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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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A novel approach for large-scale wind energy potential assessment
Increasing wind energy generation is central to grid decarbonization, yet methods to estimate wind energy potential are not standardized, leading to inconsistencies and even skewed results. This study aims to improve the fidelity of wind energy potential estimates through an approach that integrates geospatial analysis and machine learning (i.e., Gaussian process regression). We demonstrate this approach to assess the spatial distribution of wind energy capacity potential in the Contiguous United States (CONUS). We find that the capacity-based power density ranges from 1.70 MW/km2 (25th percentile) to 3.88 MW/km2 (75th percentile) for existing wind farms in the CONUS. The value is lower in agricultural areas (2.73 ± 0.02 MW/km2, mean ± 95 % confidence interval) and higher in other land cover types (3.30 ± 0.03 MW/km2). Notably, advancements in turbine manufacturing could reduce power density in areas with lower wind speeds by adopting low specific-power turbines, but improve power density in areas with higher wind speeds (>8.35 m/s at 120m above the ground), highlighting opportunities for repowering existing wind farms. Wind energy potential is shaped by wind resource quality and is regionally characterized by land cover and physical conditions, revealing significant capacity potential in the Great Plains and Upper Texas. The results indicate that areas previously identified as hot spots using existing approaches (e.g., the west of the Rocky Mountains) may have a limited capacity potential due to low wind resource quality. Improvements in methodology and capacity potential estimates in this study could serve as a new basis for future energy systems analysis and planning.
Modelling a countercurrent liquid centrifuge for large-scale isotope separation
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PowerSystems.jl — A power system data management package for large scale modeling
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Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation
This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.
Estimating environmental impacts of large-scale transportation networks with vehicle registration data
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Leveraging large-scale biobanks for therapeutic target discovery
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Large-Scale Carbon Removal Will Create Public Health, Economic, and Climate Tradeoffs
Economy-wide efforts to achieve net-zero emissions offer both climate and air quality-related public health benefits from reducing fossil fuel combustion. We explore the expected costs and benefits if carbon dioxide removal (CDR) is deployed at scale in support of these efforts. Six leading forms of CDR at two levels of deployment are compared to a scenario with no U.S. climate action. We find that heavy reliance on CDR avoids a $2.5-5.8 trillion USD2020 in climate damages, provides $2.8-6.5 trillion USD2020 public health benefits, returns $5-6 trillion USD2020 in CDR revenues, but requires $11-13 trillion USD2020 CO2 mitigation cost, cumulatively by 2050 in the U.S. In contrast, lower reliance on CDR requires much deeper near-term fossil-fuel reductions, which increases mitigation costs by 52% but also creates 26% higher public health benefits from reductions in particulate matter- and ozone-related mortality and morbidity, preventing about 12,600 premature deaths by mid-century in the U.S.
Incorporation of Oxygen Carrier Recycle into Large-Scale Production of Cu-Based Oxygen Carriers
One of the greatest challenges in the chemical looping combustion (CLC) of solid fuels is developing an oxygen carrier material that is reactive and attrition resistant and can be prepared at a reasonable cost. Recent efforts in oxygen carrier development have followed two primary approaches: (1) using natural ores, such as ilmenite, or (2) developing highly attrition-resistant and reactive synthetic materials. Both approaches have shortcomings, namely, the low reactivity and incompatibility of ores with solid fuel CLC and the high cost and low durability of synthetic materials. Here, a different approach is taken where attrition is assumed inevitable and the recycling of spent oxygen carrier materials is incorporated into oxygen carrier manufacture. For solid fuel CLC, Cu-based oxygen carriers are attrited and are collected with fly ash. Copper oxides are more reactive with nitric acid than most ash materials, meaning that a copper-nitrate-rich leachate can be generated. This copper nitrate stream could then be reused in oxygen carrier synthesis by impregnation. For proof of concept, leaching experiments were conducted to verify that copper oxides are selectively leached from ash-containing spent oxygen carriers. Several cases for process design are proposed based on the composition of spent materials, as the degree of copper oxidation and type of solid fuel dictate leaching residence times and general processing intensity. The four stages proposed here include impurity removal, copper leaching and recovery, solid–liquid separation, and evaporation/concentrating. The resulting process should be able to recover up to 95% of copper while minimizing inclusion of undesirable ash-based impurities.
Large-Scale Synthesis and Comprehensive Structure Study of δ‑MnO2
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Chemical Descriptors for a Large-Scale Study on Drop-Weight Impact Sensitivity of High Explosives
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Velocity-Gauge Real-Time Time-Dependent Density Functional Tight-Binding for Large-Scale Condensed Matter Systems
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