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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Nuclear Power's Future Role in a Decarbonized U.S. Electricity System

This study explores the potential future role of nuclear energy in a decarbonized U.S. electricity system through a multi-model comparison approach. We employ four state-of-the-art CEMs with native and harmonized input assumptions, layered with different policy and technology trajectories. Comparing outputs across models, technology assumptions, and policy scenarios informs model understanding, interpretation, and development decisions. Under current policies, models differ in their projections for nuclear retirements, but nuclear power plants consistently run with high capacity factors and new builds only occur in scenarios with very low nuclear costs. String power sector carbon policies drive models to align in keeping existing nuclear capacity and employing nuclear plant flexibility, but they may not be enough to bring new nuclear capacity online in the absence of significant cost declines. Therefore, significant economic deployment of new nuclear capacity requires both a stringent electric sector CO2 policy and very low cost assumptions for new nuclear. While these scenarios should not be interpreted as predictions, they are informative for understanding differing model assessments of the relative competitiveness of nuclear energy under a range of policy and technology conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum / Clostridium ljungdahlii Syntrophic Coculture

ABSTRACT Clostridium acetobutylicum and Clostridium ljungdahlii grown in a syntrophic culture were recently shown to fuse membranes and exchange cytosolic contents, yielding hybrid cells with significant shifts in gene expression and growth phenotypes. Here, we introduce a dynamic genome-scale metabolic modeling framework to explore how cell fusion alters the growth phenotype and panel of metabolites produced by this binary community. Computational results indicate C. ljungdahlii persists in the coculture through proteome exchange during fusing events, which endow C. ljungdahlii cells with expanded substrate utilization, and access to additional reducing equivalents from C. acetobutylicum -evolved H 2 and through acquisition of C. acetobutylicum -native cofactor-reducing enzymes. Simulations predict maximum theoretical ethanol and isopropanol yields that are increased by 0.64 mmol and 0.39 mmol per mmol hexose sugar consumed, respectively, during exponential growth when cell fusion is active. This modeling effort provides a mechanistic explanation for the metabolic outcome of cellular fusion and altered homeostasis achieved in this syntrophic clostridial community. IMPORTANCE Widespread cell fusion and protein exchange between microbial organisms as observed in synthetic C. acetobutylicum / C. ljungdahlii culture is a novel observation that has not been explored in silico . The mechanisms responsible for the observed cell fusion events in this culture are still unknown. In this work, we develop a modeling framework that captures the observed culture composition and metabolic phenotype, use it to offer a mechanistic explanation for how the culture achieves homeostasis, and identify C. ljungdahlii as primary beneficiary of fusion events. The implications for the events described in this study are far reaching, with potential to reshape our understanding of microbial community behavior synthetically and in nature.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Origin, Bulk Chemical Composition and Physical Structure of the Galilean Satellites of Jupiter: A Post-Galileo Analysis

The origin of Jupiter and the Galilean satellite system is examined in the light of the new data that has been obtained by the NASA Galileo Project. In particular, special attention is given to a theory of satellite origin which was put forward at the start of the Galileo Mission and on the basis of which several predictions have now been proven successful. These predictions concern the chemical composition of Jupiter's atmosphere and the physical structure of the satellites. According to the proposed theory of satellite origin, each of the Galilean satellites formed by chemical condensation and gravitational accumulation of solid grains within a concentric family of orbiting gas rings. These rings were cast off equatorially by the rotating proto-Jovian cloud (PJC which contracted gravitationally to form Jupiter some 4 1/2 billion years ago. The PJC formed from the gas and grains left over from the gas ring that had been shed at Jupiter's orbit by the contracting proto-solar cloud (PSC Supersonic turbulent convection provides the means for shedding discrete gas rings. The temperatures T (sub n) of the system of gas rings shed by the PSC and PJC vary with their respective mean orbital radii R (sub n) (n = 0, 1,2,...) according as T (sub n) proportional to R (sub n) (exp -0.9). If the planet Mercury condenses at 1640 K, so accounting for the high density of that planet via a process of chemical fractionation between iron and silicates, then T (sub n) at Jupiter's orbit is 158 K. Only 35% of the water vapour condenses out. Thus fractionation between rock and ice, together with an enhancement in the abundance of solids relative to gas which takes place through gravitational sedimentation of solids onto the mean orbit of the gas ring, ensures nearly equal proportions of rock and ice in each of Ganymede and Callisto. Io and Europa condense above the H20 ice point and consist solely of hydrated rock (h-rock). The Ganymedan condensate consists of h-rock and H20 ice. For Callisto, NH3 ice makes up -5% of the condensate mass next to h-rock (approximately 50%) and H2O ice (approximately 45%). Detailed thermal and structural models for each of Europa, Ganymede and Callisto are constructed on the basis of the above initial bulk chemical compositions. For Europa (E), a predicted 2-zone model consisting of a dehydrated rock core of mass 0.912 M (sub E) and a 150 km thick frozen mantle of salty H2O yields a moment-of-inertia coefficient which matches the Galileo Orbiter gravity measurement. For Ganymede (G), a 3-zone model possessing an inner core of solid FeS and mass approximately 0.116 M (sub G), and an outer H2O ice mantle of mass approximately 0.502 M (sub G) is needed to explain the gravity data. Ganymede's native magnetic field was formed by thermoremanent magnetization of Fe3O4. A new Callisto (C) model is proposed consisting of a core of mass 0.826 M (sub C) containing a uniform mixture of h-rock (60% by mass) and H2O and NH3 ices, and capped by a mantle of pure ice. This model may have the capacity to yield a thin layer of liquid NH3 (raised dot) 2H2O at the core boundary, in line with Galileo's discovery of an induced magnetic field.

Prentice, A. J. R.↗
NASA NTRS2007

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats.

Spruce, Joseph P.↗
DOE OSTI2023

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Igiugig Site Visit Report

The National Renewable Energy Laboratory (NREL) team conducted a site visit during January 23-25, 2019 with the Igiugig Village Council (IVC) and other stakeholders to assist the community of Igiugig in refining their long-term energy strategy. The agenda included a tour of the village, data collection, presentations, long-term visioning exercises, identification of energy scenarios, and a school presentation. Participants in the site visit activities spanned a range of local, regional, government, and industry participation. There were 24 attendees at the all-day community workshop on Thursday, January 24th, with participants from the IVC, NREL, Bristol Bay Native Association (BBNA), Bristol Bay Native Corporation (BBNC), Lake and Peninsula Borough, Alaska Energy Authority, Intergrid, ORPC, Deer Stone Consulting, the Southwest Alaska Municipal Conference, and University of Alaska Fairbanks - Alaska Center for Energy and Power. Igiugig is a well-organized rural Alaskan community, with a leadership team that appears to have broad community support. The use of consensus-based decision making is one tangible example of how the leadership team actively invokes and promotes community-focused thinking. The NREL team observed anecdotal evidence of how this approach seems to be strengthening the community: active participation in the visioning exercise, a student hosted fund-raiser dinner at the school, a clean and organized landfill, and well-maintained roads and buildings. Igiugig has wind, solar, and river hydrokinetic resources readily available within the community. Wind has shown to be a promising resource in the region, and has been integrated into microgrids around the state, but the community has had mixed success with wind technologies: some devices failed quickly and others continue to operate. The economics of solar energy are improving in Alaska, and economical solar projects are being installed around the state. Considering that economic activity in Igiugig peaks during the summer sport fishing season, solar could prove to be a valuable supplement to the electrical system. Igiugig has been a test site for two different river hydrokinetic devices, and they have began a third project to operate ORPC's RivGen device, which is delivering valuable device performance data, operations and maintenance experience, and design refinement information. Already, this device has made over 7-million revolutions, and delivered over 8 MWh of power to the community. Igiugig's river resource is fairly unique because it is available year-round and has the potential to provide reliable base-load power for months at a time. A preliminary investigation of this diverse resource mix suggests that Igiugig could achieve very high levels (70% or more) of annual renewable generation contributions. A critical step in pursuing this path is identifying the mix of energy assets (generation and storage) that best meets the community's budget, needs, and goals. The assets that a community installs early in their grid-modernization initiative can constrain the options that are economical at later stages, which may lead to sub-optimal solutions. This is where technical and economic analysis of potential scenarios (i.e., distinct mixes of energy assets) can be useful in identifying the most promising pathways so that a community can make informed and strategic decisions about the assets they install. These analyses are most accurate and informative when they are based on actual technology performance and cost data. As the RivGen project continues to operate and generate this data, we will be better prepared to evaluate the technology's long-term viability and to identify research areas that would improve it. Igiugig's energy projects are at the cutting edge of two intersecting technology areas: 1) deploying an operational river hydrokinetic turbine, and 2) integrating renewable energy sources to achieve very high percent renewables. If these projects are successful, the lessons learned and technologies developed could be valuable for other microgrids around the world. Igiugig's unique resource mix make it an ideal location for this work, and the community's organizational strength make it an ideal partner in this ground-breaking work. Ongoing support for technical assistance to manage and address technical challenges along the way will maximize the probability of project success.

13 HYDRO ENERGY↗
DOE OSTI2025

Standardising the “Gregory method” for calculating equilibrium climate sensitivity

The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO 2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO 2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO 2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.

Geosciences↗
DOE OSTI2022

Biotic predictors improve species distribution models for invasive plants in Western U.S. Forests at high but not low spatial resolutions

Invasions by non-native plants threaten forest health and sustainability. The ability to predict areas at greatest risk to invasion is essential for informing both monitoring and management of invasive species. Species distribution models (SDMs) are often used to identify environmental correlates of a species’ occurrence and predict geographic areas that may be suitable for its presence and are commonly constructed using solely abiotic predictors. However, mounting evidence implies that not including biotic predictors in SDMs may yield less accurate models at some resolutions typical of landscape-scale models, although this possibility has rarely been evaluated in invasive plants. In this study, we determined whether including descriptors of the biotic environment improved the accuracy of SDMs built at five decreasing spatial resolutions for infestations of five common invasive plants in forests of California, Oregon, and Washington, USA and described environmental correlates of each species’ presence. Predictors of occurrence often echoed those identified in previous studies of the focal species, indicating that our models accurately identified important environmental drivers of occurrence. Including biotic predictors in the SDMs consistently improved model accuracy only at the highest resolution we examined, which may be due to the spatial scale at which biotic interactions primarily act to constrain species’ distributions, the particular biotic predictors we used in our models, or correlations between attributes of the abiotic and biotic environment. This finding suggests that, while the practice of building SDMs using abiotic predictors alone may generally yield models whose accuracy does not differ substantially from those that also include biotic predictors, the effects of biotic interactions on the distribution of invasive plants in forests may be detectable at larger scales than previously thought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Elemental profiling and genomewide association studies reveal genomic variants modulating ionomic composition in Populus trichocarpa leaves

Samples were collected from a population of 1,089 black cottonwood genotypes (P. trichocarpa) assembled from native stands to encompass the central portion of the natural range of the species, stretching from 38.8° to 54.3° N Q13 latitude from California, USA, to British Columbia, Canada. Establishment of the common garden, growth conditions, and site maintenance have been described by Muchero et al (2015). In this study, leaf samples for ionomic profiling were collected from 4-year-old trees, during the growing season, in July 2012, from a field located in Clatskanie, Oregon, USA (46°6′11″N 123°12′13″W). The field site was located in a protected alluvial floodplain containing a uniform Wauna-Locoda silt loam soil area characterized by an acidic pH, in Columbia County, Oregon. A subset of 584 out of the 1,089 P. trichocarpa genotypes were represented in this sampling. These genotypes were randomly selected to represent the geographical distribution of the population. A single fully mature (LPI 7-9) leaf on the south side of the tree exposed to full sunlight conditions was removed from the tree within a 6-hour window centering on solar noon and immediately frozen under dry ice before processing. Leaf samples of 584 P. trichocarpa genotypes were finely ground to 40 mm particle size using a mortar and pestle, and ionomic composition was analyzed using ICP-MS. In total, 20 elements were profiled, including aluminum (Al27), arsenic (As75), boron (B11), cadmium (Cd111), calcium (Ca43), cobalt (Co), copper (Cu), iron (Fe57), magnesium (Mg25), manganese (Mn55), molybdenum (Mo), nickel (Ni60), phosphorus (P31), potassium (K39), rubidium (Rb85), selenium (Se82), sodium (Na23), strontium (Sr88), sulfur (S34), and zinc (Zn66), following a protocol established by Ziegler et al. (2013). For each sample, 75mg of powder was digested overnight in 2.5 mL HNO3 containing 20 parts per billion (ppb) indium as an internal standard, following the protocol described in Ziegler et al. (2013). Following a dilution, concentration of the 20 elements was measured using an Elan 6000 DRC-e mass spectrometer (Perkin-Elmer SCIEX) connected to a PFA microflow nebulizer (Elemental Scientific) and Apex HF desolvator (Elemental Scientific). One measurement per sample per genotype was done. For subsequent analyses, the quantifications were converted to total element concentration.

CBI ionomics, GWAS, plasma-mass spectrometry, neut↗