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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tree architecture: A strigolactone-deficient mutant reveals a connection between branching order and auxin gradient along the tree stem

Due to their long lifespan, trees and bushes develop higher order of branches in a perennial manner. In contrast to a tall tree, with a clearly defined main stem and branching order, a bush is shorter and has a less apparent main stem and branching pattern. To address the developmental basis of these two forms, we studied several naturally occurring architectural variants in silver birch (Betula pendula). Using a candidate gene approach, we identified a bushy kanttarelli variant with a loss-of-function mutation in the BpMAX1 gene required for strigolactone (SL) biosynthesis. While kanttarelli is shorter than the wild type (WT), it has the same number of primary branches, whereas the number of secondary branches is increased, contributing to its bush-like phenotype. To confirm that the identified mutation was responsible for the phenotype, we phenocopied kanttarelli in transgenic BpMAX1::RNAi birch lines. SL profiling confirmed that both kanttarelli and the transgenic lines produced very limited amounts of SL. Interestingly, the auxin (IAA) distribution along the main stem differed between WT and BpMAX1::RNAi. In the WT, the auxin concentration formed a gradient, being higher in the uppermost internodes and decreasing toward the basal part of the stem, whereas in the transgenic line, this gradient was not observed. Through modeling, we showed that the different IAA distribution patterns may result from the difference in the number of higher-order branches and plant height. Future studies will determine whether the IAA gradient itself regulates aspects of plant architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

CMIP7 data request: Earth system priorities and opportunities

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns, emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Competition between inside-out unfolding and pathogenic aggregation in an amyloid-forming β-propeller

Studies of folded-to-misfolded transitions using model protein systems reveal a range of unfolding needed for exposure of amyloid-prone regions for subsequent fibrillization. Here, we probe the relationship between unfolding and aggregation for glaucoma-associated myocilin. Mutations within the olfactomedin domain of myocilin (OLF) cause a gain-of-function, namely cytotoxic intracellular aggregation, which hastens disease progression. Aggregation by wild-type OLF (OLF WT ) competes with its chemical unfolding, but only below the threshold where OLF loses tertiary structure. Representative moderate (OLF D380A ) and severe (OLF I499F ) disease variants aggregate differently, with rates comparable to OLF WT in initial stages of unfolding, and variants adopt distinct partially folded structures seen along the OLF WT urea-unfolding pathway. Whether initiated with mutation or chemical perturbation, unfolding propagates outward to the propeller surface. In sum, for this large protein prone to amyloid formation, the requirement for a conformational change to promote amyloid fibrillization leads to direct competition between unfolding and aggregation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Understanding Bifacial PV Modeling: Raytracing and View Factor Models

As part of the PV Magazine Webinar 'View-factor vs. ray tracing - which bifacial modelling techniques should you use?,' the second part lead by NREL will explore the different rear-irradiance calculation software, which fall into two categories: view-factor and ray-tracing models. View factor models assume isotropic scattering of reflected rays, allowing for calculation of irradiance by integration. Due-diligence software such as PVSyst or SAM use the view-factor model. Ray-tracing models simulate multipath reflection and absorption of individual rays entering a scene. Raytracing software such as bifacial_radiance, which is the only available open-source toolkit, offers the possibility of reproducing complex scenes, including shading or finite-system edge effects. This can be useful to evaluate specific system geometries, shading and obstructions, areas of different albedo and electrical mismatch, all topics of interest to the prediction of bifacial performance. Model agreement for view factors and bifacial_radiance software is improving and has been shown to be between 2% (absolute) when compared with measured results.

14 SOLAR ENERGY↗
DOE OSTI2019

Modeling fuel assembly distortion in light water reactors using BISON

Individual fuel rods in a light water reactor fuel assembly (FA) often have different power profiles that result in a spatial temperature gradient in the FA. This temperature gradient causes some rods to elongate more than others which could in turn distort the entire FA. Such FA distortion has been known to cause operational difficulties with refueling and with the insertion of control rods [1]. Numerical modeling of FA distortion due to differential elongation of fuel rods requires: (i) modeling the thermal elongation as well as the creep and irradiation induced elongation of the fuel rods, and (ii) modeling the bending response of the FA due to the differential elongation of the fuel rods. Past studies on this topic have been focused on the mechanical deformation of the FA with simplistic modeling of fuel rod elongation. In this paper, a simulation approach is presented that directly couples 2D axisymmetric fuel performance models of the individual fuel rods in a FA with structural beam models of the fuel rods in a FA using the Bison code [2]. This allows all of the mechanisms internal to the fuel rod leading to cladding elongation to be accounted for in the FA distortion model. To demonstrate this capability, the bowing of a simplified and idealized 6x6 FA (Fig. 1a), modeled using beam elements, is analyzed under different spatially varying power profiles. For this study, the guide tubes are rigidly connected to the spacer grid. Also, the fuel rods are rigidly connected to the spacer grid in all directions except the vertical direction (Y) along which slipping can occur between the fuel rods and spacer grids, depending on the friction coefficient between them. Coupling the thermo-mechanical response of individual fuel rods to the global mechanical behavior of the FA can shed light on the mechanisms that cause distortion of the FA and potential solutions to address this issue.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2012

A Summary of Coupled, Uncoupled, and Hybrid Tectonic Models for the Yakima Fold Belt

This document is one in a series of topical reports compiled by the Pacific Northwest National Laboratory to summarize technical information on selected topics important to the performance of a probabilistic seismic hazard analysis of the Hanford Site. The purpose of this report is to summarize the range of opinions and supporting information expressed by the expert community regarding whether a coupled or uncoupled model, or a combination of both, best represents structures in the Yakima Fold Belt. This issue was assessed to have a high level of contention with up to moderate potential for impact on the hazard estimate. This report defines the alternative conceptual models relevant to this technical issue and the arguments and data that support those models. It provides a brief description of the technical issue and principal uncertainties; a general overview on the nature of the technical issue, along with alternative conceptual models, supporting arguments and information, and uncertainties; and finally, suggests some possible approaches for reducing uncertainties regarding this issue.

Chamness, Michele A.↗
DOE OSTI2017

CO 2 mineral trapping in fractured basalt

Fractures in basalt can provide substantial surface area for reactions, and limited mass transfer in fractures can allow accumulation of cations to form carbonate minerals in geologic carbon sequestration. In this study, flood basalt and serpentinized basalt with engineered fractures were reacted in water equilibrated with 10 MPa CO 2 at 100 °C or 150 °C for up to 40 weeks. Carbonation in basalt fractures was observed as early as 6 weeks, with Mg- and Ca-bearing siderite formed in both basalts reacted at 100 °C and Mg-Fe-Ca carbonate minerals formed in the flood basalt reacted at 150 °C. X-ray μCT segmentation revealed that precipitates filled 5.4% and 15% (by volume) of the flood basalt fracture after 40 weeks of reaction at 100 °C and 150 °C, respectively. Zones of elevated carbonate abundance did not completely seal the fracture. Limited siderite clusters (<1% volume fraction) were found in localized areas in the serpentinized basalt fracture. A 1-dimensional reactive transport model developed in CrunchTope examined how geochemical gradients drive silicate mineral dissolution and carbonate precipitation in the fracture. The model predicts that siderite will form as early as 1 day after the addition of CO 2 . In conclusion, the predicted location of maximum siderite abundance is consistent with experimental observations, and the predicted total carbonate volumes are comparable to estimates derived from CT segmentation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Unraveling capacity fading in lithium-ion batteries using advanced cyclic tests: A real-world approach

Battery lifespan estimation is essential for effective battery management systems, aiding users and manufacturers in strategic planning. However, accurately estimating battery capacity is complex, owing to diverse capacity fading phenomena tied to factors such as temperature, charge-discharge rate, and rest period duration. In this work, we present an innovative approach that integrates real-world driving behaviors into cyclic testing. Unlike conventional methods that lack rest periods and involve fixed charge-discharge rates, our approach involves 1000 unique test cycles tailored to specific objectives and applications, capturing the nuanced effects of temperature, charge-discharge rate, and rest duration on capacity fading. This yields comprehensive insights into cell-level battery degradation, unveiling growth patterns of the solid electrolyte interface (SEI) layer and lithium plating, influenced by cyclic test parameters. Here, the results yield critical empirical relations for evaluating capacity fading under specific testing conditions.

25 ENERGY STORAGE↗
DOE OSTI2015

Characterization of Red/Green Cyanobacteriochrome NpR6012g4 by Solution Nuclear Magnetic Resonance Spectroscopy: A Hydrophobic Pocket for the C15- E,anti Chromophore in the Photoproduct

Cyanobacteriochromes (CBCRs) are cyanobacterial photosensory proteins distantly related to phytochromes. Like phytochromes, CBCRs reversibly photoconvert between a dark-stable state and a photoproduct via photoisomerization of the 15,16-double bond of their linear tetrapyrrole (bilin) chromophores. CBCRs provide cyanobacteria with complete coverage of the visible spectrum and near-ultraviolet region. One CBCR subfamily, the canonical red/green CBCRs typified by AnPixJg2 and NpR6012g4, can function as sensors of light color or intensity because of their great variation in photoproduct stability. The mechanistic basis for detection of green light by the photoproduct state in this subfamily has proven to be a challenging research topic, with competing hydration and trapped-twist models proposed. Here, we use 13 C-edited and 15 N-edited 1 H- 1 H NOESY solution nuclear magnetic resonance spectroscopy to probe changes in chromophore configuration and protein-chromophore interactions in the NpR6012g4 photocycle. Our results confirm a C15-Z,anti configuration for the red-absorbing dark state and reveal a C15-E,anti configuration for the green-absorbing photoproduct. The photoactive chromophore D-ring is located in a hydrophobic environment in the photoproduct, surrounded by both aliphatic and aromatic residues. Here, characterization of variant proteins demonstrates that no aliphatic residue is essential for photoproduct tuning. Taken together, our results support the trapped-twist model over the hydration model for the red/green photocycle of NpR6012g4.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Microbial community-level regulation explains soil carbon responses to long-term litter manipulations

Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity, mediated by changes in plant inputs. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the source of these problems in four archetypal models and propose a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. We compare model predictions to 24 long-term C-input field manipulations and identify key benchmarks. The proposed formulation reproduces soil C responses to long-term C-input changes and implies greater SOC storage associated with CO 2 -fertilization-driven increases in C inputs over the coming century compared to recent microbial models. This study provides a simple modification to improve microbial models for inclusion in Earth System Models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum . The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Strain-controlled thermal conductivity in ferroic twinned films

Large reversible changes of thermal conductivity are induced by mechanical stress, and the corresponding device is a key element for phononics applications. We show that the thermal conductivity k of ferroic twinned thin films can be reversibly controlled by strain. Nonequilibrium molecular dynamics simulations reveal that thermal conductivity decreases linearly with the number of twin boundaries perpendicular to the direction of heat flow. Our demonstration of large and reversible changes in thermal conductivity driven by strain may inspire the design of controllable thermal switches for thermal logic gates and all-solid-state cooling devices.

SURFACES, INTERFACES AND THIN FILMS↗
DOE OSTI2017

3D Bragg coherent diffractive imaging of five-fold multiply twinned gold nanoparticle

The formation mechanism of five-fold multiply twinned nanoparticles has been a long-term topic because of their geometrical incompatibility. So, various models have been proposed to explain how the internal structure of the multiply twinned nanoparticles accommodates the constraints of the solid-angle deficiency. Here, we investigate the internal structure, strain field and strain energy density of 600 nm sized five-fold multiply twinned gold nanoparticles quantitatively using Bragg coherent diffractive imaging, which is suitable for the study of buried defects and three-dimensional strain distribution with great precision. Our study reveals that the strain energy density in five-fold multiply twinned gold nanoparticles is an order of magnitude higher than that of the single nanocrystals such as an octahedron and triangular plate synthesized under the same conditions. This result indicates that the strain developed while accommodating an angular misfit, although partially released through the introduction of structural defects, is still large throughout the crystal.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nanoscale laser flash measurements of diffuson transport in amorphous Ge and Si

The thermal properties of amorphous materials have attracted significant attention due to their technological importance in electronic devices. In addition, the disorder-induced breakdown of the phonon gas model makes vibrational transport in amorphous materials a topic of fundamental interest. In the past few decades, theoretical concepts, such as propagons, diffusons, and locons, have emerged to describe different types of vibrational modes in disordered solids. However, experiments can struggle to accurately determine which types of vibrational states carry the majority of the heat. In this study, we use nanoscale laser flash measurements (front/back time-domain thermoreflectance) to investigate thermal transport mechanisms in amorphous Ge and amorphous Si thin-films. We observe a nearly linear relationship between the amorphous film’s thermal resistance and the film’s thickness. The slope of the film’s thermal resistance vs thickness corresponds to a thickness-independent thermal conductivity of 0.4 and 0.6 W/(m K) for a-Ge and a-Si, respectively. This result reveals that the majority of heat currents in amorphous Si and Ge thin films prepared via RF sputtering at room temperature are carried by diffusons and/or propagons with mean free paths less than a few nanometers.

36 MATERIALS SCIENCE↗