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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.

copolymers↗
DOE OSTI2024

Proposed crystal structure of carbadox, C 11 H 10 N 4 O 4

A model for the crystal structure of carbadox has been generated and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Carbadox crystallizes in space groupP2 1 (#4) witha= 13.8155(3),b= 21.4662(1),c= 16.3297(3) Å,β= 110.0931(7)°,V= 4548.10(3) Å 3 , andZ= 16. The crystal structure is characterized by approximately parallel stacking of the eight independent carbadox molecules parallel to thebc-plane. There are two different molecular configurations of the eight carbadox molecules; five are in the lower-energy configuration and three are in a ~10% higher-energy configuration. This arrangement likely achieves the lowest-energy crystalline packing via hydrogen bonding. Hydrogen bonds link the molecules both within and between the planes. Each of the amino groups forms a N–H⋯O hydrogen bond to an oxygen atom of the 1,4-dioxidoquinoxaline ring system of another molecule. The result is four pairs of hydrogen-bonded molecules, which form rings with graph setR2,2(14). Variation in specimen preparation can affect the preferred orientation of particles considerably. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

Materials Science↗
DOE OSTI2024

Irradiation creep model for α-Fe: Dislocation climb in the presence of stress fields (Final Report)

Irradiation creep is known to be an important process for structural materials in nuclear environments, potentially leading to creep failure at temperatures where thermal creep is generally negligible. While there is a great deal of data for irradiation creep in steels and zirconium alloys in light water reactor conditions, much less is known for first wall materials under fusion energy conditions. Lacking suitable fusion neutron sources for detailed experimentation, modeling, and simulation can help bridge the dose-rate and spectral-effects gap and produce quantifiable expectations for creep deformation of first wall materials under standard fusion conditions. In this work, we present a comprehensive model for irradiation creep created from merging a crystal plasticity representation of the dislocation microstructure and a defect evolution simulator that accounts for the entire cluster dimensionality space. Both approaches are linked by way of a climb velocity that captures dislocation-biased defect absorption and a dislocation strengthening term that reflects the accumulation of defect clusters in the system. We carry out our study in Fe under first wall fusion reactor conditions, characterized by a fusion neutron spectrum at temperatures between 300 and 800°K.

36 MATERIALS SCIENCE↗
DOE OSTI2024

History and State of the Art in Advanced Thermal Protection Systems

This report documents the current state of the art of thermal protection systems (TPS) relevant to missile application materials and MTCR control language. The information is intended to provide data on current TPS for determining the current state of development and identifying potential research directions. Various vehicle configurations and types of TPS are described, including the evolution of materials and systems. Vehicle configurations such as high drag rocket and capsule systems and low-drag air-breathers are discussed. TPS are complex, transitioning from often very low temperatures to temperatures exceeding 3000°C and heat fluxes of up to 30,000 W/cm 2 . They can take the form of blankets, sheets, tiles and fabricated rigid structures, and can be made from high temperature capable alloys, refractory metals, ceramics, ceramic matrix composites, ultra-high temperature ceramics, carbon-carbon composites or ablative carbonaceous materials. Current state of the art in TPS is based on materials any systems developed for the initial space program. TPS was upgraded to the point of the Space Shuttle development and has stagnated since them.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Topological Signature of Stratospheric Poincaré-Gravity Waves

The rotation of Earth breaks time-reversal and reflection symmetries in an opposite sense north and south of the equator, leading to a topological origin for certain atmospheric and oceanic equatorial waves. Away from the equator, the rotating shallow-water and stably stratified primitive equations exhibit Poincaré inertia–gravity waves that have nontrivial topology as evidenced by their strict superinertial time scale and a phase singularity in frequency–wavevector space. This nontrivial topology then predicts, via the principle of bulk-interface correspondence, the existence of two equatorial waves along the equatorial interface, the Kelvin and Yanai waves. To directly test the nontrivial topology of Poincaré-gravity waves in observations, we examine ERA5 data and study cross correlations between the wind velocity and geopotential height of the midlatitude stratosphere at the 50 hPa height. We find the predicted vortex and antivortex in the relative phase of the geopotential height and velocity at the high frequencies of the waves. By contrast, lower-frequency planetary waves are found to have trivial topology also as expected from theory. These results demonstrate a new way to understand stratospheric waves and provide a new qualitative tool to investigate waves in other components of the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development of Scalable Coastal and Offshore Macroalgal Farming

The US DOE/ARPA-E MARINER program funded a four-year project to determine an optimal way to grow kelps in large, nearshore and offshore arrays for the eventual purpose of biofuel production with the goal of keeping the cost below $\$$80 USD per dry metric ton of kelp. This project specifically looked at how Saccharina latissima can be grown in the Gulf of Alaska to reach that goal. There were three major aspects of the research: 1) optimizing nursery production and seeding lines for outplanting, 2) designing an economical, modular outplanting structure and 3) developing methods to efficiently harvest the product. Farm designs were based on catenary structures and the use of spreader bars with variable spacing of grow lines and line types. The spacing of the grow lines makes a difference in the yield. Grow line spacing of ≥1.5m showed about a 50% increase in kg/m. There was no statistical difference in the growth of Saccharina latissima whether in the middle or the outside of the array, but the line type and perhaps line thickness can make a difference in yield. Sagging caused by the weight of the mature fronds resulted in lower growth at depth. Various harvesting approaches for mature kelps were tested by collaborating farmers. One promising innovation is the use of large bags for holding the freshly harvested fronds. Although the weight of the fronds on the growlines causes the lines to sink, the bags packed with the harvested fronds float, allowing for easy loading to the transfer vessel. Another advance in harvesting is a specially built harvest vessel, the Harvest Buddy, allowing a more mechanized and faster way to harvest. A significant aspect of this project was the TTO/T2M. Several different outreach activities were performed by the Alaska Fisheries Development Foundation and GreenWave. A techno-economic assessment (TEA) using our data has pointed to solutions to reach the $\$$80 goal. A second phase of this project involved the co-culture of two different species of kelps. Interest in farming kelps has grown beyond using kelp for food, feed or biofuels. There is considerable interest in generating biomass from seaweed for use in bioplastics and other products that would substitute for petroleum-derived products. For these uses to be viable, large amounts of biomass are needed. Very large kelp farms can be expensive to build and maintain, leading to the need to optimize the biomass per unit area. Although close spacing of growlines can lead to poor growth, a viable approach may be to grow two species of kelps together: one that hangs down and one that is buoyant, growing up. This system would increase the spacing in three dimensions. In Alaska, Saccharina latissima is commonly grown hanging down from longlines. One of the buoyant Alaskan kelps is Nereocystis luetkeana. Because there are commercial uses for wild-harvested Nereocystis in Alaska, we undertook a preliminary trial in Kodiak, Alaska that grew both Saccharina and Nereocystis in the same longline array. Closely spaced lines were seeded the first week of February 2023 and set at 3m below the surface. The arrays were harvested in late June 2023. Despite having 45% fewer grow-lines, the total yield of the Nereocystis on the combined arrays was statistically similar to the Nereocystis only arrays. Total yields were greatest on the combined arrays, followed by the Nereocystis only and Saccharina only arrays. These results may have significance for large scale macroalgal production.

09 BIOMASS FUELS↗
DOE OSTI2025

AI Model Benchmarking for Nonproliferation Applications: Steel Thread Benchmarking Task Force Technical Report (Rev. 2)

Steel Thread is a NA-22 venture that seeks to build trustworthy, reliable AI models that can be used in a wide variety of nonproliferation tasks. A key aspect of building these models is developing appropriate benchmarks and evaluation methods, which will enable the venture to identify and adapt models to provide the most value in the nonproliferation domain. Benchmarks must be relevant to key tasks in this domain, such as question answering, information retrieval, document summarization and classification, consensus analysis, and image and data analysis. This report 1) provides an overview of benchmark design, evaluation, and challenges; 2) reviews a variety of open benchmarks, with a focus on language models and tasks; and 3) identifies benchmarks that are most relevant to Steel Thread. This report is intended to serve as a basis for further efforts to classify and evaluate benchmarks and their correlation with success on nonproliferation-specific tasks. The Steel Thread venture has defined benchmarks to be a particular combination of a dataset (or datasets) and a metric (or metrics) conceptualized as representing one or more specific tasks or sets of abilities for a specific modality. It is adopted by a research community as a shared framework for comparing methods.1 It includes 1) Data: Labeled (a designated subset not used for training, which could be all the data), 2) Metric: A way to quantify performance, 3) Task/Ability: What the benchmark is testing, 4) Protocol: A structured and repeatable evaluation process, 5) Baseline/Reference Model: For comparison; could be statistical, rule-based, SME-derived, or another model, and 6) Maintenance Plan: to update with new information over time; important for long-term utility. For further clarity, the definition includes what a benchmark, in this context, is not. It is not a corpus of training data, specific to a model (it is intended to apply to a range of models), a universal evaluation of performance, a guarantee that the ‘top’ model on the leaderboard will be the best fit for every specific use case, an all-encompassing proof of a model’s universal quality, nor is it a one-size-fits-all measure of success. It does not cover every real-world constraint (like operational, ethical, or cost considerations), a systems integration test, or a unit test. This definition was inspired by and resulted from discussions within the Steel Thread Benchmarking Task Force. This group was formed to define what we would mean as a benchmark within Steel Thread but persisted as the need to develop a thorough understanding of the large and expanding existing benchmarking space. This technical report is a result of the group’s divide and conquer approach to exploring this space. The release of benchmarks might not be progressing as quickly as model development, but it is moving very fast, as many benchmarks quickly become saturated, when state-of-the-art models score so close to the benchmark’s ceiling that their results are virtually indistinguishable. At that point, the test no longer differentiates between new systems, so researchers usually stop reporting scores as the benchmark no longer informs about improvements from the next generation of models. In the OpenAI announcement of GPT-5, they reported results on six flagship public benchmarks (AIME 2025, SWE-bench Verified, Aider Polyglot, MMMU, HealthBench Hard, GPQA) but the full system-card covers roughly thirty-five separate evaluations, comprising hundreds of test task items in total. There have been some efforts to summarize benchmarks in specific fields, like for text-to-image generation, but these surveys have had a narrow methodology scope. Therefore, a comprehensive survey of all benchmarks or even all benchmarks that could be relevant to Steel Thread is outside of the scope of this report. We chose some specific benchmarks to investigate in detail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Nunes, Pedro (ORCID:0000000180012172)↗
DOE OSTI2025

Optimizing Hydronic Heating for Comfort and Performance in Multifamily Housing

Inefficient control settings in multifamily boilers often lead to substantial energy and cost penalties. To address this, a Fault Detection and Diagnostic (FDD) tool was developed to automate data analysis and identify operational faults such as suboptimal outdoor temperature sensor placement, misconfigured outdoor air reset (OAR) curves, excess boiler cycling, and domestic hot water (DHW) setpoint errors. By comparing pre- and post-implementation periods and applying engineering models, the tool quantifies energy savings and reduces manual analysis time by over 90%. Testing on over 100 monitored sites and a targeted subset of 12 buildings showed an average 11% energy savings from remote optimization; further validation across 19 OAR curve changes confirmed the tool’s accuracy, predicting actual savings within ±5% for most cases. Simple payback can be under three years for many multifamily buildings, though rising hardware, labor, and fuel costs create uncertainties, and decarbonization goals increasingly shift focus to electrification. The FDD tool remains invaluable for optimizing existing boilers, enhancing future electrification measures, and adapting to new technologies by refining building load estimates. In doing so, it supports both near-term efficiency and long-term transitions to low-carbon alternatives, ensuring buildings achieve substantial cost and energy benefits throughout their system lifecycles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Unsupervised Process Anomaly Detection and Identification Using the Leave-One-Variable-Out Approach

Automated anomaly detection and identification can signal equipment issues and pinpoint causes in large-scale industrial systems. For systems with limited failure history, unsupervised machine learning methods can be utilized as they do not require past failures. This study introduces the leave-one-variable-out (LOVO) model, which masks one variable at a time to predict the others, learning underlying process correlations. Detection performance was assessed with synthetic and experimental data, while identification performance used only synthetic data due to its ability to generate labeled anomaly types. For detection using synthetic data, the LOVO model generally outperformed comparative models; while using experimental data, the comparative methods outperformed the LOVO model. However, the comparative methods required selecting a latent size, and these conclusions pertain to using the optimal size. In practice, it would not be feasible to always select the optimal value, and incorrect selections impacted performance. In contrast, the LOVO model does not require a latent space. For identification using synthetic data, the LOVO model was slightly outperformed in interpretability and repeatability but still demonstrated impressive results. These outcomes suggest that the LOVO model is an effective model and may be more easily implemented without the challenging tuning process of selecting a latent size.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

First-Principles Statistical Mechanics Study of Magnetic Fluctuations and Order–Disorder in the Spinel LiNi 0.5 Mn 1.5 O 4 Cathode

While significant magnetic interactions exist in lithium transition metal oxides, commonly used as Li-ion cathodes, the interplay between magnetic couplings, disorder, and redox processes remains poorly understood. In this work, we focus on the high-voltage spinel LiNi 0.5 Mn 1.5 O 4 (LNMO) cathode as a model system on which to apply a computational framework that uses first principles-based statistical mechanics methods to predict the finite temperature magnetic properties of materials and provide insights into the complex interplay between magnetic and chemical degrees of freedom. Density functional theory calculations on multiple distinct Ni–Mn orderings within the LNMO system, including the ordered ground-state structure (space group P4332), reveal a preference for a ferrimagnetic arrangement of the Ni and Mn sublattices due to strong antiferromagnetic superexchange interactions between neighboring Mn 4+ and Ni 2+ ions and ferromagnetic Mn–Mn and Ni–Ni couplings, as revealed by magnetic cluster expansions. These results are consistent with qualitative predictions using the Goodenough-Kanamori-Anderson rules. Simulations of the finite temperature magnetic properties of LNMO are conducted using Metropolis Monte Carlo. We find that a “semiclassical” Monte Carlo sampling method based on the Heisenberg Hamiltonian accurately predicts experimental magnetic transition temperatures observed in magnetometry measurements. This study highlights the importance of a robust computational toolkit that accurately captures the complex chemomagnetic interactions and predicts finite temperature magnetic behavior to help analyze experimental magnetic and magnetic resonance spectroscopy data acquired ex situ and operando.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
DOE OSTI2024

Hot Hydrogen Testing of W-Coated UN Kernels in a Mo30W Matrix

Ceramic uranium mononitride (UN) is being considered as a reactor fuel for nuclear thermal propulsion. To avoid or reduce the dissociation of UN at the high temperatures needed, embedding it in a metallic matrix (cermet) has been proposed. To assess the viability of this concept, hot hydrogen testing of tungsten-coated UN kernels embedded in a Mo-30 wt% W (Mo30W) alloy matrix has been performed at temperatures from 1800°C to 2300°C. Both the isolated kernels and kernels consolidated by spark plasma sintering in the Mo30W matrix were tested. In addition to direct observations and mass loss measurements, the samples were analyzed by X-ray diffraction (XRD) and scanning electron microscopy (SEM)/energy dispersive X-ray spectroscopy (EDS) after each run. The decomposition of UN started at 1800°C despite the coating and matrix, and increased at 2000°C. Uranium seeped through the tungsten grain boundaries of the coating at all temperatures. The consolidated sample expanded irregularly at 2000°C through the formation of voids, and SEM/EDS analysis showed uranium-containing veins in the matrix consisting of U 2 Mo according to the XRD data. The observed pore generation at 2000°C was explained by the formation of water vapor from residual oxides and diffused hydrogen. At 2200°C and above, both the kernels and the consolidated samples melted through the formation of uranium or low–melting point uranium-molybdenum alloys.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Reconstructing the Stripping History of the Sagittarius Stream with Neural Networks

The Sagittarius (Sgr) Stream is produced by the ongoing disruption of the Sgr dwarf spheroidal (dSph) galaxy and is thought to contain multiple wraps that were stripped during different pericentric passages. In this study, we introduce a neural-network–based method trained on N-body simulations to infer the stripping time of Sgr Stream stars directly from their phase-space coordinates. We combine spectroscopic data from SEGUE, APOGEE DR17, and LAMOST DR7 low-resolution spectroscopic (LRS) survey with Gaia EDR3 astrometry and distance estimates from the latest StarHorse catalog to identify high-quality Sgr Stream members. Applying our method to these stars, we measure a clear metallicity gradient with stripping time, well described by a linear relation with slope ∼0.3 dex Gyr −1 . We further predict the stripping times of globular clusters previously suggested to originate from the Sgr dSph. M 54, Terzan 7, Terzan 8, and Arp 2 exhibit stripping times consistent with being currently bound to the Sgr remnant. Pal 12, Whiting 1, and NGC 2419 are inferred to have been stripped 0.9 ± 0.1, 1.1 ± 0.2, and 2.1 ± 0.2 Gyr ago, respectively. For NGC 4147 and NGC 5634, whose membership in the Sgr system remains uncertain, our analysis suggests stripping times of 1.1 ± 0.4 and 1.1 ± 0.1 Gyr, respectively, if they are ultimately confirmed as genuine Sgr members. These results demonstrate that data-driven models of dynamical stripping histories offer a promising approach for reconstructing the formation and chemical evolution of the Sgr Stream.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗