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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 OSTI2021

Feasibility and Prospects of the High Resolution Rapid Refresh Model For Dynamic Line Rating

The ampacity of transmission lines is defined as the maximum amount of current the conductor can safely carry. It is necessary for transmission line operators to apply ampacity limits due to the thermal properties of the conductor. Dynamic Line Rating (DLR) is a technology and technique that uses the environmental conditions or a set of the conditions to calculate the ampacity of the conductor. The way the DLR is calculated has depended upon some amount of physical technology to implement the solution. The High Resolution Rapid Refresh (HRRR) Model is an atmospheric model that may provide enough resolution to perform dynamic line rating without any hardware at all. The objective is to use this model to perform Dynamic Line Rating. This will solve two of the most pervasive problems in Dynamic Line Rating, how to instrument a transmission line hundreds of miles long and then how to predict near future ampacity sufficient for transmission operators to effectively use, This technique will use the Idaho National Laboratory site as the test location for analysis. The High Resolution Rapid Refresh model is a reliable, freely available, and well-maintained weather forecasting model for the entire United States of America. The model provides enough special resolution that additional local weather monitoring devices at location are not necessary to conduct dynamic line rating. The HRRR ratings are lower on average than the locally measured ratings which allows for an additional margin of safety. The adoption of using purely digital methods for dynamic line rating could allow for wide scale adoption of the technology with very little overhead expenditure of implementation.

17 WIND ENERGY↗
DOE OSTI2022

Sensitivity Analysis of Wind and Turbulence Predictions With Mesoscale‐Coupled Large Eddy Simulations Using Ensemble Machine Learning

Abstract Coupling between mesoscale models and large‐eddy simulation (LES) models is increasingly used to more realistically represent the wide range of scales of atmospheric motions affecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale‐to‐microscale coupled modeling frameworks are potentially affected by a large number of uncertain closure parameters. Here, we investigate the sensitivity associated with six closure parameters related to a 1.5‐order subgrid‐scale turbulence closure for an ensemble of mesoscale‐coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project. Machine learning algorithms are used to explore the sensitivity of LES predictions, considering the effects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coefficient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for several of the simulation response variables we examine.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimates of Southern Hemispheric Gravity Wave Momentum Fluxes across Observations, Reanalyses, and Kilometer-Scale Numerical Weather Prediction Model

Abstract Gravity waves (GWs) are among the key drivers of the meridional overturning circulation in the mesosphere and upper stratosphere. Their representation in climate models suffers from insufficient resolution and limited observational constraints on their parameterizations. This obscures assessments of middle atmospheric circulation changes in a changing climate. This study presents a comprehensive analysis of stratospheric GW activity above and downstream of the Andes from 1 to 15 August 2019, with special focus on GW representation ranging from an unprecedented kilometer-scale global forecast model (1.4 km ECMWF IFS), ground-based Rayleigh lidar (CORAL) observations, modern reanalysis (ERA5), to a coarse-resolution climate model (EMAC). Resolved vertical flux of zonal GW momentum (GWMF) is found to be stronger by a factor of at least 2–2.5 in IFS compared to ERA5. Compared to resolved GWMF in IFS, parameterizations in ERA5 and EMAC continue to inaccurately generate excessive GWMF poleward of 60°S, yielding prominent differences between resolved and parameterized GWMFs. A like-to-like validation of GW profiles in IFS and ERA5 reveals similar wave structures. Still, even at ∼1 km resolution, the resolved waves in IFS are weaker than those observed by lidar. Further, GWMF estimates across datasets reveal that temperature-based proxies, based on midfrequency approximations for linear GWs, overestimate GWMF due to simplifications and uncertainties in GW wavelength estimation from data. Overall, the analysis provides GWMF benchmarks for parameterization validation and calls for three-dimensional GW parameterizations, better upper-boundary treatment, and vertical resolution increases commensurate with increases in horizontal resolution in models, for a more realistic GW analysis. Significance Statement Gravity wave–induced momentum forcing forms a key component of the middle atmospheric circulation. However, complete knowledge of gravity waves, their atmospheric effects, and their long-term trends are obscured due to limited global observations, and the inability of current climate models to fully resolve them. This study combines a kilometer-scale forecast model, modern reanalysis, and a coarse-resolution climate model to first compare the resolved and parameterized momentum fluxes by gravity waves generated over the Andes, and then evaluate the fluxes using a state-of-the-art ground-based Rayleigh lidar. Our analysis reveals shortcomings in current model parameterizations of gravity waves in the middle atmosphere and highlights the sensitivity of the estimated flux to the formulation used.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Utilizing high-resolution genetic markers to track population-level exposure of migratory birds to renewable energy development

With new motivation to increase the proportion of energy demands met by zero-carbon sources, there is a greater focus on efforts to assess and mitigate the impacts of renewable energy development on sensitive ecosystems and wildlife, of which birds are of particular interest. One challenge for researchers, due in part to a lack of appropriate tools, has been estimating the effects from such development on individual breeding populations of migratory birds. To help address this, we utilize a newly developed, high-resolution genetic tagging method to rapidly identify the breeding population of origin of carcasses recovered from renewable energy facilities and combine them with maps of genetic variation across geographic space (called ‘genoscapes’) for five species of migratory birds known to be exposed to energy development, to assess the extent of population-level effects on migratory birds. We demonstrate that most avian remains collected were from the largest populations of a given species. In contrast, those remains from smaller, declining populations made up a smaller percentage of the total number of birds assayed. Results suggest that application of this genetic tagging method can successfully define population-level exposure to renewable energy development and may be a powerful tool to inform future siting and mitigation activities associated with renewable energy programs.

14 SOLAR ENERGY↗
DOE OSTI2025

Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations

Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.

BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗
DOE OSTI2022

Measurements of the groomed and ungroomed jet angularities in pp collisions at $ \sqrt{s}$ = 5.02 TeV

The jet angularities are a class of jet substructure observables which characterize the angular and momentum distribution of particles within jets. These observables are sensitive to momentum scales ranging from perturbative hard scatterings to nonperturbative fragmentation into final-state hadrons. We report measurements of several groomed and ungroomed jet angularities in pp collisions at $\sqrt{s}$ = 5.02 TeV with the ALICE detector. Jets are reconstructed using charged particle tracks at midrapidity (|η| < 0.9). The anti-$k_T$ algorithm is used with jet resolution parameters R = 0.2 and R = 0.4 for several transverse momentum $p^{ch jet}_{T}$ intervals in the 20–100 GeV/c range. Using the jet grooming algorithm Soft Drop, the sensitivity to softer, wide-angle processes, as well as the underlying event, can be reduced in a way which is well-controlled in theoretical calculations. We report the ungroomed jet angularities, λ α , and groomed jet angularities, λ α,g , to investigate the interplay between perturbative and nonperturbative effects at low jet momenta. Various angular exponent parameters α = 1, 1.5, 2, and 3 are used to systematically vary the sensitivity of the observable to collinear and soft radiation. Results are compared to analytical predictions at next-to-leading-logarithmic accuracy, which provide a generally good description of the data in the perturbative regime but exhibit discrepancies in the nonperturbative regime. Moreover, these measurements serve as a baseline for future ones in heavy-ion collisions by providing new insight into the interplay between perturbative and nonperturbative effects in the angular and momentum substructure of jets. They supply crucial guidance on the selection of jet resolution parameter, jet transverse momentum, and angular scaling variable for jet quenching studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

GQuEST Control System Resolution

GQuEST (Gravity from Quantum Entanglement of Space-Time), is a collaboration verifying a quantum gravity model by measuring holographic effects on a microscopic scale. Its interferometer apparatus filters excessive optical noise and requires a control system that can adjust the filtration to accommodate for frequency changes in the output light. We stress-test this control system by simulating filter behaviors with parameters in their outermost regimes, and we have determined a threshold that maintains precision while minimizing resource usage. These results are important for the successful operation of GQuEST's interferometer, enabling more accurate tests of quantum gravity models and thereby advancing our understanding of gravity at the quantum mechanical level.

Nguyen, David↗
DOE OSTI2021

The Implementation of Framework for Improvement by Vertical Enhancement Into Energy Exascale Earth System Model

Abstract The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to ameliorate the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the large eddy simulation‐like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Using FIVE to improve the representation of low‐level clouds does not come with any negative side effects associated with the simulation of mid‐ and high‐level cloud and precipitation, that can occur when running the full model at higher vertical resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Molecular homing and retention of muscle membrane stabilizing copolymers by non-invasive optical imaging in vivo

First-in-class membrane stabilizer Poloxamer 188 (P188) has been shown to confer membrane protection in an extensive range of clinical conditions; however, elements of the systemic distribution and localization of P188 at the organ, tissue, and muscle fiber levels in vivo have not yet been elucidated. Here we used non-invasive fluorescence imaging to directly visualize and track the distribution and localization of P188 in vivo. The results demonstrated that the Alx647 probe did not alter the fundamental properties of P188 to protect biological membranes. Distribution kinetics in mdx mice demonstrated that Alx647 did not interface with muscle membranes and had fast clearance kinetics. In contrast, the distribution kinetics for P188-Alx647 was significantly slower, indicating a dramatic depot and retention effect of P188. Results further demonstrated the significant retention of P188-Alx647 in the skeletal muscle of mdx mice, showing a significant genotype effect with a higher fluorescence signal in the mdx muscles over BL10 mice. High-resolution optical imaging provided direct evidence of P188 surrounding the sarcolemma of skeletal and cardiac muscle cells. Taken together, these findings provide direct evidence of muscle-disease-dependent molecular homing and retention of synthetic copolymers in striated muscles thereby facilitating advanced studies of copolymer-membrane association in health and disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Moisture-mineral interactions drive bacterial and organic matter turnover in glacier-sourced riparian sediments undergoing pedogenesis

Glacial recession is occurring at unprecedented rates resulting in increased sediment accumula-tions in some riverine ecosystems. Increased sediment deposition has implications for ecosystem stability (e.g., floods and river paths) and environmental services (e.g., carbon sequestration). Soils and sediments have an enormous potential to retain carbon (C), predominantly due to sorp-tion to mineral surfaces. However, C persistence may be sensitive to climate-change induced temperature and moisture variations. We coupled ultrahigh resolution organic matter composition classification with bacterial characterization and respiration measurements to test the combined effects of temperature (4 vs 20°C) and moisture (50 vs 100% water-filled pore space) on C turn-over in sediments maintained under different mineralogical conditions (illite-amended vs non-amended). Here we show that the inhibition of CO 2 emissions from the combined effect of in-creased moisture content and illite was reflected in the turnover of key molecular signatures, such as the nominal oxidation state of C, often irrespective of temperature. However, shifts in bacteri-al communities from a coupled moisture-mineral interaction, were temperature-dependent. Our results highlight the importance of moisture in driving mineral-organic interactions and suggest that C in clay-rich, water-saturated sediments is both thermodynamically unfavorable and miner-al-protected from microbial consumption.

58 GEOSCIENCES↗
DOE OSTI2025

Analysis of biofilm assembly by large area automated AFM

Biofilms are complex microbial communities critical in medical, industrial, and environmental contexts. Understanding their assembly, structure, genetic regulation, interspecies interactions, and environmental responses is key to developing effective control and mitigation strategies. While atomic force microscopy (AFM) offers critically important high-resolution insights on structural and functional properties at the cellular and even sub-cellular level, its limited scan range and labor-intensive nature restricts the ability to link these smaller scale features to the functional macroscale organization of the films. We begin to address this limitation by introducing an automated large area AFM approach capable of capturing high-resolution images over millimeter-scale areas, aided by machine learning for seamless image stitching, cell detection, and classification. Large area AFM is shown to provide a very detailed view of spatial heterogeneity and cellular morphology during the early stages of biofilm formation which were previously obscured. Using this approach, we examined the organization of Pantoea sp. YR343 on PFOTS-treated glass surfaces. Our findings reveal a preferred cellular orientation among surface-attached cells, forming a distinctive honeycomb pattern. Detailed mapping of flagella interactions suggests that flagellar coordination plays a role in biofilm assembly beyond initial attachment. Additionally, we use large-area AFM to characterize surface modifications on silicon substrates, observing a significant reduction in bacterial density. This highlights the potential of this method for studying surface modifications to better understand and control bacterial adhesion and biofilm formation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Analysis of RF Sheath-Driven Tungsten Erosion at RF Antenna in the WEST Tokamak

This study applies the newly developed STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to analyze tungsten (W) erosion at RF antenna structures in the WEST tokamak. STRIPE integrates SolEdge3x for edge plasma backgrounds, COMSOL for 3D RF sheath potentials, RustBCA for sputtering yields, and GITR for impurity transport and ion energy–angle distributions. Building on prior work by Kumar et al. (2025) Nuclear Fusion, 65, 076039, which validated STRIPE for WEST ICRH discharge #57877, the present study provides a spatially resolved assessment of gross W erosion at both Q2 antenna limiters under ohmic and ICRH conditions. Simulations using 2D SolEdge3x profiles in COMSOL capture rectified sheath potentials exceeding 300 V, leading to strong upper-limiter localization. Both poloidal and toroidal asymmetries are observed and attributed to RF sheath effects, with modeled erosion patterns deviating from experiment—highlighting sensitivity to sheath geometry and plasma resolution. Erosion is driven primarily by high-charge-state oxygen ions (O6+–O8+), while D+ plays a negligible role. Assuming a plasma composition of 1% oxygen and 98% deuterium, STRIPE predicts a 30-fold increase in gross W erosion from ohmic to ICRH phases, consistent with a >25-fold rise in W-I (400.9 nm) brightness. Quantitative agreement is within 5% in the ohmic phase and 30% under ICRH, demonstrating predictive capability. Importantly, the study shows that the magnitude of ICRH-driven W erosion depends strongly on the concentration of light impurities (O, B, N, C), which drive sputtering through high charge states. Cleaner plasma conditions with reduced impurity content are therefore expected to substantially mitigate antenna W sources in WEST and other toroidal fusion devices. These findings establish STRIPE as a predictive framework for RF-induced plasma–material interactions and support its application to reactor-scale antenna design.

Kumar, Atul [ORNL] (ORCID:0000000277475210)↗
DOE OSTI2021

New methodology for measuring electron density perturbations caused by plasma coherent modes using profile reflectometry: Magnitudes and radial profiles in DIII-D

New capabilities of fast-sweep frequency-modulated profile reflectometry are explored to measure electron density ne perturbation magnitudes and radial profiles due to plasma coherent modes in DIII-D. The first approach is based on the frequency analysis of phase perturbations associated with high frequency (~MHz) Alfvén eigenmodes (AEs). The measurement of ~5.5 MHz fast-ion-driven global Alfvén eigenmodes (GAEs) is demonstrated in a neutral beam-heated DIII-D plasma. The GAE induced a broad radial distribution of phase perturbations in the profile reflectometer data. Analysis of these data determined the effective cutoff location displacement and the estimated ne fluctuation profile. In the second approach, high resolution ne profiles are used directly to determine the radial structure of ne perturbations due to a neo-classical tearing mode. These new measurements broaden the application of profile reflectometry and advance the development of AE spectroscopy as a tool for non-invasive diagnosis of fast-ion-driven modes in DIII-D and burning plasmas such as ITER.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Membrane potential sensing: Material design and method development for single particle optical electrophysiology

We review the development of “single” nanoparticle-based inorganic and organic voltage sensors, which can eventually become a viable tool for “non-genetic optogenetics.” The voltage sensing is accomplished with optical imaging at the fast temporal response and high spatial resolutions in a large field of view. Inorganic voltage nanosensors utilize the Quantum Confined Stark Effect (QCSE) to sense local electric fields. Engineered nanoparticles achieve substantial single-particle voltage sensitivity (~2% Δλ spectral Stark shift up to ~30% ΔF/F per 160 mV) at room temperature due to enhanced charge separation. A dedicated home-built fluorescence microscope records spectrally resolved images to measure the QCSE induced spectral shift at the single-particle level. Biomaterial based surface ligands are designed and developed based on theoretical simulations. The hybrid nanobiomaterials satisfy anisotropic facet-selective coating, enabling effective compartmentalization beyond non-specific staining. Self-spiking- and patched-HEK293 cells and cortical neurons, when stained with hybrid nanobiomaterials, show clear photoluminescence intensity changes in response to membrane potential (MP) changes. Organic voltage nanosensors based on polystyrene beads and nanodisk technology utilize Fluorescence (Förster) Resonance Energy Transfer (FRET) to sense local electric fields. Voltage sensing FRET pairs achieve voltage sensitivity up to ~35% ΔF/F per 120 mV in cultures. Non-invasive MP recording from individual targeted sites (synapses and spines) with nanodisks has been realized. However, both of these QCSE- and FRET-based voltage nanosensors yet need to reach the milestone of recording individual action potentials from individual targeted sites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Probing the atmospheric boundary layer with integrated remote-sensing platforms during the American WAKE ExperimeNt (AWAKEN) campaign

The American WAKE ExperimeNt (AWAKEN) collaboration is an observational-based field campaign in northern Oklahoma intended to analyze the potential influence of onshore wind farms and their collective wakes on wind power production, turbine structural loads, and on the atmospheric boundary layer (ABL). Focusing on the ABL effects, the University of Oklahoma and the Lawrence Livermore National Laboratory collected continuous high-resolution kinematic and thermodynamic profile measurements during 2022 and Summer 2023. The deployment strategy for these campaigns is detailed first, followed by an initial comparison of data from two sites in the AWAKEN domain: a near-farm site to examine collective wake impacts on the ABL, and a far-field site remaining outside the wind farm-waked region. Here, we summarize the datasets available and demonstrate the benefits of these observations and multiple value-added products (VAPs) for investigation of ABL features observed during AWAKEN. We also highlight examples of preliminary analyses, including ABL height detection and nocturnal low-level jet examination, which are produced using novel VAPs based on optimal estimation to retrieve deeper Doppler lidar wind profiles than previously resolved, along with their uncertainty. By including the near-farm and far-field site in these analyses, we identified a pattern of stronger lower-atmospheric mixing at the near-farm site than the far-field site, motivating deeper investigation into the relationship between wind farms and general ABL characteristics. Future analysis will delve deeper into this relationship by examining other ABL characteristics, such as atmospheric stability and convection.

17 WIND ENERGY↗