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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rapid High-Resolution Analysis of Polysaccharide-Lignin Interactions in Secondary Plant Cell Walls Using Proton-Detected Solid-State NMR

The plant secondary cell wall, a complex matrix composed of cellulose, hemicellulose, and lignin, is crucial for the mechanical strength and water-proofing properties of plant tissues, and serves as a primary source of biomass for biorenewable energy and biomaterials. Structural analysis of these polymers and their interactions within the secondary cell wall has been heavily relying on 13 C-based solid-state NMR techniques. In this study, we explore the application of 1 H-detected solid-state NMR techniques for rapid, high-resolution structural characterization of polysaccharides and lignin, demonstrated on the stems of hardwood eucalyptus. We explored the use of synthesized 2D spectra to resolve central 1 H resonances and the combined application of 3D hCCH and hCHH experiments for complete resonance assignment and unambiguous identification of lignin-carbohydrate interactions. Our findings emphasize the central role of acetylated three-fold xylan conformers, rather than two-fold, in stabilizing the carbohydrate-lignin interface, with glucuronic acid sidechains in eucalyptus glucuronoxylan colocalizing with lignin, revised cellulose-lignin interactions involving uncoated microfibril surfaces, and pectin-lignin interactions indicative of early-stage lignification. These results present a novel approach for rapid structural analysis of lignocellulosic biomaterials without the need for solubilization or extraction.

09 BIOMASS FUELS↗
DOE OSTI2025

Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs

Estimating actual evapotranspiration (ET) is particularly crucial for addressing how vegetation affects the water balance of ecosystems. ET estimation can be complex with empirical models due to their many parameters and reliance on aridity. In contrast, artificial neural networks (ANNs) could potentially estimate ET with fewer and more common meteorological parameters. In this study, we trained two ANNs, one using a feed-forward approach (FFN) and the other a nonlinear auto-regressive network (NARX), to predict ET and compared them to the commonly used empirical model Granger and Gray (GG). We trained our models on a nine-year eddy covariance (EC) dataset for Miscanthu s × giganteus ( M . × giganteus ) from Illinois (UIEF), then tested them using out-of-sample data from both UIEF and a different location in Iowa (SABR) to compare the accuracy of FFN, NARX, and GG models in estimating daily ET. A combination of air temperature (T a ) and solar radiation (R s ) was chosen as inputs due to the highest R 2 for FFN (R 2 = 0.79, 0.81, and 0.79 for training, testing, and validation, respectively) and only T a for NARX (R 2 = 0.70 for out-of-sample validation). The predictive power of the FFN model was superior to the NARX and GG models at the UIEF site (R 2 = 0.84, 0.70, and 0.83 for out-of-sample validation, respectively). Our analysis showed that ANN approaches are as accurate as empirical approaches for estimating ET but use fewer inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98% (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63%—the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.

Marfo, William↗
DOE OSTI2020

Quantification of High-Temperature Transition Al2O3 and Their Phase Transformations

High temperature exposure of ?-Al2O3 can lead to a series of polymorphic transformations, including the formation of ?-Al2O3 and ?-Al2O3. Quantification of the microstructure in the ?/?-Al2O3 formation range represents a formidable challenge as both phases accommodate a high degree of structural disorder. In this work, we explore the use of XRD recursive stacking formalism for quantification of high temperature transition aluminas. We formulate the recursive stacking methodology for modelling of disorder in ?-Al2O3 and twinning in ?-Al2O3 and show that explicitly accounting for the disorder is necessary to reliably model the XRD patterns of high temperature transition alumina. In the second part, we use the recursive stacking approach to study phase transformation during high temperature (1050 ºC) treatment. We show that the two different intergrowth modes of ?-Al2O3 have different transformation characteristics, and that a significant portion of ?-Al2O3 is stabilized with ?-Al2O3 even after prolonged high-temperature exposures. In discussions, we outline the limitation of the current XRD approach and discuss a possible multimodal XRD and NMR approach which can improve analysis of complex transition aluminas. This work was performed in the Wiley Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by DOEs Office of Biological and Environmental Research and located at PNNL. The work was supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences.

Kovarik, Libor↗
DOE OSTI2024

A numerical study of ignition and flame development characteristics in GCI combustion using large eddy simulations and chemical explosive mode analysis

Here, this work investigates the ignition and flame development processes of low reactivity fuel combustion under compression ignition conditions based on the large eddy simulation approach. The chemical explosive mode analysis (CEMA) is employed to characterize the local combustion features, including gas-liquid fuel zone, auto-ignition, diffusion-assisted, extinction, cool flame and post-ignition zone, among which auto-ignition and post-ignition are found to play a key role in the overall heat release process. The local flame propagation modes in gasoline compression ignition (GCI) are determined by quantifying the relative magnitude of diffusion/chemistry at a representative progress variable in the pre-ignition zone. The results show that autoignition fronts and deflagration waves exist simultaneously in the ignition and intense high temperature heat release (HTHR) stages, but autoignition fronts dominate. In addition, the chemical kinetic processes of four heat release periods are analyzed. The heat release during the ignition period is found to be dominated by the reactions CH 3 + H (+M) <=> CH 4 (+M) and CH 3 CHO + H <=> CH 2 CHO + H 2 . The reaction CH 2 OH + OH <=> CH 2 O + H 2 O always plays an important role in the heat releases during the other three combustion stages including intense HTHR, moderate HTHR and post-combustion.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Scenario Exploration and Timeline Analysis for Advanced Reactors [Slides]

Slides created to discuss the report "Approach and Model Used to Represent a Timeline Analysis for Security Design Enhancements" (August 2022 INL/ RPT-22-68664) for an upcoming DOE security workshop. Advanced reactors will be able to use risk insights for many design aspects. We need realistic scenarios for input into the licensing basis safety-case. These scenarios must include timing and physics. We need to automate the safety-case creation as much as possible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation

Here we present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, in this study, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Improving Traditional Registrational Trial End Points: Development and Application of a Desirability of Outcome Ranking End Point for Complicated Urinary Tract Infection Clinical Trials

Abstract Background Traditional end points used in registrational randomized, controlled trials (RCTs) often do not allow for complete interpretation of the full range of potential clinical outcomes. Desirability of outcome ranking (DOOR) is an approach to the design and analysis of clinical trials that incorporates benefits and risks of novel treatment strategies and provides a global assessment of patient experience. Methods Through a multidisciplinary committee of experts in infectious diseases, clinical trial design, drug regulation, and patient experience, we developed a DOOR end point for infectious disease syndromes and demonstrated how this could be applied to 3 registrational drug trials (ZEUS, APEKS-cUTI, and DORI-05) for complicated urinary tract infections (cUTIs). ZEUS compared fosfomycin to piperacillin/tazobactam, APEKS-cUTI compared cefiderocol to imipenem, and DORI-05 compared doripenem to levofloxacin. Using DOOR, we estimated the probability of a more desirable outcome with each investigational antibacterial drug. Results In each RCT, the DOOR distribution was similar and the probability that a patient in the investigational arm would have a more desirable outcome than a patient in the control arm had a 95% confidence interval containing 50%, indicating no significant difference between treatment arms. DOOR facilitated improved understanding of potential trade-offs between clinical efficacy and safety. Partial credit and subgroup analyses also highlight unique attributes of DOOR. Conclusions DOOR can effectively be used in registrational cUTI trials. The DOOR end point presented here can be adapted for other infectious disease syndromes and prospectively incorporated into future clinical trials.

Immunology↗
DOE OSTI2023

Enabling quantitative analysis of complex polymer blends by infrared nanospectroscopy and isotopic deuteration

Atomic-force microscopy coupled with infrared spectroscopy (AFM-IR) deciphers surface morphology of thin-film polymer blends and composites by simultaneously mapping physical topography and chemical composition. However, acquiring quantitative phase and composition information from multi-component blends can be challenging using AFM-IR due to the possible overlapping infrared absorption bands between different species. Isotope labeling one of the blend components introduces a new type of bond (carbon-deuterium vibration) that can be targeted using AFM-IR and responds at wavelengths sufficiently shifted toward unoccupied regions (around 2200 cm –1 ). In this project, AFM-IR was used to probe the surface morphology and chemical composition of three polymer blends containing deuterated polystyrene; each blend is expected to exhibit various degrees of miscibility. AFM-IR results successfully demonstrated that deuterium labeling prevents infrared spectral overlap and enables the visualization of blend phases that could not normally be distinguished by other scanning probe techniques. The nanoscale domain composition was resolved by fast infrared spectrum analysis. Overall, we presented isotope labeling as a robust approach for circumventing obstacles preventing the quantitative analysis of multiphase systems by AFM-IR.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Nonintrusive Optical Approach to Characterize Heliostats in Utility-Scale Power Tower Plants: Camera Position Sensitivity Analysis

Optics plays a major role in the effectiveness of concentrating solar power (CSP) technologies. The nonintrusive optical (NIO) approach developed by the National Renewable Energy Laboratory uses uncrewed aircraft system (UAS)-based imaging to survey heliostats in a commercial-scale power tower CSP plant and characterize their optical errors. The image processing algorithm uses photogrammetry to calculate the camera position for each image frame, and the accuracy of the estimated optical errors is highly sensitive to the calculated camera position accuracy. In this study, we simulate a series of case studies in python to examine the impact of different parameters of the sensitivity of the camera calculation, including the number of facet corners used as control points for the photogrammetric calculation, precision error in the detected pixel locations of the facet corners in the image, and precision error of the canting and mounting positions of the facets of the heliostat. The case studies consider heliostat geometry based on three commercial designs to serve as representative examples of different possible sizes of heliostats that the NIO method could be applied to. The results show that increasing the number of control points can improve accuracy for heliostats with many facets, pixel precision has a significantly larger impact on camera calculation accuracy than facet canting and mounting errors, and the camera distance and focal length must be chosen to ensure adequate pixel accuracy on the heliostat surface depending on the size of heliostat. In conclusion, based on the results, recommendations for the allowable values of each parameter are provided to achieve the required NIO optical error estimation accuracy depending on the size of heliostat.

14 SOLAR ENERGY↗
DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Improved Statistical Analysis for the Neutrinoless Double-Beta Decay Matrix Element of 136Xe

Neutrinoless double beta decay nuclear matrix element (M0ν) for 136Xe was recently analyzed using a statistical approach (Phys. Rev. C 107, 045501 (2023)). In the analysis, three initial shell model effective Hamiltonians were randomly altered, and their results for 23 measured observables were used to infer credibility for the M0ν nuclear matrix element (NME) based on a Bayesian Model Averaging approach. In that analysis, a reasonable Gamow-Teller quenching factor of 0.7 was assumed for each starting effective Hamiltonian. Given that the result of the statistical analysis was sensible to this choice, we are here improving that analysis by assuming that the Gamow-Teller quenching factor is also randomly chosen within reasonabe limits for all three starting Hamiltonians. The outcomes are slightly higher expectation values and uncertainties for the M0ν NME.

Astronomy & Astrophysics↗
DOE OSTI2026

Spatially resolved phase transformation mapping in 410 stainless steel during additive manufacturing visualized via real-time infrared data

Here, in this study, we demonstrate that spatially resolved cooling curves derived from real-time infrared (IR) thermography during additive manufacturing (AM) can capture spatial variations in phase transformation temperatures through cooling curve analysis (CCA). Using this approach, we show that during laser hot-wire deposition of 410 stainless steel (410SS), the martensite start temperature (M s ) evolves dynamically throughout the build. The M s temperature is spatially nonuniform, ranging from 185 °C to 348 °C, with the lowest values toward the build center and higher values toward the upper region of the deposit. In the lower portion of the build, no M s inflection is detected via CCA, consistent with transformation occurring earlier during thermal cycling followed by tempering during subsequent thermal cycles. These trends in M s are corroborated by characterizing the microstructure by electron backscatter diffraction (EBSD). Traditionally, M s is assumed to be constant, and a single interpass temperature is applied during both deposition and residual stress modeling. Our results demonstrate that IR-derived cooling curves provide a route to spatially and temporally resolved transformation temperature tracking for dynamic interpass control and improved residual-stress modeling.

Additive manufacturing↗
DOE OSTI2021

A New Approach to Predict Hydrogeological Parameters Using Shear Waves from the Multichannel Analysis of Surface Waves Method

For near-surface contaminant characterization, the accurate prediction of hydrogeological parameters in anisotropic and heterogeneous environments has been a challenge since the last decades. However, recent advances in near-surface geophysics have facilitated the use of geophysical data for hydrogeological characterization in the last few years. A pseudo 3-D high resolution P-wave shallow seismic reflection survey was performed at the P Reactor Area, Savannah River Site, South Carolina in order to delineate and predict migration pathways of a large contaminant plume including trichloroethylene. This contaminant plume originates from the northwest section of the reactor facility that is located within the Upper Atlantic Coastal Plain. The data were collected with 40 Hz geophones, an accelerated weight-drop as seismic source and 1 m receiver spacing with near- and far-offsets of 0.5 and 119.5 m, respectively. In such areas with near-surface contaminants, a detailed subsurface characterization of the vadose zone hydraulic parameters is very important. Indeed, an inexpensive method of deriving such parameters by the use of seismic reflection surveys is beneficial, and our approach uses the relationship between seismic velocity and hydrogeological parameters together with empirical observations relating porosity to permeability and hydraulic conductivity. Shear wave velocity ( V s ) profiles were estimated from surface wave dispersion analysis of the seismic reflection data and were subsequently used to derive hydraulic parameters such as porosity, permeability, and hydraulic conductivity. Additional geophysical data including core samples, vertical seismic profiling, surface electrical resistivity tomography, natural gamma and electrical resistivity logs allowed for a robust assessment of the validity and geological significance of the estimated V s and hydrogeological models. The results demonstrate the usefulness of this approach for the upper 15 m of shallow unconsolidated sediments even though the survey design parameters were not optimal for surface wave analysis due to the higher than desired frequency geophones.

Engineering↗
DOE OSTI2021

Quantitative Risk Analysis of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants using IRADIC Technology

This report documents the activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2021 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) Risk Assessment project. In FY-2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems (IRADIC technology) was proposed for this strategy, which aims to (1) provide a best-estimate risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the High Safety Significant Safety-related (HSSSR) DI&C systems, (2) develop an advanced risk assessment technology to support transition from analog to DI&C technologies for nuclear industry, (3) assure the long-term safety and reliability of vital HSSSR DI&C systems, (4) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the IRADIC technology is instructive for nuclear vendors and utilities to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify responding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the individual level, system level, and plant level, (4) providing insights and suggestions on designs to manage the risks; thus, to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). In this report, an approach for performing software CCF analysis, given limited data, is developed and demonstrated using a case study of a highly redundant digital reactor trip system. Consequence analysis is also performed based on different accident scenarios. Results indicate that plant modernization including the improvement of HSSSR DI&C systems will make great benefits to plant safety by providing more safety margins to accident management. In addition, a novel approach is proposed in this report for the quantification of software hazards when sufficient operational and testing data available. The method incorporates software development quality as well as strong analysis techniques to identify and link software defects to potential failure modes. The approach includes both semantic and test-based analysis to detect failures that can exist in different stages of the software development life cycle. This method is applied to an advanced human system interface relevant to reactor trip safety developed from the APR 1400 design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗