinuities that naturally exist and may control the stimulation. Even with comprehensive stress and formation characterization programs substantial uncertainty remains in these key parameters. This is especially the case in high-temperature EGS environments where drilling conditions are often difficult and a far fewer logging and testing options are available. This paper presents a new approach for explicitly quantifying the uncertainties in the state of stress using a Bayesian Markov Chain Monte Carlo method. This approach produces a probability distribution for the stress tensor, including a general 3D orientation, that reflects the uncertainties in all the observations or indicators used to constrain the stress state. This method is demonstrated on the characterization data for the EGS Collab Experiment 2 site. The output of the analysis is used to guide the design of the planned stimulations. In the case of research projects like EGS Collab, explicitly quantifying the uncertainties in the stress state allow for more rigorous hypothesis testing by allowing conclusions drawn from the experiments to be interpreted in the context of the uncertain knowledge about conditions in the test bed.

Burghardt, Jeffrey A.↗

DOE OSTI2025

Connecting In Situ Stress and Wellbore Deviation to Near-Well Fracture Complexity Using Phase-Field Simulations

The interactions among in situ stress, rock fabric, wellbore geometry, natural fractures, and other natural or man-made defects create highly complex fracture trajectories in the near-wellbore region, far more intricate than those in the far-field. These near-wellbore complexities are critical for the Utah FORGE project and Enhanced Geothermal Systems (EGS) in general. Frictional pressure loss in the near-wellbore region during stimulation can significantly influence the growth of far-field fractures, while pressure losses during circulation serve as a major source of energy dissipation. Near-wellbore fracture complexities are often observable through image logs, offering valuable insights into in situ stress characteristics. However, leveraging this information requires a high-fidelity model capable of capturing the interplay among the diverse factors influencing fracture behavior.

58 GEOSCIENCES↗

DOE OSTI2021

The dark matter haloes of HI selected galaxies

We present the neutral hydrogen mass (M HI) function (HIMF) and velocity width (w 50 ) function (HIWF) based on a sample of 7857 galaxies from the 40 per cent data release of the ALFALFA survey (α.40). The low mass (velocity width) end of the HIMF (HIWF) is dominated by the blue population of galaxies whereas the red population dominates the HIMF (HIWF) at the high mass (velocity width) end. We use a deconvolution method to estimate the HI rotational velocity (V rot ) functions (HIVF) from the HIWF for the total, red, and blue samples. The HIWF and HIVF for the red and blue samples are well separated at the knee of the function compared to their HIMFs. We then use recent stacking results from the ALFALFA survey to constrain the halo mass (M h ) function of HI-selected galaxies. This allows us to obtain various scaling relations between M HI –w 50 –V rot –M h , which we present. The M HI –M h relation has a steep slope ~2.10 at small masses and flattens to ~0.34 at masses larger than a transition halo mass, $\log _{10}(M_{\rm{ht}}h_{70}^2/M_{\odot })=10.62$. Our scaling relation is robust and consistent with a volume-limited sample of α.40. The M HI –M h relation is qualitatively similar to the M star –M h relation but the transition halo mass is smaller by ~1.4 dex compared to that of the M star –M h relation. Our results suggest that baryonic processes like heating and feedback in larger mass haloes suppress HI gas on a shorter time-scale compared to star formation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

G-LiHT Campaign Leaf Carbon and Nitrogen Content, Mar2017: Puerto Rico

Measurements of leaf carbon and nitrogen content collected from 68 tropical tree species. Data includes leaves collected from fully sunlit and shaded canopy strata as well as leaves for young, mature, old and senescent leaf ages. Data for each sample includes the relative age estimate, leaf canopy position and sample number. This data was collected as part of the 2017 NGEE-Tropics / NASA G-LiHT airborne campaign. This data package includes processed data for leaf carbon and nitrogen content (*.csv). Metadata files include data description (_dd.csv) for tabular data, site information (*.csv), sampling protocol (*.pdf) and the NGEE-Tropics FRAMES e-field log and file submission metadata (*.xlsx). See related datasets for sample details including photographs, leaf-level reflectance and transmittance spectra, leaf mass per area (LMA) and water content.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

G-LiHT Campaign Leaf Carbon and Nitrogen Content, Mar2017: Puerto Rico

54 ENVIRONMENTAL SCIENCES↗