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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Grand Teton Ecological Forecasting: Assessing Forage Change and Winter Habitat Availability for Bighorn Sheep that Employ a High-Elevation Overwintering Strategy to Identify Areas for Intervention

Grand Teton National Park provides habitat for a small native population of approximately 125 bighorn sheep (Ovis canadensis). The reduction in population of this species is attributed to loss of low elevation habitat, changing local environmental and climatic conditions, and increased disturbance from backcountry recreation. In response to these changes, this population of sheep employs a unique high-elevation wintering strategy in which they amass large fat stores in the summer and expend as little energy as possible in the winter while foraging on high elevation wind-swept and snow-free areas. For this project, DEVELOP partnered with Grand Teton National Park and used NASA Earth observations including Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 5 Thematic Mapper (TM), and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data to assess habitat suitability. Landcover change analyzed between 1987 and 2020 indicated shifting trends in grass and forb cover and tree cover. These trends persisted in the 2031 prediction, with grass cover decreasing and tree cover increasing, which translates to a loss of overall favorable habitat for bighorn sheep. Snow cover analyzed between 2001 and 2020 indicated similar unfavorable trends for the sheep with a decrease in barren areas, important for winter foraging. Finally, habitat suitability was modeled for 2020 and predicted to 2031 to determine habitat gains and losses for this species across the landscape. Overall, the results predicted decreases in suitable habitat and indicated that while these sheep are highly adaptable, strategies to manage suitable bighorn habitat may need to be employed rapidly to effectively conserve this species.

Remote sensing↗
DOE OSTI2021

A decreasing carbon allocation to belowground autotrophic respiration in global forest ecosystems

Belowground autotrophic respiration (RAsoil) depends on carbohydrates from photosynthesis flowing to roots and rhizospheres, and is one of the most important but least understood components in forest carbon cycling. Carbon allocation plays an important role in forest carbon cycling and reflects forest adaptation to changing environmental conditions. However, carbon allocation to RAsoil has not been fully examined at the global scale. To fill this knowledge gap, first, the spatio-temporal patterns of RAsoil from 1981 to 2017 were predicted by a Random Forest (RF) algorithm using the most updated Global Soil Respiration Database (v5) with global environmental variables; second, carbon allocation from photosynthesis to RAsoil (CAB), was calculated as the ratio of RAsoil to gross primary production; and its temporal and spatial patterns were assessed in global forest ecosystems. . Globally, mean RAsoil from forests was 8.9 ± 0.08 Pg C yr-1 (mean ± standard deviation) from 1981 to 2017 with strong spatial variabilities. Temporally, RAsoil increased at a rate of 0.0059 Pg C yr-2, paralleling broader soil respiration changes and indicating increasing carbon respired by roots. Mean CAB was 0.243 ± 0.016 and decreased over time. The temporal trend of CAB varied greatly in space, reflecting uneven responses of CAB to environmental changes. This study is the first attempt to predict global CAB and analyze its temporal and spatial patterns. With the linkage of carbon use efficiency, the developed CAB offers an completely independent approach to quantify global aboveground autotropic respiration spatially and temporally, which could provide crucial insights into carbon flux partition and global carbon cycling under climate change.

Tang, Xiaolu↗
DOE OSTI2022

CCUS deployment under the U.S. 45Q tax credit and adaptation by other North American Governments: MARKAL modeling results

In 2018, U.S. Congress passed the Bipartisan Budget Act, known as 45Q [H.R. 3761], expanding the corporate income tax credit for carbon capture utilization and storage (CCUS). 45Q provides a performance-based tax credit for carbon capture projects of $30/ton of carbon dioxide (CO 2 ) (tCO 2 ) for anthropogenic CO 2 going to enhanced oil recovery (EOR), and $50/tCO 2 if going to straight storage. There are several conditions; for example, there is a 12-year time limit on tax credits for a new plant that commences construction before 2024 [H.R. 3761]. Here, this study aims to test the incremental impact of the 45Q tax credits on CCUS deployment, CO 2 -EOR, and power generation technological changes for North American (the United States, Canada, and Mexico) long-term energy system development. The scenario results show that offering a 12-year CO 2 storage subsidy provides the motivation needed for CCUS investment during the 12-year subsidy period, and the benefits of such investment can be sustained over the 40-year lifetime under CO 2 taxation. The modeling results show that carbon capture generation replaces uncontrolled fossil-fueled power, not new or existing renewables, so power generation and corresponding emission reductions from renewables remain unaffected by the availability of 45Q in the United States. Our scenario with CCUS technological learning indicates that CCUS could play a very important role under stringent environmental constraints. Thus, accelerated support and funding for the large-scale CCUS demonstrations is important for the execution of both short- and long-term climate mitigation goals. Given the significant role of the United States, Canada, and Mexico on the world energy system, our results represent an important contribution to the study of global energy trends.

42 ENGINEERING↗
DOE OSTI2021

Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs

Ecological theory is built on trade-offs, where trait differences among species evolved as adaptations to different environments. Trade-offs are often assumed to be bidirectional, where opposite ends of a gradient in trait values confer advantages in different environments. However, unidirectional benefits could be widespread if extreme trait values confer advantages at one end of an environmental gradient, whereas a wide range of trait values are equally beneficial at the other end. Here, we show that root traits explain species occurrences along broad gradients of temperature and water availability, but model predictions only resembled trade-offs in two out of 24 models. Forest species with low specific root length and high root tissue density (RTD) were more likely to occur in warm climates but species with high specific root length and low RTD were more likely to occur in cold climates. Unidirectional benefits were more prevalent than trade-offs: for example, species with large-diameter roots and high RTD were more commonly associated with dry climates, but species with the opposite trait values were not associated with wet climates. Directional selection for traits consistently occurred in cold or dry climates, whereas a diversity of root trait values were equally viable in warm or wet climates. Explicit integration of unidirectional benefits into ecological theory is needed to advance our understanding of the consequences of trait variation on species responses to environmental change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes data management of NASA remote sensing data for Northern Eurasia Earth Science Partnership Initiative (NEESPI). Many types of ground and integrative (e.g., satellite, GIs) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of Northern Eurasia cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential. The proposed project targets integration of remote sensing data from AVHRR, MODIS, and other NASA instruments on board US- satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEPNCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗
DOE OSTI2021

Halfway to Zero: Progress towards a Carbon-Free Power Sector

Sharply reducing carbon emissions is imperative to prevent the worst effects of climate change. Yet even in the power sector—often viewed as the lynchpin to economy-wide decarbonization, and where low-carbon solutions are increasingly plentiful and cost-effective—the pace and scale of the required transformation can be daunting. A review of historical trends, however, shows the progress the power sector has already made in reducing emissions. Fifteen years ago, many business-as-usual projections anticipated that annual carbon dioxide (CO 2 ) emissions from power supply in the United States would reach 3,000 million metric tons (MMT) in 2020. In fact, direct power-sector CO 2 emissions in 2020 were 1,450 MMT—roughly 50% below the earlier projections. By this metric, in only 15 years the country’s power sector has gone halfway to zero emissions. Other metrics also evolved differently than projected: total consumer electricity costs (i.e., bills) were 18% lower; costs to human health and the climate were 92% and 52% lower, respectively; and the number of jobs in electricity generation was 29% higher. Economic, technical, and policy factors contributed to this success, including sectoral changes, energy efficiency, wind and solar, continued operations of the nuclear fleet, and coal-to-gas fuel switching. This historical record demonstrates the ability of technological and policy changes to set the power sector on a dramatically different emissions trajectory. Past success, however, does not trivialize the challenges that remain for further decarbonization in the power sector and beyond. Nor does it offer a specific roadmap for how best to achieve additional power-sector emissions reductions. Numerous challenges confront a zero-emissions pathway, and future strategies will likely differ from those of the past. Many recent studies have assessed how to make further progress in decarbonizing the power sector on the pathway to decarbonizing the economy as a whole. We summarize the core results of those studies, but the primary goal of this report is to highlight the progress that has already been made in reducing power-sector emissions. As the country maps out a plan for further decarbonization, experience from the past 15 years offers two central lessons. First, policy and technology advancement are imperative to achieving significant emissions reductions. Second, our ability to predict the future is limited, and so it will be crucial to adapt as we gain policy experience and as technologies advance in unexpected ways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Household energy resilience in extreme weather events: An investigation of energy service importance, HVAC usage behaviors, and willingness to pay

Household energy resilience, particularly maintaining essential energy services within an acceptable and affordable level, is critical during extreme weather events (e.g., winter storms, heat waves) with power shortages or outages. However, the research on the importance and acceptable compromise levels of energy services, HVAC usage behavior changes, and willingness to pay (WTP) for energy services during disasters is limited. Therefore, this study investigates the importance and compromise of household energy services, HVAC usage behaviors, and WTP, as three key dimensions of household energy resilience, in both normal days and disaster scenarios through surveys (n=485). First, 12 common residential end-uses are grouped into high, mid, and low-priority services using hierarchical clustering, in which high-priority services (perceived as important and un-compromisable) include food cooking and storage (refrigerator and freezer) and thermal comfort (space heating and cooling), mid-priority services involve water heating and lighting, while low-priority services are mostly related to entertainment and housework (e.g., TV, laundry). Also, HVAC use patterns demonstrate that occupants become more energy conservative during disasters, e.g., reducing durations of running HVAC and adjusting temperature setpoints to save energy. In terms of energy cost, approximately 60% of respondents are willing to pay more to maintain energy services during disasters, although only 13% are prepared to pay over double the normal energy prices. The regression models further reveal that household energy resilience varies across different sociodemographic groups (e.g., age, income, family size) and is impacted by disaster experience and beliefs in clean energy and climate. In conclusion, these research findings provide important perspectives to understand household energy resilience and inform measures for disaster adaptation of households in extreme weather events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2022

Scientist-Stakeholder Relationships Drive Carbon Data Product Transfer Effectiveness within NASA Program

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA’s Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the PI, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As Federal, State, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

Monitoring↗
DOE OSTI2025

Elucidating the drought-responsive changes in Poplar cuticular waxes: A GWAS analysis of genes involved in fatty acid biosynthesis

Drought and episodic drought events are major impending impacts of climate change, limiting the productivity of plants and especially trees due to their inherent high transpiration rates. One common mechanism used by plants to cope with drought stress is to change the composition of their leaf cuticular waxes. Cuticular waxes are essential for controlling non-stomatal water loss and are typically composed of a homologous series of very-long-chain fatty acid-derived compounds, as well as flavonoids, tocopherols, triterpenoids, and phytosterols. In this study, we compared the cuticular waxes of 339 natural accessions of Populus trichocarpa (black cottonwood) grown under control and drought conditions in a common garden. A Genome-Wide Association Study (GWAS) was then used to identify candidate genes associated with cuticular wax biosynthesis and/or its regulation. Although no major differences were observed in total wax load when subject to drought conditions, the amounts of the individual wax constituents were indeed responsive to drought. Specifically, changes in alkenes, alcohols, esters, and aldehydes were evident, and suggest that they contribute to the drought response/tolerance in poplar. GWAS uncovered several genes linked to fatty acid biosynthesis, including CER1, CER3, CER4, FATB, FAB1, FAR3, FAR4, KCS, and a homolog of SOH1, as well as other candidate genes that may be involved in coordinating the drought responses in poplar trees. Our findings provide new evidence that genotype-specific shifts in wax composition, rather than total wax accumulation, contribute to drought adaptation in poplar. Additionally, we show that genetic variation in key wax biosynthetic genes drives cuticular wax plasticity in P. trichocarpa under drought, identifying putative molecular targets for improving stress resilience in trees. This study expands our understanding of the adaptative mechanisms of the cuticle and their potential for enhancing drought tolerance in poplar species.

Alkanes↗
NASA NTRS2022

Scientist-Stakeholder Relationships Drive Carbon Data Product Transfer Effectiveness within NASA Program

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA's Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the Principal Investigator, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As federal, state, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

Molly E Brown↗
NASA NTRS2000

System Concepts for the Advanced Post-TRMM Rainfall Profiling Radars

Global rainfall is the primary distributor of latent heat through atmospheric circulation. The recently launched Tropical Rainfall Measuring Mission satellite is dedicated to advance our understanding of tropical precipitation patterns and their implications on global climate and its change. The Precipitation Radar (PR) aboard the satellite is the first radar ever flown in space and has provided. exciting, new data on the 3-D rain structures for a variety of scientific uses. However, due to the limited mission lifetime and the dynamical nature of precipitation, the TRMM PR data acquired cannot address all the issues associated with precipitation, its related processes, and the long-term climate variability. In fact, a number of new post-TRMM mission concepts have emerged in response to the recent NASA's request for new ideas on Earth science missions at the post 2002 era. This paper will discuss the system concepts for two advanced, spaceborne rainfall profiling radars. In the first portion of this paper, we will present a system concept for a second-generation spaceborne precipitation radar for operations at the Low Earth Orbit (LEO). The key PR-2 electronics system will possess the following capabilities: (1) A 13.6/35 GHz dual frequency radar electronics that has Doppler and dual-polarization capabilities. (2) A large but light weight, dual-frequency, wide-swath scanning, deployable antenna. (3) Digital chirp generation and the corresponding on-board pulse compression scheme. This will allow a significant improvement on rain signal detection without using the traditional, high-peak-power transmitters and without sacrificing the range resolution. (4) Radar electronics and algorithm to adaptively scan the antenna so that more time can be spent to observe rain rather than clear air. and (5) Built-in flexibility on the radar parameters and timing control such that the same radar can be used by different future rain missions. This will help to reduce the overall instrument development costs. In the second portion of this paper, we will present a system concept for a geostationary rainfall monitoring radar for operations at the geosynchronous Earth Orbit (GEO). In particular, the science requirements, the observational strategy, the instrument design, and the required technologies will be discussed.

Im, Eastwood↗
DOE OSTI2025

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.

aerosol chemistry dynamics↗
DOE OSTI2025

Tropical intertidal microbiome response to the 2024 Marine Honour oil spill

Marine fuel oil (MFO) spills in tropical coastal environments are under-characterized despite increasing risk from maritime activities. Microbial and geochemical responses to the June 2024 Marine Honour MFO spill on Singapore's intertidal sediments were analyzed in real time over 185 days. Using metagenomics and hydrocarbon profiling, microbial community shifts and hydrocarbon degradation were quantified across visibly oiled (high-impact) and clean (low-impact) sites. Microbiomes at all sites adapted rapidly to the spill through increased diversity and abundance of genes encoding alkane and aromatic compound degradation, detoxification, and biosurfactant production. The dominant hydrocarbon-degrading bacteria differed markedly from those reported in other crude oil spills and in regions with different climates. Oil deposition intensity strongly influenced microbial succession and hydrocarbon-degrading gene profiles, and this reflected early toxicity constraints in heavily oiled areas. The persistence of hydrocarbon degradation genes beyond hydrocarbon detection in sediments suggested long-term functional priming may occur. The study provides novel genome-resolved insight into the microbial response to MFO pollution, advances understanding of marine environmental biodegradation, and provides urgently needed baseline data for oil spill response strategies in Southeast Asia and beyond.

Coastal pollution↗
DOE OSTI2022

Observing, Measuring, and Assessing the Consequences of Snow Drought

Warmer and shorter winters from climate change will reduce snowpacks in most seasonally snow-covered regions of the world, with consequences for freshwater availability in spring and summer when people and ecosystems demand water most. Recent record-low snowpacks, such as those in the winters of 2013/14 and 2014/15 in the western United States, have led to a surge in research on “snow droughts,” which are pointed to as harbingers of global warming that pose significant societal hazards. Yet, despite the importance of understanding snow droughts to best prepare for their attendant impacts, the concept remains amorphous, with no agreed-upon definition of what they are, how best to measure them, and how such snow droughts connect to warm-season impacts. These knowledge gaps limit our understanding of the risks posed by snow droughts in the present and future, and thus our preparedness for their differential impacts on freshwater resources. To address these issues, we compile a hemispheric ensemble of in situ, satellite, and reanalysis snowpack datasets. We use this ensemble to evaluate the scientific challenges and uncertainties arising from differences in defining and measuring snow droughts, and to identify opportunities to leverage this information to better understand the significance of snow droughts. We show that a clearer quantification of what constitutes a snow drought, including its uncertainties, improves our ability to anticipate costly and disruptive warm-season droughts, which is vital for informing risk management and adaptation to changing snow regimes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Evaluation of Dynamic Coastal Response to Sea-level Rise Modifies Inundation Likelihood

Sea-level rise (SLR) poses a range of threats to natural and built environments, making assessments of SLR-induced hazards essential for informed decision making. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30m resolution predictions for more than 38,000 sq km of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Climate-change impacts↗
DOE OSTI2023

Developing Non-Food Grade Brassica Biofuel Feedstock Cultivars with High Yield, Oil Content, and Oil Quality that are Suitable for Low Input Production Dryland Systems (Final Report)

The U.S. uses a substantial amount of fossil fuel as an energy source for a wide range for functions including home heating, agriculture and transportation. In the transportation sector, diesel and jet fuel are consumed at a rapid rate, and alternative liquid energy is being investigated globally and nationally to reduce our dependence on fossil fuel and reduce the impact of our carbon footprint on global climate change. Non-food Brassica crops have the potential of producing high oil yield (over 250 gal acre-1) and have oil quality highly desirable for use as biodiesel or bio jet fuel. Developing oilseed feedstock Brassica cultivars with higher seed and oil yield, with high oil quality and with resistance to pathogens, that can be grown with few chemical inputs will helping break our dependence on fossil fuels and reduce importation of fossil fuels. While some oilseed Brassicas have been grown on a small scale for many years in the Pacific Northwest (PNW), adoption has been limited, and the potential of the crops have not been realized or even fully investigated. This report summarizes the results of a study to develop superior non-food grade winter (B. napus) and spring (B. napus and B. juncea) oilseed cultivars suitable for a range of PNW, and other US environments with high resistance to the biotic and abiotic stresses suitable for high-quality biofuel feedstocks. In conducting this work, genome-wide association selection was used to dissect the genetic architecture of industrial Brassica oilseed germplasm for yield, quality, and resistance to biotic and abiotic stresses. A genome-wide bioinformatics approach was used to identify putative PRR (pattern recognition receptor) - type resistance genes that confer durable resistance to blackleg. A novel transgenic approach was developed to generate resistant non-food oilseed lines using PPR genes Br1033 and Br8486. These genes were inserted into regionally adapted oilseed cultivars.

09 BIOMASS FUELS↗
DOE OSTI2025

Simulating regional workforce impacts of decarbonizing integrated steelmaking

Global efforts to mitigate climate change are increasing pressure on heavy manufacturing industries to decarbonize production. The iron and steel industry is responsible for 7% of CO 2 emissions globally (2% in the United States) and is often a major employer in the regions where iron and steel is produced. Understanding the future prospects for workers in regions with high CO 2 emitting industries—including impacts of phasing out or evolving such industries—will be critical for informing regional economic and clean energy strategies. We simulate the impact of an “in-place” transition that replaces today’s integrated production with direct reduced iron (DRI) used in electric arc furnaces (EAFs), using Southwest Pennsylvania as an application of our generalizable approach. Our results suggest that the integrated steelmaking workforce today has the skills, knowledge, and abilities (SKAs) to fill over 95% of all jobs required by DRI/EAF facilities, but the number of jobs is only 25% of those at integrated plants. We also find that some occupational groups have greater general transferability into the broader job market, while other groups, such as production workers, are ill-equipped today based on current SKAs to transition out of the iron and steel industry. Our methodology further suggests factors that limit transitions: Around 85% of occupations are more limited by missing skills, while 15% are more limited by insufficient wages. These results may help to improve the design of social policy and the targeting of retraining programs, while the simulation approach can be readily adapted for other regions and industries.

decarbonization↗
DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗