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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

LocOO3D User's Manual

LocOO3D is a software tool that computes geographical locations for seismic events at regional to global scales. This software has a rich set of features, including the ability to use custom 3D velocity models, correlated observations and master event locations. The LocOO3D software is especially useful for research related to seismic monitoring applications, since it allows users to easily explore a variety of location methods and scenarios and is compatible with the CSS3.0 data format used in monitoring applications. The LocOO3D software, User's Manual, and Examples are available on the web at: https://github.com/sandialabs/LocOO3D For additional information on GeoTess, SALSA3D, RSTT, and other related software, please see: https://github.com/sandialabs/GeoTessJava, www.sandia.gov/geotess, www.sandia.gov/salsa3d, and www.sandia.gov/rstt

58 GEOSCIENCES↗
DOE OSTI2021

Seismic Spatial Gradients and Machine Learning-Based Classifiers for Explosion Monitoring (LDRD 218327)

This final report summarizes the work completed under the Laboratory Directed Research and Development (LDRD) project “Seismic Spatial Gradients as a Machine Learning-Based Classifier for Explosion Monitoring.” The overarching goal of the project was to explore the efficacy of using machine learning-based classification algorithms where the input data are the spatial gradient of the seismic wavefield collected at a single point on the Earth’s surface. The methods that I describe here are in direct contrast to conventional methods of seismic discrimination which typically rely on a spatially extended network of instruments and physics-based wavefield attributes such as, for example, the ratio between $\textit{P}$ and $\textit{S}$ waves. Rather, we use the spatial gradient of the seismic wavefield observed at a single point on the Earth’s surface and data processing approaches inspired by the machine learning community. We tested two algorithms, a neural network and a modified version of principal component analysis termed Spectrally Filtered Principal Component Analysis (SFPCA). To test these algorithms, we first conducted a series of numerical tests using synthetic data and then conducted a small-scale controlled field experiment. The tests using synthetic data showed that both algorithms had high success rates on gradiometric data, even when simulated noise was added to the signal. Furthermore, we found that using seismic spatial gradients increased the performance of our discrimination algorithms when compared to using just the traditional translational motion seismic data. The tests with field data also showed a high degree of discriminative success.

58 GEOSCIENCES↗
DOE OSTI2024

Modulated Thermomechanical Analysis of Compression-Molded High-Density Polyethylene

Thermomechanical analysis (TMA) experiments conducted on high-density polyethylene (HDPE) show both reversible and irreversible dimensional changes. To further explore these reversible and irreversible processes, modulated thermomechanical analysis (MTMA) was used. Before reliable data on compression-molded HDPE was collected, a parameter optimization was performed to obtain a suitable MTMA method. Once a suitable method was obtained, several MTMA experiments were conducted on compression-molded HDPE. This work highlights the steps taken during the MTMA parameter optimization and the results obtained from MTMA experiments conducted on pristine compression-molded HDPE samples.

36 MATERIALS SCIENCE↗
DOE OSTI2024

TrustDER: Trusted, Private and Scalable Coordination of Distributed Energy Resources

In this project, the Stanford and SLAC Teams have developed a Trusted, Private and Scalable platform for coordinating Coordination of Distributed Energy Resources (TrustDER). This is a layered system that ensures private, trusted and scalable coordination and monitoring of DERs. It accommodates a variety of resources, such as solar generation, gensets and loads, with a particular focus on battery systems-based resources, as they are a transformational technology experiencing fast growth in adoption by large critical facilities. The platform can be used as standalone or added to existing aggregation systems to enable trust, privacy and resilience. TrustDER consists of layers that address each of the shortcomings of the existing state of the art. Each layer in the platform can operate independently but provides information to the layers above it to enable a novel form of overall coordination architecture. The project consists of several tasks, with each task dedicated to the design of each layer. Task 2 Resource Virtualization defined a software abstraction layer for distributed energy resources (DERs). The goal of this abstraction was to simplify the implementation of algorithms utilizing cooperation of DERs resources in a variety of use cases. Task 3 is on Secure ID for Asset Authentication. Identity Management Systems (IDMS) are a foundational infrastructure for interactions between entities (organizations, users, devices, and services). Secure ID is blockchain-based a distributed identity management system allowing (1) identity provisioning, (2) authentication, (3) authorization, and (4) identity data sharing for IoT-enabled assets on the electricity grid. In this project, the SLAC team focused on designing and testing Keymaker, a protocol for authenticating device identity managed by Secure ID. Task 5 Private and Safe Integration is focused on the design and evaluation of a DER cooperation scheme which allows for the aggregation of DERs without impacting network reliability. The approach is designed based on realistic assumptions regarding data availability, communication infrastructure limitations, and privacy. Task 6 Scalable Distributed Privacy for Information explored how virtualized batteries could be managed privately. Specifically, it examined the case in which a principal provides a partitioned battery to multiple clients. Task 7 Use Cases was to ensure that this technology was applied in relevant situations and scenarios. Primarily, this means that virtualization needed to be employed in a manner that either improved flexibility, bolstered security or privacy, or decreased costs.

25 ENERGY STORAGE↗
DOE OSTI2025

New Results on Communication- and Memory-Aware Load Balancing Model and Algorithms

While load balancing in distributed-memory computing has been well-studied, we present an innovative approach to this problem: a unified, reduced-order model that combines three key components to describe “work” in a distributed system: computation, communication, and memory. Our model enables an optimizer to explore complex tradeoffs in task placement, such as augmented parallelism, at the expense of data replication increasing memory usage. We propose a fully distributed, heuristic-based load balancing optimization algorithm, and demonstrate that it quickly finds close-to-optimal solutions. We formalize the complex optimization problem as a mixed-integer linear program, and compare it to our strategy. Finally, we show that when applied to an electromagnetics code, our approach obtains up to 2.3x speedups for the imbalanced execution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Effect of Production Bias on Radiation-Induced Segregation in Ni-Cr Alloys

We present an in-depth investigation into the Radiation-Induced Segregation (RIS) phenomenon in Ni-Cr alloys. All the pivotal factors affecting RIS such as surface’s absorption efficiency, grain size, production bias, dose rate, temperature, and sink density were systematically studied. Through comprehensive simulations, the individual and collective impacts of these factors were analyzed, enabling a refined understanding of RIS. A notable finding was the significant influence of production bias on point defects’ interactions with grain boundaries/surfaces, thereby playing a crucial role in RIS processes. Production bias alters the neutrality of these interactions, leading to a preferential absorption of one type of point defect by the boundary and consequent establishment of distinct surface-mediated patterns of point defects. These spatial patterns further result in non-monotonic spatial profiles of solute atoms near surfaces/grain boundaries, corroborated by experimental observations. In particular, a positive production bias, signifying a higher production rate of vacancies over interstitials, drives more Cr depletion at the grain boundary. Moreover, a temperature-dependent production bias must be considered to recover the experimentally reported dependence of RIS on temperature. The severity of radiation damage and RIS becomes more pronounced with increased production bias, dose rate, and grain size, while high temperatures or sink density suppress the RIS severity. Model predictions were validated against experimental data, showcasing robust qualitative and quantitative agreements. The findings pave the way for further exploration of these spatial dependencies in subsequent studies, aiming to augment the comprehension and predictability of RIS processes in alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2026

NASA’s Pandora SmallSat Mission: Simulated Modeling and Retrieval of Near-infrared Exoplanet Transmission Spectra

Pandora is a SmallSat mission dedicated to understanding exoplanets and their host stars by disentangling the impact of stellar heterogeneity on exoplanet transmission spectra. Selected as a NASA Astrophysics Pioneers mission in 2021, Pandora will provide simultaneous long-term visible photometric monitoring (0.4–0.7 μm) and low-resolution near-infrared (NIR) spectroscopy (0.9–1.6 μm) of transiting systems for the purposes of monitoring host star variability and characterizing exoplanetary atmospheres. Pandora’s year-long prime mission from 2026 to 2027 coincides with the middle of a decade defined by targeted efforts for atmospheric characterization of exoplanets, offering a key opportunity to leverage this new resource to maximize science with JWST and other observatories. Here we investigate Pandora’s anticipated performance for the general exoplanet population accessible to transit spectroscopy, from hot Jupiters to temperate sub-Neptunes. By modeling the atmospheres of five test cases broadly consistent with the bulk properties of HD 209458b, HD 189733b, WASP-80 b, HAT-P-18 b, and K2-18 b, we find that Pandora may provide abundance constraints as precise as ∼1.0 dex for main atmospheric absorbers such as H 2 O and CH 4 . Then, we explore the synergies between Pandora and JWST. Our results suggest that targets with JWST data in the NIR can benefit from the addition of Pandora observations and result in more reliable abundance estimates than with JWST data alone. Moreover, Pandora can serve the community by providing precursory observations of targets of interest for JWST atmospheric characterization. We conclude by outlining strategies for the use of Pandora as a standalone observatory and in synergy with JWST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design

Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a multipurpose 10 yr optical survey of the Southern Hemisphere sky: the Legacy Survey of Space and Time. Significant flexibility in survey strategy remains within the constraints imposed by the core science goals of probing dark energy and dark matter, cataloging the solar system, exploring the transient optical sky, and mapping the Milky Way. The survey’s massive data throughput will be transformational for many other astrophysics domains and Rubin’s data access policy sets the stage for a huge community of potential users. To ensure that the survey science potential is maximized while serving as broad a community as possible, Rubin Observatory has involved the scientific community at large in the process of setting and refining the details of the observing strategy. The motivation, history, and decision-making process of this strategy optimization are detailed in this paper, giving context to the science-driven proposals and recommendations for the survey strategy included in this Focus Issue.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

MSD CoP Webinar: Quantum Computing Futures through a Multisector Lens

Context: This webinar featured two presentations examining quantum computing and its complex implications across the energy, water, and materials sectors. Olivier Ezratty introduced quantum computing, its anticipated applications and added value, and the hardware required to support these systems. He also discussed their energy demands and the role of the Quantum Energy Initiative in developing an interdisciplinary research field focused on these challenges. David McCollum then explored the opportunities and multisectoral challenges associated with next-generation, quantum-accelerated data centers, including their potential energy and resource impacts and the infrastructure chokepoints that could emerge. Together, the presentations emphasized the need for long-term planning and cross-cutting research collaboration as quantum computing technologies continue to develop. Presenters: Olivier Ezratty (Quantum Energy Initiative); David McCollum (Oak Ridge National Laboratory) Moderator: Patrick M. Reed (MSD CoP Facilitation Team); Gokul Iyer (Pacific Northwest National Laboratory) This webinar was held on: July 9th, 2026 from 1:00–2:30 PM EDT.

Quantum Computing↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
DOE OSTI2021

Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

Allen, Benjamin↗
DOE OSTI2020

Incorporating uncertainty for enhanced leaderboard scoring and ranking in data competitions

Data competitions have become a popular and cost-effective approach for crowdsourcing versatile solutions from diverse expertise. Current practice relies on the simple leaderboard scoring based on a given set of competition data for ranking competitors and distributing the prize. However, a disadvantage of this practice in many competitions is that a slight difference in the scores due to the natural variability of the observed data could result in a much larger difference in the prize amounts. In this article, we propose a new strategy to quantify the uncertainty in the rankings and scores from using different data sets that share common characteristics with the provided competition data. By using a bootstrap approach to generate many comparable data sets, the new method has four advantages over current practice. Furthermore, during the competition, it provides a mechanism for competitors to get feedback about the uncertainty in their relative ranking. After the competition, it allows the host to gain a deeper understanding of the algorithm performance and their robustness across representative data sets. It also offers a transparent mechanism for prize distribution to reward the competitors more fairly with superior and robust performance. Finally, it has the additional advantage of being able to explore what results might have looked like if competition goals evolved from their original choices. The implementation of the strategy is illustrated with a real data competition hosted by Topcoder on urban radiation search.

42 ENGINEERING↗
DOE OSTI2025

Validation of the DESI 2024 Lyman alpha forest BAL masking strategy

Broad absorption line quasars (BALs) exhibit blueshifted absorption relative to a number of their prominent broad emission features. These absorption features can contribute to quasar redshift errors and add absorption to the Lyman-α (Lyα) forest that is unrelated to large-scale structure. We present a detailed analysis of the impact of BALs on the Baryon Acoustic Oscillation (BAO) results with the Lyα forest from the first year of data from the Dark Energy Spectroscopic Instrument (DESI). The baseline strategy for the first year analysis is to mask all pixels associated with all BAL absorption features that fall within the wavelength region used to measure the forest. We explore a range of alternate masking strategies and demonstrate that these changes have minimal impact on the BAO measurements with both DESI data and synthetic data. This includes when we mask the BAL features associated with emission lines outside of the forest region to minimize their contribution to redshift errors. We identify differences in the properties of BALs in the synthetic datasets relative to the observational data, as well as use the synthetic observations to characterize the completeness of the BAL identification algorithm, and demonstrate that incompleteness and differences in the BALs between real and synthetic data also do not impact the BAO results for the Lyα forest.

Lyman alpha forest↗
DOE OSTI2023

Galactic ArchaeoLogIcaL ExcavatiOns (GALILEO) II. t-SNE portrait of local fossil relics and structures

Based on high-quality Apache Point Observatory Galactic Evolution Experiment (APOGEE) DR17 and Gaia DR3 data for 1742 red giants stars within 5 kpc of the Sun and not rotating with the Galactic disk (V φ < 100 km s -1 ), we used the nonlinear technique of unsupervised analysis t-Distributed Stochastic Neighbor Embedding (t-SNE) to detect coherent structures in the space of ten chemical-abundance ratios: [Fe/H], [O/Fe], [Mg/Fe], [Si/Fe], [Ca/Fe], [C/Fe], [N/Fe], [Al/Fe], [Mn/Fe], and [Ni/Fe]. Additionally, we obtained orbital parameters for each star using the nonaxisymmetric gravitational potential GravPot16. Seven structures are detected, including Splash, Gaia-Sausage-Enceladus (GSE), the high-α heated-disk population, N-C-O peculiar stars, and inner disk-like stars, plus two other groups that did not match anything previously reported in the literature, here named Galileo 5 and Galileo 6 (G5 and G6). These two groups overlap with Splash in [Fe/H], with G5 having a lower metallicity than G6, and they are both between GSE and Splash in the [Mg/Mn] versus [Al/Fe] plane, with G5 being in the α-rich in situ locus and G6 on the border of the α-poor in situ one. Nonetheless, their low [Ni/Fe] hints at a possible ex situ origin. Their orbital energy distributions are between Splash and GSE, with G5 being slightly more energetic than G6. We verified the robustness of all the obtained groups by exploring a large range of t-SNE parameters, applying it to various subsets of data, and also measuring the effect of abundance errors through Monte Carlo tests.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Data for Zheng et al. (2025), "AquaMEND: Reconciling multiple impacts of salinization on soil carbon biogeochemistry"

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystems and soil function. Salinity affects soil carbon cycling by directly impacting microbial activity and indirectly altering soil physicochemical properties, but current models inadequately represent these complexities. This dataset contains the observational and modeling data from Zheng et al. (2025), which described a process-based modeling framework that couples soil solution chemistry with microbial carbon cycling reactions to study the impacts of soil salinization. This conceptual model is implemented numerically into the open-source geochemical program PHREEQC 3.0 (Parkhurst and Appelo, 2013). This dataset consists of: - Figure2_AquaMEND_salinity_buffer: Contains model simulation outputs to assess the impact of three different cation exchange and surface complexation processes on salinity buffering (Fig. 2 from Zheng et al. 2025). - Figure3_Salinity_function: Contains salinity function fitting for literature data (Fig. 3 from Zheng et al. 2025). - Figure4_AquaMEND_microbial_mechanisms: Contains model simulation outputs for testing various microbial process-based hypotheses related to soil salinization, including microbial mortality, carbon use efficiency (CUE), extracellular enzyme activity, and other microbial mechanisms (Fig. 4 from Zheng et al. 2025). - Figure5_AquaMEND_Redox: Contains on model simulation outputs to evaluate shifts among key redox processes, such as aerobic respiration, sulfate reduction, and methanogenesis (Fig.5 from Zheng et al. 2025). - Figure6_AquaMEND_sorption: Contains on model simulation outputs for investigating the effects of salinity on dissolved organic matter (DOM) sorption and desorption processes (Fig. 6 from Zheng et al. 2025). - Figure7_AquaMEND_process_couple: Contains on model simulation outputs for exploring coupled biotic-abiotic processes and their interactions (Fig. 7 from Zheng et al. 2025). - data: Includes datasets used to develop salinity response functions and evaluate salinity buffering capacity. Datasets for MEND model calibration. - database: Contains the `.dat` file required by PHREEQC for model execution. - README.md: A Markdown plain text file describing the computational tools and directories. Files are a mixture of plain text CSV (comma-separated value) and plain text *.dat files written by the model; no special software is required to read them.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL SALINIT↗
DOE OSTI2025

Accelerating Nuclear-Integrated Data Centers in the USA: SWOT Analysis, Power-Thermal Management Strategies, and Industrial-Scale Demonstration and Potential Deployment

Driven by the growth in digital services, cloud computing, AI, and manufacturing, data centers face rising energy demands that challenge traditional power sources and cooling efficiency. This study explores using nuclear power to meet these demands, focusing on accelerated reactor technology deployment and highlighting needs such as N+1/N+2 power supplies and integrated power-thermal management. A SWOT analysis addresses grid connectivity, reactors, and site selection, particularly DOE sites. Reactor technology demonstration and deployment could be accelerated by leveraging test facilities such as MARVEL, MAGNET, TED, FAS, DOME, LOTUS, ATR, Energy System Proving Grounds, and upcoming Energy Launch Pads, along with modeling and simulation tools such as RELAP5, MOOSE, VERA, RAVEN, and FORCE. The potential power and thermal management options, including various cooling technologies, waste-heat utilization, and an industrial-scale demonstration plan, aim to accelerate the integration of nuclear power and data centers in the USA, while emphasizing community and stakeholder engagement and synergistic efforts.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

Autoregressive long-horizon prediction of plasma edge dynamics *

Accurate modeling of scrape-off layer (SOL) and divertor-edge dynamics is vital for designing plasma-facing components in fusion devices. High-fidelity edge fluid/neutral codes such as SOLPS-ITER capture SOL physics with high accuracy, but their computational cost limits broad parameter scans and long transient studies. We present transformer-based, autoregressive surrogates for efficient prediction of 2D, time-dependent plasma edge state fields. Trained on SOLPS-ITER spatiotemporal data for the KSTAR tokamak, the surrogates forecast electron temperature, electron density, and radiated power over extended horizons. We evaluate model variants trained with increasing autoregressive horizons (1–100 steps) on short- and long-horizon prediction tasks. Longer-horizon training systematically improves rollout stability and mitigates error accumulation, enabling stable predictions over hundreds to thousands of steps and reproducing key dynamical features such as the motion of high-radiation regions. Measured end-to-end wall-clock times show the surrogate is orders of magnitude faster than SOLPS-ITER, enabling rapid parameter exploration. Prediction accuracy degrades when the surrogate enters physical regimes not represented in the training dataset, motivating future work on data enrichment and physics-informed constraints. Overall, this approach provides a fast, accurate surrogate for computationally intensive plasma edge simulations, supporting rapid scenario exploration, control-oriented studies, and progress toward real-time applications in fusion devices.

autoregressive deep learning↗
DOE OSTI2021

A D-term Modeling Code (DMC) for Simultaneous Calibration and Full-Stokes Imaging of Very Long Baseline Interferometric Data

In this paper we present DMC, a model and associated tool for polarimetric imaging of very long baseline interferometry data sets that simultaneously reconstructs the full-Stokes emission structure along with the station-based gain and leakage calibration terms. DMC formulates the imaging problem in terms of posterior exploration, which is achieved using Hamiltonian Monte Carlo sampling. The resulting posterior distribution provides a natural quantification of uncertainty in both the image structure and the data calibration. We run DMC on both synthetic and real data sets, the results of which demonstrate its ability to accurately recover both the image structure and calibration quantities, as well as to assess their corresponding uncertainties. The framework underpinning DMC is flexible, and its specific implementation is under continued development.

47 OTHER INSTRUMENTATION↗