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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Distributed Energy Resource Integration

This webinar focuses on the subject of DER integration, outlining the current industry state of the art and status of DER adoption in the US, a holistic view and roadmap of DER integration, DER interconnection standards, interconnection screening and study processes, interconnection automation, DER hosting capacity, AMI analytics, and non-wires alternatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Toward the determination of 𝐶⁢𝑃-odd pion-nucleon couplings

The nucleon matrix elements (NMEs) associated with quark chromomagnetic dipole moments (cMDMs) play a crucial role in determining the 𝐶𝑃-odd pion-nucleon couplings induced by quark chromoelectric dipole moments. In recent years, it has been argued that the NMEs of cMDMs can be related to the third moment of the nucleon's higher-twist (specifically, twist-3) parton distribution function (PDF) 𝑒⁡(𝑥), which can, in principle, be measured through dihadron production in semi-inclusive deep inelastic scattering processes. By applying the spin-flavor expansion to the cMDM operators in the large-𝑁 𝑐 limit, where 𝑁 𝑐 is the number of quark colors, we show that the NMEs receive contributions not only from the twist-3 PDF 𝑒⁡(𝑥) but also from an additional, previously neglected nucleon form factor. Incorporating constraints from the spin-flavor expansion, recent experimental data on 𝑒⁡(𝑥), as well as model calculations of 𝑒⁡(𝑥), we estimate the NMEs of the cMDM operators. Our analysis indicates that the NMEs are dominated by the nucleon form factors, and the cMDM contributions to pion-nucleon couplings can be comparable to those from the quark sigma terms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Ultrafast Reactive Laser Sintering of Highly Conductive Garnet-Type LLZTO Solid Electrolytes

Rapid and scalable fabrication of garnet-type solid electrolytes remains a major challenge for the practical deployment of lithium metal batteries. Here, we report reactive laser sintering (RLS) as an ultrafast and potentially scalable strategy for fabricating garnet-type Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) solid electrolytes. RLS of LLZTO enables simultaneous reaction and densification, achieving ∼95% relative density while minimizing lithium loss and suppressing secondary phase formation. Compared to conventional furnace sintering, RLS promotes enhanced grain growth and improved densification, leading to improved ionic conductivity (0.36 ± 0.08 mS cm −1 ) while maintaining comparable activation energies for Li + transport. Structural characterization by X-ray diffraction (XRD), Raman spectroscopy, and solid-state 6 Li/ 7 Li NMR confirms the formation of cubic garnet LLZTO with homogeneous microscale elemental distribution. In addition, nanoindentation measurements demonstrate that RLS preserves the mechanical properties of the garnet framework despite ultrafast localized thermal processing. By integrating simultaneous reaction and densification with tunable microstructural control, reactive laser sintering provides a promising manufacturing pathway for high-performance garnet solid electrolytes toward next-generation solid-state batteries.

CO2 laser↗
DOE OSTI2024

Production of meson molecules in ultraperipheral heavy ion collisons

In this work we present a calculation of exotic charmonium production in ultraperipheral collisions, in which the exotic state is explicitly treated as a meson molecule. Our formalism is general, but we focus on the lightest possible exotic charmonium state: a 𝐷 + ⁢𝐷 − molecular bound state. It was proposed some time ago, and it has been an object of experimental searches. Here we study the production of the open charm pair in the process 𝛾⁢𝛾 → 𝐷 + ⁢𝐷 − . Then we use a prescription to project the free pair |𝐷 + ⁢𝐷 − ⟩ onto a bound state at the amplitude level and compute the cross section of the process 𝛾⁢𝛾 → 𝐵 (where 𝐵 is the bound state). Finally, we convolute this last cross section with the equivalent photon distributions coming from the projectile and target in an ultraperipheral collision and find the 𝐴⁢𝐴 → 𝐴⁢𝐴⁢𝐵 cross section, which, for 𝑃⁢𝑏 −𝑃⁢𝑏 collisions at $\sqrt{s_{NN}}$ =5.02 TeV, is of the order of 3 μ⁢b.

Sobrinho, F. C.↗
DOE OSTI2024

Neck Rupture and Scission Neutrons in Nuclear Fission

Just before a nucleus undergoes fission, a neck is formed between the emerging fission fragments. It is widely accepted that this neck undergoes a rather violent rupture, despite the absence of unambiguous experimental evidence. The main difficulty in addressing the neck rupture and saddle-to-scission stages of fission is that both are highly nonequilibrium processes. Here, we present the first fully microscopic characterization of the scission mechanism, along with the spectrum and the spatial distribution of scission neutrons (SNs), and some upper limit estimates for the emission of charged particles. The spectrum of SNs has a distinct angular distribution, with neutrons emitted in roughly equal numbers in the equatorial plane and along the fission axis. They carry an average energy around 3±0.5 MeV for the fission of 236 U, 240 Pu, and 252 Cf, and a maximum of 16–18 MeV. We estimate a conservative lower bound of 9%–14% of the total emitted neutrons are produced at scission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Cell Population–resolved Multiomics Atlas of the Developing Lung

The lung is a vital organ that undergoes extensive morphological and functional changes during postnatal development. To disambiguate how different cell populations contribute to organ development, we performed proteomic and transcriptomic analyses of four sorted cell populations from the lung of human subjects aged 0 to 8 years-old with a focus on early life. The cell populations analyzed included epithelial, endothelial, mesenchymal, and immune cells. Our results revealed distinct molecular signatures for each of the sorted cell populations that enable the description of molecular shifts occurring in these populations during post-natal development. Here, we confirmed that the proteome of the different cell populations was distinct regardless of age and identified functions specific to each population. We identified a series of cell population protein markers, including those located at the cell surface, that show differential expression and distribution on RNA in situ hybridization and immunofluorescence imaging. We validated the spatial distribution of AT1 and endothelial cell surface markers. Temporal analyses of the proteome of each of the four populations revealed processes modulated during postnatal development and disambiguating results obtained on whole tissue proteome. Finally, the proteome was compared to a transcriptomics survey performed on the same lung samples to evaluate processes under post-transcriptional control.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

From Plastic Waste to Fuel: Pyrolysis and Gasification of Polyethylene for Hydrogen Production

Thermochemical conversion processes offer promising solutions to address the plastic pollution crisis by transforming plastic waste into valuable products, notably hydrogen. In this study, thermal pyrolysis and steam gasification of polyethylene (PE), the most abundantly produced plastic waste, are investigated in a drop tube reactor system. Various process parameters, namely temperature, residence time, and feedstock composition, are evaluated to establish their correlations with reaction performance. Coal refuse, obtained from discarded thickener underflow in coal processing, is introduced as a co-feedstock for gasification to enhance PE handling and examine synergistic effects on product distribution, particularly H2 yield and syngas quality. Furthermore, the potential of low-cost, environmentally friendly catalysts (i.e., iron oxides, coal ash) for tar reforming is explored.

Natesakhawat, Sittichai↗
DOE OSTI2024

New Tank Mapping Method Improves Waste Removal Process

Savannah River Mission Completion is the Liquid Waste (LW) contractor at the Savannah River Site (SRS). The LW mission is tasked with treating and disposing of legacy nuclear waste. There are multiple facilities involved in this work, including the Concentration, Storage, and Transfer Facilities (CSTF), the Defense Waste Processing Facility (DWPF), the Salt Waste Processing Facility (SWPF), and the Saltstone Production Facility (SPF). The CSTF includes 43 underground waste tanks used to store and support processing of radioactive liquid waste. Waste removal activities, such as salt dissolution campaigns and sludge agitation, are conducted within the CSTF waste tanks to convert the waste into a form that allows for downstream processing at other LW facilities. While performing these waste removal campaigns, camera inspections are performed to assess the quantity and distribution of the remaining waste within the waste tank (i.e. saltcake or sludge). Understanding the quantity and distribution of the salt/sludge within the waste tanks allows for improved waste removal strategies (e.g. mixing pump operation) and refined safety controls. Typically, several camera inspections are performed during a waste removal transfer to verify the elevation of the visible salt/sludge mounds against the known elevation of the liquid surface. The camera inspection footage must then be interpreted by a trained engineer who will develop a 2-D map that depicts the waste distribution at various elevations within the waste tank. This tank mapping is then used in conjunction with conservative assumptions to evaluate the volume of saltcake or sludge that is present within the waste tank.

Mini, Melany↗
DOE OSTI2025

Joint Modeling of Quasar Variability and Accretion Disk Reprocessing Using Latent Stochastic Differential Equations

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series.

Fagin, Joshua [City Univ. of New York (CUNY), NY (↗
DOE OSTI2025

Fast jet tagging with MLP-Mixers on FPGAs

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗
DOE OSTI2024

Alternating conduction and convection drying of paper – an experimental analysis with a continuous data acquisition approach

In conventional multi-cylinder drying of paper and board, both conductive drying from steam-heated dryer cylinders and convective drying by flowing air over the paper surface in the pockets are used. Conductive drying from steam-heated drying cylinders is a critical component in providing the necessary thermal energy to paper and board as they dry. Steam temperature and internal and external resistances at the contacting surface are critical process parameters influencing the conductive drying process. An experimental setup was developed to study the alternating conductive and convective drying of paper and board. Paper sheet moisture, temperature, and temperature distribution within the heated platen and the instantaneous heat flux as the sheet was being dried were measured. The instantaneous heat flux, contact heat transfer coefficient, and drying rates were determined as drying proceeds. Experimental results, as well as comparisons to literature and commercial data, are presented. The conductive heat transfer coefficients determined were compared to traditional correlations normally used in the modeling of paper drying. Similarly, the convective heat and mass transfer coefficients are also determined and compared to literature data. In addition to the evaluation of alternating conductive and convective drying characteristics of paper and board, the potential inclusion of auxiliary energy components will also be included. Experimental results from the conduction and convection drying system are presented. Furthermore, this data will be useful in process development, intensification of manufacturing processes, and modeling and simulation of paper drying processes.

42 ENGINEERING↗
DOE OSTI2026

Characterization of ceramic fuel powder packing fractions to support INFLUX

Triply periodic minimal surface (TPMS)-based structures show marked potential in novel nuclear reactor fuel designs, as their high surface area-to-volume ratio increases the efficiency of heat transfer out of the fuel, enabling safer, more innovative reactor designs. This milestone report addresses the role of dUO 2 powder processing route on the fill behavior of TPMS-based cladding shells to understand and advance the feasibility of manufacturing TPMS-based nuclear fuel forms. dUO 2 powder was processed through either a dry granulation route, varying consolidation pressure, or through milling, varying milling time, milling method and milled size distribution. The lowest tapped bulk densities (TBD), but best powder flowabilities, were obtained when testing unprocessed dUO 2 powder which was prone to self-agglomeration and formed low-density spheroids. The highest TBD and lowest flowabilities were obtained when using powder produced by hammer-milling dUO 2 powder to pass through a 200-mesh sieve, which led to particles with angular morphologies. Powder produced by dry granulation exhibited TBD that varied according to the consolidation pressure used to form the initial pellets and exhibited improved flowabilities when compared to hammer-milled material. Because of the large span of granule sizes formed as well as the irregular shape associated with the granules, a packing fraction of 0.69 was achieved, exceeding the analytical solution for random close packing of mono-sized spheres. TPMS polymer shells were loaded with unprocessed, granulated, and hammer-milled dUO 2 powders, and their qualitative packing behaviors were analyzed using x-ray computed tomography (xCT). TBDs calculated after loading TPMS polymer shells were 10-20% lower when compared to tapped bulk density measurements taken in a glass graduated cylinder, indicating a non-trivial impact on the tapped bulk density of either the TPMS channel size, TPMS channel surface material, powder cohesiveness, or a combination of the two parameters. A metallic zircaloy-4 TPMS shell will be loaded with hammer-milled dUO 2 powder upon receipt of the shell from Oak Ridge National Laboratory (ORNL) and shipped to Idaho National Labs (INL) for subsequent hot isostatic pressing (HIP) densification experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Foliar element determination from field survey in association with the National Ecological Observatory Network Airborne Observation Platform survey, East River, Colorado 2018

The purpose of this dataset is to support research aimed at understanding the coupling between hydrologic and biogeochemical processes at watershed scale, particularly the relationship between aboveground vegetation characteristics and subsurface soil properties. These data are intended to inform and calibrate models of catchment-scale biogeochemical fluxes, including rock-derived nutrient cycling, and they were procured to address the following questions: (1) What is the distribution of vegetation characteristics across the study catchments? (2) Are foliar concentrations of rock-derived nutrients related to underlying lithology and soil availability, or are these signals masked by biotic nutrient cycling and retention processes?This data package contains foliar elemental data collected during the 2018 National Ecological Observatory Networks (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. Folair samples were collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds and contain a mixture of vegetation including meadow, shrub, and tree foliar samples. The samples were processed using aqua regia digestion and analyzed for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES).The data package includes: (1) raw foliar elemental data files in CSV and PDF formats, (2) quality control certificates in PDF format, and (3) an aggregated CSV file containing all elemental measurements compiled across samples. No specialized software is required to access or use these files.

2018 National Ecological Observatory Network Campa↗