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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S.

Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.

NASA POWER↗
DOE OSTI2020

Chicago microclimate and building energy use data

These data comprise three elements: - High resolution, 90 m simulated weather data for 1 year at 15 min. intervals (with known gaps toward the end of each month). These files are in .csv format. - A mapping of individual buildings with individual IDs, their latitude/longitude location, and height. (Excel file) - Energy simulation output of these individual buildings, at 15 min. intervals for a whole year. (.json and other files)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Biodiesel Ethers: Fatty Acid-Derived Alkyl Ether Fuels as Improved Bioblendstocks for Mixing-Controlled Compression Ignition Engines

In the last 20 years, biodiesel consumption in the United States has swiftly increased to ~2 billion gallons per year as a renewable supplement to fossil fuel. However, further expansion of biodiesel use is currently limited in part by poor cold weather performance, which prevents year-round blending and necessitates blend walls ≤5% v/v. In order to provide a diesel fuel blendstock with improved cold weather performance (cloud point, pour point, and cold filter plug point), while at the same time maintaining other required fuel performance specifications, several biodiesel redox analogues were synthesized and tested. The best performing candidate fuels from this class showed improvement in the derived cetane number (29.3% shorter ignition delay), lower heating value (+4.7 MJ/kg), relative sooting tendency (–7.4 YSI/MJ), and cloud point (15 °C lower) when compared to a B100 biodiesel composed of an identical fatty acid profile. It was observed as a general trend that the reduced form of biodiesel, fatty alkyl ethers (FAEs), shows performance improvements in all fuel property metrics. The suite of improved properties provided by FAEs gives biodiesel producers the opportunity to diversify their portfolio of products derived from lipid and alcohol feedstocks to include long-chain alkyl ethers, a biodiesel alternative with particular applicability for winter weather conditions across the US.

09 BIOMASS FUELS↗
DOE OSTI2020

Mesoscale Modeling to Characterize Eagle Soaring Habitat

Uncovering drivers of risk is crucial to understanding interactions between wildlife and wind turbines, and identifying options for impact minimization. These drivers tie to co-variates linked to behavior and movement patterns that allow us to estimate locations and periods of risk. For volant species, atmospheric flow can have significant influence on flight patterns. For obligate soaring birds, like golden eagles, updraft velocities can inform where eagles are likely to travel, at what altitude, and where conditions are not likely sufficient to sustain soaring flight. This has been an active area of study in recent years, using relatively coarse atmospheric data generally at the 20km x 20km scale or larger. Leveraging a 20-year dataset from the Weather Research and Forecasting Model (WRF) (https://www.mmm.ucar.edu/weather-research-and-forecasting-model), we are quantifying vertical velocities across the continental United States at a 2km x 2km resolution. Wind resource data sets originally were static maps showing the mean annual wind speed over an area. However, for these data sets to be optimally used for various applications they must be high-resolution time series, seamlessly span large geographic contexts, and account for uncertainty in wind speed. The National Renewable Energy Laboratory is producing public available datasets that meet these criteria and will be bias corrected to yield the most accurate wind resource data. This effort is an augment to the current WIND Toolkit which houses a high resolution data set. In the new iteration of the WIND Toolkit, a 20-year dataset will be used to improve the accuracy and estimate uncertainty using ensemble and machine-learning techniques. The resulting product will be the most accurate dataset of its size and at a 2km x 2km spatial and 5-minute temporal resolution. Through this work, a mesoscale vertical velocity layer will be produced by calculating the likelihood of orographic updraft and thermal updraft conditions across the continental United States. Specifically, we will use WRF model output combined with digital elevation maps to predict updrafts and then determine if vertical velocities are sufficient to support Golden Eagle soaring and gliding. Ultimately this data layer will be made available as a GIS layer in the Wind Prospector (maps.nrel.gov/wind-prospector/) tool or a similar framework. Data that will be incorporated include wind speed, direction temperature, relative humidity, barometric pressure, air density, precipitation rate, solar radiation, atmospheric stability, skin temperature, and upward heat flux. These products will advance research on interactions between volant species and wind energy by providing open access to highly resolved data with uncertainty quantification not previously available at this scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Degradation-related defect level in weathered silicon heterojunction modules characterized by deep level transient spectroscopy

Commercial silicon heterojunction photovoltaic modules, known as amorphous-silicon-based heterojunction with intrinsic thin-film layer (HIT) modules, show average degradation after 10 years in the field. HIT modules weathered outdoors in Colorado and Florida display mostly uniform decreases in intensity when mapped with photoluminescence (PL) imaging compared to a control module. Flash-table-based current-voltage curves show that degradation is dominated by voltage loss. Samples are cored from each of the modules, and deep level transient spectroscopy (DLTS) detects three electron-trap defect states in all modules with activation energies of electron emission from the defects of 0.07, 0.16, and 0.50 eV. DLTS measurements on the weathered modules show an additional deep-level, electron-trap defect state with an activation energy of 0.51 eV and a trap density of approximately 10 12 cm –3 . The capture rate is measured using varying short filling pulse times, and the resulting capture cross section is estimated to be 1.1x10 –16 cm 2 . The development of the weathering-related defect level correlates to decreases in carrier lifetime, PL intensity, and module voltage. Various depths of the space charge region are probed with increments in applied reverse bias and filling-pulse bias. Furthermore, this DLTS depth profiling shows a trend of trap density increasing with less applied reverse bias, suggesting that the weathering-related defect increases carrier recombination toward the interface between the bulk silicon wafer and the junction-forming amorphous-silicon passivation layers.

14 SOLAR ENERGY↗
DOE OSTI2020

In silico assessment of the potential of basalt amendments to reduce N 2 O emissions from bioenergy crops

Abstract The potential of large‐scale deployment of basalt to reduce N 2 O emissions from cultivated soils may contribute to climate stabilization beyond the CO 2 ‐removal effect from enhanced weathering. We used 3 years of field observations from maize ( Zea mays ) and miscanthus ( Miscanthus × giganteus ) to improve the nitrogen (N) module of the DayCent model and evaluate the potential of basalt amendments to reduce N losses and increase yields from two bioenergy crops. We found 20%–60% improvement in our N 2 O flux estimates over previous model descriptions. Model results predict that the application of basalt would reduce N 2 O emissions by 16% in maize and 9% in miscanthus. Lower N 2 O emissions responded to increases in the N 2 :N 2 O ratio of denitrification with basalt‐induced increases in soil pH, with minor contributions from the impact of P additions (a minor component of some basalts) on N immobilization. The larger reduction of N 2 O emissions in maize than in miscanthus was likely explained by a synergistic effect between soil pH and N content, leading to a higher sensitivity of the N 2 :N 2 O ratio to changes in pH in heavily fertilized maize. Basalt amendments led to modest increases in modeled yields and the nitrogen use efficiency (i.e., fertilizer‐N recover in crop production) of maize but did not affect the productivity of miscanthus. However, enhanced soil P availability maintained the long‐term productivity of crops with high nutrient requirements. The alleviation of plant P limitation led to enhanced plant N uptake, thereby contributing to lower microbial N availability and N 2 O emissions from crops with high nutrient requirements. Our results from the improved model suggest that the large‐scale deployment of basalt, by reducing N 2 O fluxes of cropping systems, could contribute to the sustainable intensification of agriculture and enhance the climate mitigation potential of bioenergy with carbon capture and storage strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dial-A-Cluster User Manual

The Dial-A-Cluster (DAC) model allows interactive visualization of multivariate time series data. A multivariate time series dataset consists of an ensemble of data points, where each data point consists of a set of time series curves. The example of a DAC dataset used in this guide is a collection of 100 cities in the United States, where each city collects a year's worth of weather data, including daily temperature, humidity, and wind speed measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Concurrent Optimization of Capital Cost and Expected O&M

Concentrating solar power (CSP) technologies can utilize heat from concentrated sunlight from a field of tracking mirrors to generate electricity, reform fuel, provide process heat, or augment fossil plant heat sources. Electricity-generating power tower systems focus light from thousands of independent heliostats onto a thermal receiver, which uses the focused light to warm a heat transfer fluid (HTF), typically, a molten nitrate salt. The HTF is then sent to a power generation cycle or diverted into thermal energy storage (TES) for later use. Thermal storage is – in principle – a straightforward proposition. However, optimal utilization of a TES resource is complex and multi-faceted: thermal energy may be dispatched to produce electricity immediately upon first availability, or thermal energy may be reserved for next-day peak periods at risk of filling storage and dumping energy, or a portion of the thermal energy can be reserved to maintain equipment temperatures, reducing power cycle startup time, etc. Many possible dispatch permutations variously emphasize producing peak power, operating through transients, expediting daily startup, etc. The best operation strategy can change day-to-day throughout the year, depending on the weather and market pricing forecasts. The project we describe in this report develops a software package that allows users to explore design optimization, operations decisions, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The software we develop is the first comprehensive design tool of its kind to incorporate all of these aspects while being deployed as open source.

14 SOLAR ENERGY↗
DOE OSTI2024

Explained: Reliability of the Current Power Grid

Maintaining reliability of the bulk power system, which supplies and transmits electricity, is a constant focus of electric grid planners, operators, and regulators. Based on the standards set by power system reliability entities, the U.S. grid has been and continues to be very reliable. Over the past decade the average U.S. customer has only experienced about 15 minutes of outages per year due to supply limitations, with most of those outages occurring due to failures on the distribution system. However, there have been a few notable incidents in recent years tied to extreme weather events. The fact sheet provides and introduction to reliability of the bulk power system in the U.S.

power grid↗
DOE OSTI2024

Characteristics of Precipitation and Mesoscale Convective Systems Over the Peruvian Central Andes in Multi 5‐Year Convection‐Permitting Simulations

Abstract Using the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Climate change increases risk of extreme rainfall following wildfire in the western United States

Post-wildfire extreme rainfall events can have destructive impacts in the western United States. Using two climate model large ensembles, we assess the future risk of extreme fire weather events being followed by extreme rainfall in this region. By mid-21st century, in a high warming scenario (RCP8.5), we report large increases in the number of extreme fire weather events followed within 1 year by at least one extreme rainfall event. By 2100, the frequency of these compound events increases by 100% in California and 700% in the Pacific Northwest in the Community Earth System Model v1 Large Ensemble. We further project that more than 90% of extreme fire weather events in California, Colorado, and the Pacific Northwest will be followed by at least three spatially colocated extreme rainfall events within five years. Our results point to a future with substantially increased post-fire hydrologic risks across much of the western United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Compounding effects of Lake and urbanization on summer precipitation in the Greater Chicago area

Here, this study explores the impacts of Lake Michigan and Chicago's urbanization on precipitation patterns over the Greater Chicago Area, using 22 years of observational data and Weather Research and Forecasting (WRF) model simulations focused on an early summer rain event. Observational analysis reveals that urban areas consistently experience more precipitation than the adjacent southern Lake Michigan region throughout the year, particularly before 2015. However, this disparity has narrowed since 2016 due to a more rapid increase in heavy precipitation over the lake compared to urban areas. Specifically, lake precipitation has risen by 25 mm per year, compared to 15 mm per year over urban areas. Additionally, the number of days with precipitation exceeding 5 mm per day has been rising at a rate of 1.34 days per year over the lake and 0.84 days per year over urban areas. Modeling experiments reveal that both urbanization and lake effects, including lake breezes, enhance precipitation over urban areas, primarily through convergence induced by interactions between land and lake breezes. In contrast, these same factors suppress precipitation over the lake. The suppression results from Lake Michigan's stable environment, characterized by cooler surface temperatures, limited evaporation in early summer, and a high-pressure anomaly over the lake driven by urban heating, which creates upward motion over urban areas and downward motion over the lake, further influencing precipitation patterns.

Coastal urban↗
DOE OSTI2022

Practical and thermodynamic constraints on electromicrobially accelerated CO 2 mineralization

By the end of the century, tens of gigatonnes of CO 2 will need to be removed from the atmosphere every year to maintain global temperatures. Natural weathering of ultramafic rocks and subsequent mineralization reactions can convert CO 2 into ultra-stable carbonates. Although this will draw down all excess CO 2 , it will take thousands of years. CO 2 mineralization could be accelerated by weathering ultramafic rocks with biodegradable lixiviants. We show that if these lixiviants come from cellulosic biomass, this demand could monopolize the world’s biomass supply. We demonstrate that electromicrobial production technologies (EMP) that combine renewable electricity and microbial metabolism could produce lixiviants for as little as $\$200$ to $\$400$ per tonne at solar electricity prices achievable within the decade. We demonstrate that EMP could make enough lixiviants to sequester a tonne of CO 2 for less than $\$100$. This work highlights the potential of this approach and the need for extensive R&D.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Simulations suggest offshore wind farms modify low-level jets

Abstract. Offshore wind farms are scheduled to be constructed along the East Coast of the US in the coming years. Low-level jets (LLJs) – layers of relatively fast winds at low altitudes – also occur frequently in this region. Because LLJs provide considerable wind resources, it is important to understand how LLJs might change with turbine construction. LLJs also influence moisture and pollution transport; thus, the effects of wind farms on LLJs could also affect the region’s meteorology. In the absence of observations or significant wind farm construction as yet, we compare 1 year of simulations from the Weather Research and Forecasting (WRF) model with and without wind farms incorporated, focusing on locations chosen by their proximity to future wind development areas. We develop and present an algorithm to detect LLJs at each hour of the year at each of these locations. We validate the algorithm to the extent possible by comparing LLJs identified by lidar, constrained to the lowest 200 m, to WRF simulations of these very low LLJs (vLLJs). In the NOW-WAKES simulation data set, we find offshore LLJs in this region occur about 25 % of the time, most frequently at night, in the spring and summer months, in stably stratified conditions, and when a southwesterly wind is blowing. LLJ wind speed maxima range from 10 m s−1 to over 40 m s−1. The altitude of maximum wind speed, or the jet “nose”, is typically 300 m above the surface, above the height of most profiling lidars, although several hours of vLLJs occur in each month in the data set. The diurnal cycle for vLLJs is less pronounced than for all LLJs. Wind farms erode LLJs, as LLJs occur less frequently (19 %–20 % of hours) in the wind farm simulations than in the no-wind-farm (NWF) simulation (25 % of hours). When LLJs do occur in the simulation with wind farms, their noses are higher than in the NWF simulation: the LLJ nose has a mean altitude near 300 m for the NWF jets, but that nose height moves higher in the presence of wind farms, to a mean altitude near 400 m. Rotor region (30–250 m) wind veer is reduced across almost all months of the year in the wind farm simulations, while rotor region wind shear is similar in both simulations.

17 WIND ENERGY↗
DOE OSTI2021

Los Alamos Climatology 2021 Update

The Los Alamos National Laboratory (LANL or the Laboratory) operates a Meteorological Monitoring (Met) Program to support LANL emergency response, engineering designs, environmental compliance, environmental assessments, safety evaluations, weather forecasting, environmental monitoring, research programs, and environmental restoration. Weather data has been collected in Los Alamos since 1910. A 30-year averaging period is termed by the National Weather Service (NWS) as a climatic normal, and these statistics are updated every 10 years to represent the current climatic normal conditions. Previous climate statistics are provided in Bowen (1990) for the 1961–1990 averaging period and Dewart et al. (2017) for the 1981–2010 averaging period. No climate statistics were developed for the 1971-2000 averaging period. This report provides climate statistics for the current 30-year 1991–2020 averaging period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability"

The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗