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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Predictive outlook for experiments resolving prompt vs local redeposition of high- Z materials in tokamaks

High-Z plasma facing components redeposit within the sheath through a combination of two distinct mechanisms: prompt (or geometric-driven) and local (or sheath-driven) redeposition. Experimental efforts are needed to determine the leading-order parameters influencing prompt-vs-local trade-off, which sets the fraction of material entering the scrape-off layer. In preparation for such experiments, leading-order parameters are isolated within the PYEAD-RustBCA-GITR coupled net erosion code using Sobol’ sensitivity analysis. Then, experiments resolving prompt-vs-local trade-off under variation of these leading-order parameters are proposed using an isotopic coupon design with multifaceted diagnostic coverage. The measurability of these experiments is evaluated using synthetic diagnostics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

The use of D–D reactions to diagnose the lifetime of spin polarized fuel

If the nuclei remain polarized, spin polarized fuel can increase the fusion power produced in a reactor while using less fuel. This study assesses whether reactions between an unpolarized deuterium (D) beam and polarized deuterium nuclei can in principle experimentally ascertain the persistence of nuclear polarization in a magnetic fusion experiment. The differential cross section for D–D reactions between unpolarized and polarized nuclei depends upon polarization, so the polarization state can be inferred from measurements of the D–D fusion products. An evaluation is performed for 3 MeV proton detection in the DIII-D tokamak using 81 keV neutral beams and polarized target fuel injected as a pellet. Measurement of the escaping proton pitch v 3 ∥ v 3 is insensitive to the degree of polarization but energy-resolved measurements are sensitive. The sensitivity is comparable for different angles of beam injection. Because the D–D reaction rate is large for this scenario, uncertainties associated with counting statistics are small, making inference of the polarization feasible with current technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

UDP-glucuronic acid decarboxylase in alfalfa: a target to improve ruminal digestibility of stems

Alfalfa (Medicago sativa) has a high nutritional value, but poor digestibility of the stems limits its value as an energy source in ruminant diets. Xylan and lignin negatively affect cell wall digestibility, whereas pectins have high digestibility in the rumen. In plants, UDP-xylose synthase (UXS) catalyses the decarboxylation of UDP-glucuronic acid to form UDP-xylose in an irreversible step that is key for xylan synthesis. Here, we functionally characterized two UXS genes in alfalfa, namely MsaUXS2 and MsaUXS4, and investigated their impact on ruminal digestibility. Both genes are more highly expressed in stems than leaves, and the enzymes have UDP-glucuronic acid decarboxylase activity in vitro. Silencing of MsaUXS2 and MsaUXS4 via RNAi altered plant growth and resulted in a 40% decrease in xylose, a 115% increase in arabinose, and a 60% increase in galacturonic acid in the polysaccharide matrix as well as a 20% decrease in lignin in the cell wall. Together, our results show a major role for UXS2 and UXS4 in xylan synthesis and secondary cell wall deposition in alfalfa. Additionally, in vitro rumen digestibility assays for the silenced lines had on average 30% increased gas production at 24 h, demonstrating the potential of targeting UXS genes to increase stem digestibility.

UDP-xylose synthase↗
DOE OSTI2026

First observation of currents induced by Alfven eigenmodes in a magnetic confinement device

Instabilities driven by energetic particles normally reduce fusion power but recent studies find Alfven eigenmode instabilities can drive ``zonal" flows and currents that reduce thermal transport, improving overall performance. Motional Stark effect (MSE) data from the DIII-D tokamak reveal changes in magnetic pitch angle during the phase when Alfven-eigenmode nonlinearity drives zonal flows, suppresses microturbulence, and increases the electron and ion temperatures. Finally, measured changes in safety factor of ∆q/q ≃ 5% occur in roughly 20 ms, consistent with a rough estimate based on the measured zonal flow.

Heidbrink, W. W. [Univ. of California, Irvine, CA ↗
DOE OSTI2025

Microturbulence Suppression by Alfvén Eigenmodes in the DIII-D Tokamak

Mitigation and suppression of low-𝑘 turbulence are observed during the nonlinear evolution of toroidicity-induced Alfvén eigenmodes (TAEs) in DIII-D experiments. Turbulence mitigation begins when the dominant TAE starts to depart from the typical shear Alfvén wave polarization. TAE activity then evolves into a new state, characterized by more discrete coherent modes, increased total amplitude, and more localized radial structures. During this transition, a narrow shear flow layer forms, driven by an enhanced Reynolds stress force, with a shearing rate that exceeds the local turbulence decorrelation rate, leading to a full turbulence suppression. Furthermore, these observations indicate the imbalance between Reynolds and Maxwell stress forces during the nonlinear evolution of the TAE and their important roles in shear flow generation and turbulence reduction.

Alfvén waves↗
DOE OSTI2026

Evidence of nonlinear coupling in the edge harmonic oscillation sustaining quiescent high confinement in a tokamak plasma

In a tokamak plasma, we measure nonlinear coupling between harmonics comprising a saturated edge oscillation, as predicted by nonlinear magnetohydrodynamic simulations. The coherent structure formed by this coupling suppresses bursty edge-localized modes that cause energy and particle loss. We also measure nonlinear coupling of the edge harmonic oscillation to core-resonant magnetic tearing modes. When tearing modes are present, the edge oscillation harmonics decouple, and edge-localized modes return. In this article, we conclude that nonlinear interaction with tearing modes interrupts the formation of the edge harmonic oscillation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Novel anaerobic selenium oxyanion reducers native to FGD wastewater for enhanced selenium removal

Biological treatment is a recognized approach for removing selenate and selenite oxyanions present in flue gas desulfurization (FGD) wastewater. However, the knowledge of the specific microbial species or communities responsible for reducing water-soluble selenium oxyanions to insoluble elemental selenium remains limited. In addition, the selenium oxyanion reduction genes and pathways have yet to be understood in these wastewaters. This study characterizes selenium oxyanion-reducing bacteria (SeRB) native to FGD wastewater, and the resulting elemental selenium particles formed. By selecting native SeRB microbes in a defined media, a novel resolution of these organisms has been achieved. This research identifies previously unrecognized selenium oxyanion-reducing capabilities in Anaerosolibacter, alongside predominant SeRB from Mesobacillus and Tepidibacillus genera. This work encompasses both 16S and metagenomic techniques to recover novel metagenome-assembled genomes, distinct to this environment. The biogenic selenium produced by these organisms was predominantly of elemental selenium, either amorphous or with a hexagonal structure. This study identifies the SeRB present in FGD wastewater and characterizes their selenium products, offering crucial insights to enhance the efficiency of biological treatment strategies and the potential of selenium recovery from this industrial waste.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗
DOE OSTI2021

DOE/SNL/GCBS: Biorisk Management Curriculum for Vietnam One Health University Network

In line with the Global Health Security Agenda for Vietnam SNL has successfully engaged in country by ensuring long term sustainability of its programs and building BRM capacity by engaging with Government Institutions and strengthening the national biosafety and biosecurity and also by engaging with academic institutions through Vietnam One Health University Network to educate the One Health Workforce and promote a shared culture of responsibility, reduce dual use risks, mitigate biological proliferation and deliberate use threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Pore-scale visualization of natural hydrate-bearing sediments

Accurate modeling of gas hydrate reservoir productivity and geomechanical risks associated with subsurface dissociation of natural gas hydrates (NGH) requires the determination of model parameters through physical testing on natural hydrate-bearing sediments (HBS). This involves investigating the hydro-mechanical behavior of undisturbed hydrate samples from nature under in situ conditions using pressure core characterization and analysis, which provides a unique opportunity for research. By employing state-of-the-art micro computed tomography imagery on cryogenically preserved, hydrate-bearing sediment samples, we can determine hydrate saturation as well as permeability with and without the presence of hydrates in the sediment. Furthermore, utilizing a machine learning based image segmentation technique, it is possible to extract pore space and grain information. Subsections of the entire image volume were used to determine anisotropic permeabilities using a finite-difference method Stokes solver (FDMSS). Additionally, permeability measurements on whole pressure and temperature preserved hydrate-bearing core were analyzed by utilizing the National Energy Technology Laboratory’s (NETL) Pressure Core Characterization and X-ray CT Visualization Tool (PCXT) to manipulate, cut, and analyze pressure preserved sediment. Permeabilities were measured under a broad range of vertical stress states to simulate expected pressure changes during production scenarios, and the results show that permeabilities derived from images are in agreement with those from traditional core derived experiments. The collected stress-dependent permeability, permeability anisotropy, and corresponding gas hydrate saturations provide valuable input into numerical simulations of reservoir productivity. These properties have been proven to be key parameters determining a long-term reservoir response under depressurization.

Liu, Mengwei [Oak Ridge Institute for Science and ↗
DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗
DOE OSTI2025

The Effect of Flow on CO2 Corrosion of Self-Healing Metallic Coatings

Internal corrosion is an issue that affects natural gas pipelines, a significant part of the United States’ energy infrastructure. Over time, this corrosion has worn away longstanding pipelines due to the original construction materials used for the lines and impurities in the gas and liquid streams flowing through them. The main impurities in natural gas are H2O, CO2, H2S, and O2. One of the leading causes of corrosion is the CO2 dissolved in water, giving rise to carbonic acid formation, which can further dissolve the steel pipe. The National Energy Technology Laboratory (NETL) has been studying different solutions to this problem. One potential answer is a self-healing sacrificial metallic coating applied by a novel cold spray technique. This study explored the impact of flow on corrosion with this coating and carbon steel when exposed to a saturated CO2 environment by simulating the pipeline flow profile in a small-scale lab setting. The samples were evaluated using multiple electrochemical techniques that found corrosion rates and were backed up with surface analysis to conclude the behavior of the corrosion mechanisms. The cold spray coating exhibited steel corrosion protection under flow conditions. This study has significantly advanced our understanding of CO2 corrosion, particularly in the context of natural gas pipelines and coating design.

carbon steel↗
DOE OSTI2025

Theoretical study of CO2 capture mechanisms of SrO and Sr(OH)2.nH2O (n=0,1,8)

<span style="font-family: Roboto; font-size: 11pt;">In this presentation, the adsorption reactions of CO</span><sub style="font-family: Roboto; font-size: 11pt;">2</sub><span style="font-family: Roboto; font-size: 11pt;"> on SrO surfaces and its hydrated counterparts are examined. The predicted energies of CO</span><sub style="font-family: Roboto; font-size: 11pt;">2</sub><span style="font-family: Roboto; font-size: 11pt;"> adsorption on follows the order of increasing H</span><sub style="font-family: Roboto; font-size: 11pt;">2</sub><span style="font-family: Roboto; font-size: 11pt;">O content. The carbonation on the monohydrate surface is barrierless, whereas the CO</span><sub style="font-family: Roboto; font-size: 11pt;">2</sub><span style="font-family: Roboto; font-size: 11pt;"> reaction on the Sr(OH)</span><sub style="font-family: Roboto; font-size: 11pt;">2</sub><span style="font-family: Roboto; font-size: 11pt;"> and the octahydrate surfaces follows pathways with finite activation barriers. The thermodynamics of bulk reaction is also addressed.</span>

computational science and engineering↗