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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optimizing the electron transport chain to sustainably improve photosynthesis

Abstract Genetically improving photosynthesis is a key strategy to boosting crop production to meet the rising demand for food and fuel by a rapidly growing global population in a warming climate. Many components of the photosynthetic apparatus have been targeted for genetic modification for improving photosynthesis. Successful translation of these modifications into increased plant productivity in fluctuating environments will depend on whether the electron transport chain (ETC) can support the increased electron transport rate without risking overreduction and photodamage. At present atmospheric conditions, the ETC appears suboptimal and will likely need to be modified to support proposed photosynthetic improvements and to maintain energy balance. Here, I derive photochemical equations to quantify the transport capacity and the corresponding reduction level based on the kinetics of redox reactions along the ETC. Using these theoretical equations and measurements from diverse C3/C4 species across environments, I identify several strategies that can simultaneously increase the transport capacity and decrease the reduction level of the ETC. These strategies include increasing the abundances of reaction centers, cytochrome b6f complexes, and mobile electron carriers, improving their redox kinetics, and decreasing the fraction of secondary quinone–nonreducing photosystem II reaction centers. I also shed light on several previously unexplained experimental findings regarding the physiological impacts of the abundances of the cytochrome b6f complex and plastoquinone. The model developed, and the insights generated from it facilitate the development of sustainable photosynthetic systems for greater crop yields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Grid-Forming Wind: Getting Ready for Prime Time With and Without Inverters

Variable wind power is one of the fastest-growing energy-generation technologies, harnessing the energy of wind, both on land and at sea. During the past decade, the global share of wind power has grown tremendously, and wind power is evolving into a major contributor to electricity supplies in many countries. In this journey, wind is also becoming a source of reliability services to the grid, which has required grid-supporting functions originally provided by synchronous generators, enabling very high levels of instantaneous penetration (ranging from 60 to 70% in some power systems). To get beyond this, a fundamental shift is required to address challenges associated with the transition to a grid with only a few remaining (or even without any) conventional synchronous generators while achieving a minimum acceptable level of stability.

energy consumption↗
DOE OSTI2020

Pervasive shifts in forest dynamics in a changing world

Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. In combination, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ground observations of a space laser for the assessment of its in-orbit performance

The wind mission Aeolus of the European Space Agency was a groundbreaking achievement for Earth observation. Between 2018 and 2023, the space-borne lidar instrument ALADIN onboard the Aeolus satellite measured atmospheric wind profiles with global coverage, which contributed to improving the accuracy of numerical weather prediction. The precision of the wind observations, however, declined over the course of the mission due to a progressive loss of the atmospheric backscatter signal. The analysis of the root cause was supported by the Pierre Auger Observatory in Argentina whose fluorescence detector registered the ultraviolet laser pulses emitted from the instrument in space, thereby offering an estimation of the laser energy at the exit of the instrument for several days in 2019, 2020, and 2021. The reconstruction of the laser beam not only allowed for an independent assessment of the Aeolus performance, but also helped to improve the accuracy in the determination of the laser beam’s ground track on single pulse level. The results presented in this paper set a precedent for the monitoring of space lasers by ground-based telescopes and open new possibilities for the calibration of cosmic-ray observatories.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Software: gcamusa_v5.3_im3

GCAM USA version (gcamusa_v5p3_im3) supporting the publication: Khan, Z., Zhao, M., Ahsan, H., Wolfram, P., Vernon, C., Rice, J., Iyer, G., Binsted, M., Snyder, A., Graham, N., Kyle, P., 2023. Coevolution of future water, energy and land systems across the United States in response to national and global socioeconomic, climate, and energy policy drivers. (In progress) Earths Future, DOI: XXX with further details available at the meta-repository: https://immm-sfa.github.io/khan-etal_2023_im3gcamusa

Climate Change↗
DOE OSTI2023

Output Data: gcam_usa_v5p3_im3

Output databases and tables supporting the publication: Khan, Z., Zhao, M., Ahsan, H., Wolfram, P., Vernon, C., Rice, J., Iyer, G., Binsted, M., Snyder, A., Graham, N., Kyle, P., 2023. Coevolution of future water, energy and land systems across the United States in response to national and global socioeconomic, climate, and energy policy drivers. (In progress) Earths Future, DOI: XXX with further details available at the meta-repository: https://immm-sfa.github.io/khan-etal_2023_im3gcamusa Folder: gcam_usa_im3_outputs (GCAM USA v 5.3 used for IM3 experiment Output Databases): database_rcp45cooler_ssp3.tar.gz database_rcp45cooler_ssp5.tar.gz database_rcp45hotter_ssp5.tar.gz database_rcp45hotter_ssp5.tar.gz database_rcp85cooler_ssp3.tar.gz database_rcp85cooler_ssp5.tar.gz database_rcp85hotter_ssp5.tar.gz database_rcp85hotter_ssp5.tar.gz Folder: gcam_usa_im3_gcamextractor_outputs (gcamextractor post-processing of the GCAM databases for specific downstream models ): dataGCAM_cerf.tar.gz (For CERF Model) dataGCAM_demeter.tar.gz (For Demeter model) dataGCAM_go.tar.gz (For GO model) gcamDataTable.tar.gz (All IM3 scenarios)

Climate Change↗
DOE OSTI2022

FY 2022 Site Sustainability Plan

The United States Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) is a recognized leader in sustainability, as evidenced by its ongoing optimization of resources in campus operations, including water, energy, waste, and purchasing, as well as continued commitment to meeting federal mandates and goals. NREL's dedication to sustainability supports the laboratory's success by applying what is learned through research and development to campus facilities and infrastructure systems. The Site Sustainability Plan provides a road map for all site planning and development. NREL drives the adoption of these initiatives to a global audience by conducting business operations that demonstrate the incorporation of clean energy practices. NREL will continue to develop a sustainable and resilient campus while growing greater technological capabilities to advance the national renewable energy marketplace.

climate change↗
DOE OSTI2024

How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?

Abstract The historic 22–26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record‐breaking rainfall and $3 billion of damage in the region. A month‐long regional convection‐permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high‐emission (SSP5‐8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4–6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low‐level jet. A warmer and moister lower troposphere leads to 36%–59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold. However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak‐to‐moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Data Library of Liquid Clouds Modelled With a Large Eddy Simulation Framework

We describe a library of atmospheric large eddy simulations (LES) of liquid-phase boundary layer clouds constructed to enable aerosol–cloud–turbulence interaction studies, support parameterization evaluation and development, and provide training data for machine learning applications. The simulations use a modern LES framework designed for high numerical accuracy, coupled to a detailed spectral bin microphysical scheme. Case studies are configured to represent observed conditions in four key global cloud regions—the Northeastern Atlantic, Northeastern Pacific, Continental United States and Southern Ocean—following a semi-idealised approach. The library also includes aerosol concentration halving and doubling experiments to expose the sensitivities of the case studies to aerosol perturbations. Simulation results are compared to observations on a case-by-case basis, then the library's coverage is evaluated in terms of spreads in meteorological factors and atmospheric boundary layer attributes.

aerosol↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Mechanistic Evaluation of Black Cohosh Extract-Induced Genotoxicity in Human Cells

Abstract Black cohosh extract (BCE) is marketed to women as an alternative to hormone replacement therapy for alleviating menopausal symptoms. Previous studies by the National Toxicology Program revealed that BCE induced micronuclei (MN) and a nonregenerative macrocytic anemia in rats and mice, likely caused by disruption of the folate metabolism pathway. Additional work using TK6 cells showed that BCE induced aneugenicity by destabilizing microtubules. In the present study, BCE-induced MN were confirmed in TK6 and HepG2 cells. We then evaluated BCE-induced DNA damage using the comet assay at multiple time points (0.5–24 h). Following a 0.5-h exposure, BCE induced significant, concentration-dependent increases in %tail DNA in TK6 cells only. Although DNA damage decreased in TK6 cells over time, likely due to repair, small but statistically significant levels of DNA damage were observed after 2 and 4 h exposures to 250 µg/ml BCE. A G1/S arrest in TK6 cells exposed to 125 µg/ml BCE (24 h) was accompanied by apoptosis and increased expression of γH2A.X, p-Chk1, p-Chk2, p53, and p21. Conditioning TK6 cells to physiological levels of folic acid (120 nM) did not increase the sensitivity of cells to BCE-induced DNA damage. BCE did not alter global DNA methylation in TK6 and HepG2 cells cultured in standard medium. Our results suggest that BCE induces acute DNA strand breaks which are quickly repaired in TK6 cells, whereas DNA damage seen at 4 and 24 h may reflect apoptosis. The present study supports that BCE is genotoxic mainly by inducing MN with an aneugenic mode of action.

Seo, Ji-Eun↗
DOE OSTI2021

Euclidean gravity and holography

We discuss a recent proposal that the Euclidean gravity approach to quantum gravity is correct if and only if the theory is holographic, providing several examples and general arguments to support the conjecture. This provides a natural mechanism for the low-energy gravitational effective field theory to access a host of deep ultraviolet properties, like the Bekenstein–Hawking entropy of black holes, the unitarity of black hole evaporation, and the lack of exact global symmetries.

Astronomy & Astrophysics↗
DOE OSTI2022

PyZebrascope: An Open-Source Platform for Brain-Wide Neural Activity Imaging in Zebrafish

Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An Experimental and Numerical Study of Plasma Treatment Effects on Adhesive Joints of Metals, CFRTPs, and Metal–CFRTP Combinations

This work investigates the effects of plasma surface treatment on both the local adherend–adhesive interfacial strength and the global lap shear strength (LSS) of adhesively-bonded joints composed of metals, CFRTPs, and metal-CFRTP combinations, using AA6061 and CFRPPA as representative examples. Experimental and computational results show that plasma treatment improved the ratio of CFRPPA–adhesive interfacial normal strength to adhesive tensile strength (sint,n/scri,t,adhesive) from 0.59–1.00 to 0.91–1.73, leading to an average 90% increase in LSS for CFRPPA-related joints. In contrast, the LSS and sint,n/scri,t,adhesive values of AA6061–AA6061 joints (>=2.27) remained nearly unchanged after plasma treatment. The enhanced joint bonding was further supported by post-failure surface morphologies from three-dimensional profilometry and simulations, showing a transition from dominant interfacial failure to mixed modes involving interfacial, cohesive, and slight fiber-peeling failures. The LSS evolution with respect to sint,n/scri,t,adhesive was numerically established until the saturation of the LSS of various joints, and the behavior of AA6061–CFRPPA T-joints with varying interfacial strengths was also modeled. This study highlights the importance of local interfacial bonding strength, rather than global LSS, for understanding and optimizing the mechanical performance of adhesively-bonded composite–metal and composite-composite structures via different surface modification strategies.

Qiao, Yao (ORCID:0000000265867604)↗
DOE OSTI2022

Support for climate policy researchers

In the past 2 years, the European Union and the United States announced plans to spend $573 and $391 billion, respectively, through 2030 on climate actions and passed landmark legislation such as the US Inflation Reduction Act. Although unprecedented in size and scope, these combined investments of $964 billion pale in comparison to the more than $4 trillion in global clean energy investment needed annually by 2030 to stay on track for net zero greenhouse gas emissions by 2050. Furthermore, to maximize the impact of this public money, efficient policies informed by independent, objective analysis will be needed. Yet scientists who commit to policy-relevant research face unique challenges that must be addressed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Pinto, G↗
DOE OSTI2024

Self-assembled reconfigurable pump architectures via magnetic colloidal swarms

Self-assembled swarms of interactive active units, which are adaptive and dynamically reconfigurable to accommodate different functionalities, represent a promising platform for the development of next-generation robotics. Here, we utilize the emergent collective behavior of active magnetic colloids confined in quasi-two-dimensional arrays of overlapping wells to demonstrate the self-organization of a colloidal swarm into a dynamic pump architecture capable of controlled transport of passive cargo particles. This dynamic architecture provides a global unidirectional looping flow pattern along the entire length of the system. We show that the flow direction of the dynamic swarm-based pump can be externally controlled by a phase shift of a driving magnetic field energizing the swarm. The experimental observations are supported by computational modeling based on phenomenological coarse-grained particle dynamics coupled to shallow-water Navier-Stokes hydrodynamics. In conclusion, our findings demonstrate how the emergent collective behavior of a swarm can be orchestrated into a desired functionality by exploiting the interplay between activity and confinement potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Surface Radiation Balance Dataset from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

Abstract A field campaign at Siple Dome in West Antarctica during the austral summer 2019/20 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-powered equipment, includes surface energy balance measurements, meteorology, and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model’s snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6- μ m atmospheric window reveal cloud phase transitions associated with cloud life cycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations. significance statement Antarctica is a critical region for understanding climate change and sea level rise, as the great ice sheets and the ice shelves are subject to increasing risk as global climate warms. Climate models have difficulties over Antarctica, particularly with simulation of cloud properties that regulate snow surface melting or refreezing. Atmospheric and climate-related field work has significant challenges in the Antarctic, due to the small number of research stations that can support state-of-the-art equipment. Here we present new data from a suite of transportable and solar-powered instruments that can be deployed to remote Antarctic sites, including regions where ice shelves are most at risk, and we demonstrate how key components of climate model simulations can be evaluated against these data.

54 ENVIRONMENTAL SCIENCES↗