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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

NO 2 Interactions with MoO 3 and CuO at Atmospherically Relevant Pressures

NO x concentrations in some geographic regions are harmful to human health. Gas filters to trap NO x and other toxic chemicals contain metal oxides, including MoO 3 and CuO. These materials are also being investigated for NO x gas sensors. In a step to understand the fundamental adsorption mechanism in sensors and the effect on binding site availability in gas filters, ambient-pressure X-ray photoelectron spectroscopy (APXPS) was used to study the interaction of NO 2 with polycrystalline MoO 3 and CuO surfaces under pressures up to 0.01 Torr (14 parts per million volume (ppmv)). Density functional theory-based computational modeling was performed to reveal the mechanisms of NO 2 interactions with the MoO 3 (010) and CuO(111) surfaces to aid interpretation of the experimental results. With pressure dependence, NO 2 interacts with reduced Mo 5+ atoms generated by oxygen vacancies and abstracts hydrogen atoms from hydroxyl groups on MoO 3 without accumulating N-containing species on the surface; vacancy-induced electronic states in the band gap are also removed, hinting toward an increase in the resistivity of the material. N-containing species begin accumulating on the CuO surface at atmospherically relevant pressures of 140 ppbv. NO 2 only decomposes at oxygen vacancy sites of CuO. The nitrogen species leave the CuO surface upon evacuation, highlighting the importance of in situ surface characterization when studying gas sensing and adsorption mechanisms. Finally, these results imply that NO 2 removes hydroxyl and O vac binding sties on these materials when used in gas filtration and sensing applications. Furthermore, the results show the key role of O vac sites in the gas sensing mechanism of MoO 3 and highlight the potential of APXPS for further studies of gas sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mine Water Use, Treatment, and Reuse in the United States: A Look at Current Industry Practices and Select Case Studies

Mining provides significant economic value while often impacting local water supplies and environments because of freshwater usage and waste disposal practices. In this paper we identify current practices in mine water, including how water is used in mining, influent and effluent water quality, treatment technologies, and end uses with the goal of informing future research on implementable, reliable, and cost-effective advanced water treatment in the mining sector. This study also reviews the available literature to broadly evaluate mining in the United States and performs a techno-economic assessment on water use and disposal for three detailed case studies applicable to lithium, uranium, and copper mines. These case studies highlight specific industry examples of distinct extraction methods, geographical regions, and mined commodities. Hypothetical scenarios based on case study baselines revealed potential impacts to mine water available for beneficial reuse through the use of novel water treatment technologies and alternate water management strategies. Finally, an assessment of national level impacts resulting from the reuse of treated mine source water is presented.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Challenges and Roadmap for Solar-Thermal Desalination

Decarbonizing desalination systems requires combining renewable energy technologies with desalination systems. Solar thermal desalination, which combines a thermal desalination system (e.g., distillation, multistage flash, etc.) with a concentrated solar system is attractive in geographic regions with an abundance of saline water and solar energy. However, the high economic cost and low efficiency limits its adoption. This review provides an overview of the techno-economic, materials, and performance challenges that need to be overcome to realize solar-desalination. The review describes the four most prominent pathways for overcoming thermodynamic and cost limitations present in current systems. Specifically, methodologies and approaches such as direct solar evaporation structures, low-cost thermal energy storage, solar cogeneration schemes for power and desalination, and solar hybrid desalination are discussed. Furthermore, these strategies can enable solar thermal desalination technologies that are resilient to intermittent solar energy.

14 SOLAR ENERGY↗
DOE OSTI2024

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Examining Clustered MCSs and Their Precipitation Significance Over Global Land MCS Hotspots

Multiple independent mesoscale convective systems (MCSs) can occur closely in space and time, subjecting common geographic regions to multiple precipitation events over short periods. Motivated by an earlier study highlighting significant hydrological impacts of clustered MCSs in the US, we extend the analysis to seven global continental MCS hotspots using a 20‐year global MCS data set. Our findings reveal that (a) 40%–80% of MCSs occur in clusters, depending on the region; (b) clustered MCSs exhibit larger rainfall areas and higher mean areal rainfall, with the highest values observed in Argentina and India; and (c) clustered MCSs contribute increasing fractions to more extreme 24‐ and 72‐hr precipitation events across all regions. Our results underscore the critical role of clustered MCSs in driving hydrological extremes over global land regions and highlight the need to better understand how their formation mechanisms differ from non‐clustered events across the globe.

Hu, Huancui [Pacific Northwest National Laboratory↗
DOE OSTI2024

Century-long timelines of herbarium genomes predict plant stomatal response to climate change

Abstract Dissecting plant responses to the environment is key to understanding whether and how plants adapt to anthropogenic climate change. Stomata, plants’ pores for gas exchange, are expected to decrease in density following increased CO 2 concentrations, a trend already observed in multiple plant species. However, it is unclear whether such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 Arabidopsis thaliana historical herbarium specimens collected over 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a functional score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the past centuries, suggesting a genetic component contributing to this change. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes may have already responded to climate change through adaptive evolution.

Environmental Sciences & Ecology↗
DOE OSTI2021

Potential ecological impacts of climate intervention by reflecting sunlight to cool Earth

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth’s surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth’s albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth’s climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth’s surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goals and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data-driven predictions of the time remaining until critical global warming thresholds are reached

Leveraging artificial neural networks (ANNs) trained on climate model output, we use the spatial pattern of historical temperature observations to predict the time until critical global warming thresholds are reached. Although no observations are used during the training, validation, or testing, the ANNs accurately predict the timing of historical global warming from maps of historical annual temperature. The central estimate for the 1.5 °C global warming threshold is between 2033 and 2035, including a ±1σ range of 2028 to 2039 in the Intermediate (SSP2-4.5) climate forcing scenario, consistent with previous assessments. However, our data-driven approach also suggests a substantial probability of exceeding the 2 °C threshold even in the Low (SSP1-2.6) climate forcing scenario. While there are limitations to our approach, our results suggest a higher likelihood of reaching 2 °C in the Low scenario than indicated in some previous assessments—though the possibility that 2 °C could be avoided is not ruled out. Explainable AI methods reveal that the ANNs focus on particular geographic regions to predict the time until the global threshold is reached. Our framework provides a unique, data-driven approach for quantifying the signal of climate change in historical observations and for constraining the uncertainty in climate model projections. Given the substantial existing evidence of accelerating risks to natural and human systems at 1.5 °C and 2 °C, our results provide further evidence for high-impact climate change over the next three decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A global assessment of policy tools to support climate adaptation

Governments, businesses, and civil society organizations have diverse policy tools to incentivize adaptation. Policy tools can shape the type and extent of adaptation, and therefore, function either as barriers or enablers for reducing risk and vulnerability. Using data from a systematic review of academic literature on global adaptation responses to climate change (n = 1549 peer-reviewed articles), we categorize the types of policy tools used to shape climate adaptation. We apply qualitative and quantitative analyses to assess the contexts where particular tools are used, along with equity implications for groups targeted by the tools, and the tools’ relationships with transformational adaptation indicators such as the depth, scope, and speed of adaptation. We find diverse types of tools documented across sectors and geographic regions. We also identify a mismatch between the tools that consider equity and those that yield more transformational adaptations. Direct regulations, plans, and capacity building are associated with higher depth and scope of adaptation (thus transformational adaptation), while economic instruments, information provisioning, and networks are not; the latter tools, however, are more likely to target marginalized groups in their design and implementation. We identify multiple research gaps, including a need to assess instrument mixes rather than single tools and to assess adaptations that result from policy implementation. Key policy insights Information-based approaches, networks, and economic instruments are the most frequently documented adaptation policy tools worldwide. Direct regulations, plans, and capacity building are associated with higher depth and scope of adaptation, and thus more transformational adaptation. Capacity building, economic instruments, networks, and information provisioning approaches are more likely to target specific marginalized groups and thus equity challenges. There are many regions and sectors where certain tools are not widely documented (e.g. regulations and plans in Africa and Asia), representing a key research gap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

IEA Wind Task 49: Reference Site Conditions for Floating Wind Arrays

The commercial-scale deployment of floating offshore wind (FOW) projects is expected to take place in a diverse range of sites that may differ significantly from existing fixed-bottom projects. FOW farms are particularly sensitive to the water depth and the meteorological and oceanographic (metocean) and geotechnical conditions at the project site due to the wave-induced system motions and loads as well as the anchoring system constraints imposed by the seafloor conditions. Uncertainty around the site conditions will permeate through all aspects of project design, leading to suboptimal and overly conservative designs, increased costs, and adversely affected performance. As FOW expands into a global industry, metocean and geotechnical conditions will increasingly vary for projects located in different geographic regions or in far-from-shore, deep-water sites. This study represents the outputs of work package 1 of International Energy Agency Wind Task 49, which focuses on the integrated design of floating wind arrays. The primary goal of this study is to establish the type of parameters and constraints required to characterize FOW array reference sites; provide a realistic and publicly available set of reference site conditions to the FOW community as a baseline set of data for individual research projects; identify and categorize any critical gaps in the existing data or methodologies required to define reference site characteristics; and inform and support the design of reference FOW arrays. A building block concept was developed for synthesizing reference sites for the design of FOW arrays. The building blocks include three classes of site conditions focusing on the techno-economic design of FOW projects: metocean conditions, seabed conditions, and coastal infrastructure. All reference site data produced and collected in this study are publicly available.

17 WIND ENERGY↗
DOE OSTI2020

Genomic Characterization of Newly Completed Genomes of Botulinum Neurotoxin-Producing Species from Argentina, Australia, and Africa

Botulinum neurotoxin-producing clostridia are diverse in the types of toxins they produce as well as in their overall genomic composition. They are globally distributed, with prevalent species and toxin types found within distinct geographic regions, but related strains containing the same toxin types may also be located on distinct continents. The mechanisms behind the spread of these bacteria and the independent movements of their bont genes may be understood through examination of their genetic backgrounds. The generation of 15 complete genomic sequences from bacteria isolated in Argentina, Australia, and Africa allows for a thorough examination of genome features, including overall relationships, bont gene cluster locations and arrangements, and plasmid comparisons, in bacteria isolated from various areas in the southern hemisphere. Insights gained from these examinations provide an understanding of the mechanisms behind the independent movements of these elements among distinct species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Integrating crop growth models with remote sensing for predicting biomass yield of sorghum

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.

Yang, Kai-Wei↗
DOE OSTI2020

Linezolid-resistant (Tn 6246 :: fexB - poxtA ) Enterococcus faecium strains colonizing humans and bovines on different continents: similarity without epidemiological link

Abstract Objectives poxtA is the most recently described gene conferring acquired resistance to linezolid, a relevant antibiotic for treating enterococcal infections. We retrospectively screened for poxtA in diverse enterococci and aimed to characterize its genetic/genomic contexts. Methods poxtA was screened by PCR in 812 enterococci from 458 samples (hospitals/healthy humans/wastewater/animals/retail food) obtained in Portugal/Angola/Tunisia (1996–2019). Antimicrobial susceptibility testing was performed for 13 antibiotics (EUCAST/CLSI). poxtA stability (∼500 generations), transfer (filter mating), clonality (SmaI-PFGE) and location (S1-PFGE/hybridization) were tested. WGS (Illumina-HiSeq) was performed for clonal representatives. Results poxtA was detected in Enterococcus faecium from six samples (1.3%): a healthy human (rectal swab) in Porto, Portugal (ST32/2001); four farm cows (milk) in Mateur, Tunisia (ST1058/2015); and a hospitalized patient (faeces) in Matosinhos, Portugal (ST1058/2015). All expressed resistance to linezolid (MIC = 8 mg/L), chloramphenicol, tetracycline and erythromycin, with variable resistance to ciprofloxacin and streptomycin. ST1058-poxtA-carrying isolates from Tunisia and Portugal differed by two SNPs and had similar plasmid content. poxtA, located in an IS1216-flanked Tn6246-like element, co-hybridized with fexB on one or more plasmids per isolate (one to three plasmids of 30–100 kb), was stable after several generations and transferred only from ST1058. ST1058 strains carried resistance/virulence genes (Efmqnr/acm) possibly induced under selective quinolone treatment. Conclusions poxtA has been circulating in Portugal since at least 2001, corresponding to the oldest description worldwide to date. We also extend the reservoir of poxtA to bovines. The similar linezolid-resistant poxtA-carrying strains colonizing humans and livestock on different continents, and without a noticeable relationship, suggests a recent transmission event or convergent evolution of E. faecium populations in different hosts and geographic regions.

Freitas, Ana R.↗
DOE OSTI2021

NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.

Chowdhury, Shovan↗
DOE OSTI2024

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Probabilistic Sizing of Energy Storage Systems for Reliability and Frequency Security in Wind-Rich Power Grids

The penetration of wind energy has increased significantly in the power grid in recent times. Although wind is abundant, environment-friendly, and cheap, it is variable in nature and does not contribute to system inertia as much as conventional synchronous generators. Coupled with the low inertia contribution, the generation intermittency of wind power leads to reliability and stability issues in the power system. Energy storage systems (ESSs) are among the most prominent alternatives to alleviate these concerns associated with high wind penetration. This paper proposes a planning strategy to size ESS for the reliability and frequency security of wind-rich power grids. A probabilistic methodology for ESS sizing is developed utilizing a composite reliability-based framework with sequential Monte Carlo simulation (MCS). The MCS generates composite reliability indices for the power system, which are employed to obtain the capacity for a reliability energy storage system (RESS). Simultaneously, the MCS-derived probability of synchronization of conventional generators is integrated into an analytical approach for sizing a frequency support energy storage system (FESS). The effect of wind farm dispersion across geographical regions is incorporated in the framework to study possible reductions in the ESS size while maintaining the system reliability and frequency security. Furthermore, the efficacy of the proposed strategy is demonstrated on the RTS-GMLC test system.

25 ENERGY STORAGE↗
DOE OSTI2022

Land‐based climate solutions for the United States

Abstract Meeting end‐of‐century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature‐based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature‐based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the United States that illustrates the benefits of combining approaches. We assign a portion of non‐forested former cropland to bioenergy sufficient to meet projected mid‐century transportation needs, with the remainder assigned to nature‐based solutions such as reforestation. Bottom‐up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost‐effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of forest management. Results show a conservative end‐of‐century mitigation capacity of 110 (57–178) Gt CO 2 e for the U.S., ~50% higher than existing estimates that prioritize nature‐based or bioenergy solutions separately. Further research is needed to shrink uncertainties, but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now.

54 ENVIRONMENTAL SCIENCES↗