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DOE OSTI2023

IACMI Project 4.7: Pultruded Textile Carbon Fiber for Spar Caps (Final Report)

The primary objective of this project was to demonstrate the potential to significantly reduce the cost of wind turbine blades with carbon fiber reinforced polymer (CFRP) structure. Applicability of textile carbon fibers (TCF) were evaluated for use in pultruded spar cap (SC) elements as a path to cost reduction for utility scale wind turbine blades. In earlier work for the Department of Energy (DOE) Wind Energy Technologies Office (WETO), a collaboration of Sandia National Laboratory (SNL), Oak Ridge National Laboratory (ORNL), and Montana State University has demonstrated potential for pultruded TCF to compete with infused fiberglass and commercially available carbon fiber pultruded sections for spar cap construction. In the design cases evaluated, the TCF sections fared well when compared on cost per unit composite stiffness and cost per unit composite compressive strength for those designs [1]. Both stiffness and compressive strength tend to be key factors in the design of blade composite Spar Cap which carry the bulk of the blade structural loads in bending. Spar Cap design tends to distribute largely symmetric tensile and compressive stresses to opposite sides of the spar structure, but since carbon fiber composite compressive strength is typically 20-50% lower than tensile strength, the compressive loading reaches failure levels well before the tensile loading. Stiffness is critical in containing the large tip deflection in high wind loading situations. However, materials and process development were very limited in the earlier study and the work in this project was expanded to make the comparative information more representative of what will be required in order to make further inroads towards implementation. Similar to that study, this project team confirmed that the primary materials of interest for pultruded spar cap elements should be thermoset (TS) resins reinforced by carbon fibers, utilizing as high a percentage of TCF as practical to benchmark cost and performance against commercial carbon fibers. To make the closest comparison possible and eliminate specific test article size, resin selection, and equipment/operational nuance effects, the team planned to pultrude sections with 100% commercially available carbon fiber (Panex 35 carbon fiber from Zoltek) as well as samples utilizing high fractions of TCF. The resin system chosen was based on formulations recommended by large wind industry supplier Hexion and consisted of Hexion resin RSL-4597, curing agent CCA-138, and internal mold release additive 117, along with common kaolin filler ASP400P from BASF. As commonly deployed in spar cap configurations, the team had a mold built to pultrude a rectangular spar cap element of 100mm width and 3mm thickness. The extremely limited number of samples produced for the earlier study were produced with a “generic” epoxy utilized for a variety of applications by the pultruder contracted to produce test articles for demonstration purposes. More importantly, those samples were produced at a fiber fraction only slightly over 50%. Based on feedback from our industrial advisory team for that project and strongly recommended by this project team, the consensus is that it is highly desirable to obtain fiber fractions of 65-68% for significant penetration in wind blade spar cap. Although this requirement has yet to be exhaustively confirmed in readily available information, this was established as a project goal and informally decided we needed to exceed 60% fiber fraction to gain serious industry consideration. Previous TCF pultrusion trials have been challenged by the lack of robust TCF packages, resulting in non-uniform tension across and between tows, as well as excess labor and waste for removal of interleaved paper. The non-uniform tension and associated intermingling of tows in textile acrylic fiber tows and associated difficulties created from broken filaments in carbon fiber conversion inhibit the ordered packing necessary to enhance fiber fraction elevation. (These “cross-overs” are not considered undesirable for textile applications and there is some sense that they might be advantageous for those applications). In addition to work that is ongoing at the acrylic fiber manufacturers to improve their formats, The Institute of Advanced Composites Manufacturing Innovation (IACMI) Project 6.12 (report PA16-0349-6.12-01) [2] has developed and demonstrated a more robust packaging and creeling approach that at least partially addresses these issues, thus improving control of the TCF feed into the pultrusion unit. It was hoped that these and other improvements currently being implemented would allow us to achieve fiber fractions at least approaching these fiber fraction targets. During this project, sections utilizing 100% commercially available carbon fiber reinforcement were produced as a baseline, as well as sections reinforced with about 94% TCF and the balance being commercially available fiber for comparison. The most important finding was that similar to results reported in the earlier WETO-funded project and results from tests of TCF reported at IACMI meetings, this work demonstrated that sections pultruded with TCF in an epoxy resin frequently utilized in actual spar cap production had stiffness and compressive strengths largely comparable to similar sections pultruded with a commercially available carbon fiber also frequently utilized in the wind industry. Although the amount of that data is limited, some of the tensile strength results were actually closer than would have been expected based on fiber strength results provided by the TCF and commercial fiber producers. The actual test data are reported and discussed in detail in Section 5. The pultruded sections dominated by TCF reinforcement were approximately 8-10% lower in fiber fraction than for the sections produced using commercial fiber alone, making direct comparison difficult. The COVID-19 project has provided significant insight into the current state-of-the-art with various TCF product forms. The data obtained in this project will guide the planned improvements at the precursor level, especially in attaining uniform tensioning and payout to facilitate enhanced fiber fractions and overall processability of the TCF composites. The project team is providing guidance to stakeholders concerning the attributes, needs, and potential demand for TCF in wind blade spar caps. Results achieved in this project are consistent with findings in the related work cited [1] and support this guidance and the high potential for this product type. TCF precursor-producing partners continue to express interest in enhancing their product forms and the team looks forward to working with these improved materials as they become available.

42 ENGINEERING↗
DOE OSTI2024

Advanced Permanent Magnet Generator Topologies Using Multimaterial Shape Optimization and 3D Printing: Preprint

A vast majority of utility-scale wind turbine generators in the United States are dependent on foreign- sourced rare-earth permanent magnets that are vulnerable to supply chain uncertainties. Many small wind original equipment manufacturers are motivated to pursue continuous improvements to the generator design to lower the material and production costs and improve performance by lowering cogging torque and increasing the efficiency. Traditional design and manufacturing offer limited opportunities. In this work, we demonstrate advanced design approaches for a 15-kW baseline wind turbine generator by making use of recent progress in three-dimensional (3D) printing of polymer- bonded magnets and, electrical and structural steel. We explore three methods of magnet parametrization using Bezier curves resulting in symmetric, asymmetric and multimaterial magnet designs. We employ a multiphysics approach combining parametric computer-aided design modeling, finite- element analysis and targeted sampling to identify novel designs with more opportunities for reducing rare-earth material, improving efficiency and minimizing cogging torque. The results show that asymmetric-pole design and multimaterial-pole designs offer a greater opportunity to minimize rare-earth magnet materials by up to 35% with similar performance as the baseline generator, suggesting newer opportunities with design freedom beyond traditional limits of symmetry and as allowed by 3D printing.

Bezier curves↗
DOE OSTI2023

Evolution of Scenario Generation Capabilities in the ExaSGD Project

High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.

economic dispatch↗
DOE OSTI2020

The Ubiquity of AGN Winds in Seyfert 1 Galaxies at Low Redshift

The optical spectra of 3896 Seyfert 1 (Sy1) galaxies detected with WISE at z < 0.4 were analyzed for evidence of outflows. In 37% of the Sy1s in our sample, the outflows appear as broad, blue-shifted, spectrally resolved components of the [O iii]λ5007 Å emission line, with a mean maximum velocity V {sub max} ∼ 1014 km s{sup −1} that is consistent with AGN winds. For each Sy1, we deduced the black hole (BH) mass, bolometric luminosity, Eddington ratio, and power-law index of the continuum, which we compared with the star formation rate (SFR) and host morphology. Having separated our sample in two spectroscopic subgroups—Sy1s with only broad Balmer lines (Sy1B) and those with both narrow and broad (Sy1N) lines—and distinguishing those that show as outflow (Sy1Bw and Sy1Nw), we report the following differences: (1) the BH mass is systematically higher and the power-law steeper in the Sy1B–Sy1Bw than in the Sy1N–Sy1Nw; (2) V {sub max} is higher in the Sy1Bw than in the Sy1Nw, correlated in both groups with the BH mass and bolometric luminosity; (3) the Eddington ratio and SFR are higher in the Sy1 with outflows; and (4) the specific star formation rates (sSFRs) of the Sy1s are normal for their morphology and mass, typical of early-type spiral galaxies in the green valley, far from the quenched regime. From these results, we conclude that AGN winds in Sy1s are triggered by higher accretion rates and probably radiatively launched, and there is no clear evidence of an effect on the star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Spatially resolved gas and stellar kinematics in compact starburst galaxies

The kinematics of galaxies provide valuable insights into their physics and assembly history. Kinematics are governed not only by the gravitational potential, but also by merger events and stellar feedback processes such as stellar winds and supernova explosions. We aim to identify what governs the kinematics in a sample of SDSS-selected nearby starburst galaxies, by obtaining spatially resolved measurements of the gas and stellar kinematics. We obtained near-infrared integral-field K-band spectroscopy with VLT/SINFONI for 15 compact starburst galaxies. We derived the integrated as well as spatially resolved stellar and gas kinematics. The stellar kinematics were derived from the CO absorption bands, and Paα and Brγ emission lines were used for the gas kinematics. Based on the integrated spectra, we find that the majority of galaxies have gas and stellar velocity dispersion that are comparable. A spatially resolved comparison shows that the six galaxies that deviate show evidence for a bulge or stellar feedback. Two galaxies are identified as mergers based on their double-peaked emission lines. In our sample, we find a negative correlation between the ratio of the rotational velocity over the velocity dispersion (v rot /σ) and the star formation rate surface density. We propose a scenario where the global kinematics of the galaxies are determined by gravitational instabilities that affect both the stars and gas. This process could be driven by mergers or accretion events. Effects of stellar feedback on the ionised gas are more localised and detected only in the spatially resolved analysis. The mass derived from the velocity dispersion provides a reliable mass even if the galaxy cannot be spatially resolved. The technique used in this paper is applicable to galaxies at low and high redshift with the next generation of infrared-focussed telescopes (JWST and ELT).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Going mobile to address emerging climate equity needs in the heterogeneous urban environment

We report the Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step towards this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods (IOPs).. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

stochprop Documentation (Release 1.0)

Simulations of infrasonic propagation in the atmosphere typically utilize a single atmospheric specification describing the acoustic sound speed, ambient winds, and density as a function of altitude. Due to the dynamic and sparsely sampled nature of the atmosphere, there is a notable amount of uncertainty in the atmospheric state at a given location and time so that a more robust analysis of infrasonic propagation requires inclusion of this uncertainty. This Python library, stochprop, has been implemented using methods developed jointly by infrasound scientists at Los Alamos National Laboratory (LANL) and the University of Mississippi’s National Center for Physical Acoustics (NCPA). This software library includes methods to quantify variability in the atmospheric state, identify typical seasonal variability in the atmospheric state and generate suites of representative atmospheric states during a given season, as well as perform uncertainty analysis on a specified atmospheric state given some level of uncertainty. These methods have been designed to interface between propagation modeling capabilities such as InfraGA/GeoAc and NCPAprop and signal analysis methods in the LANL InfraPy tool.

58 GEOSCIENCES↗
DOE OSTI2022

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗
DOE OSTI2020

Impacts of Hurricane Disturbance on Water Quality across the Aquatic Continuum of a Blackwater River to Estuary Complex

Hurricanes cause landscape-scale disturbances that affect biogeochemical cycling and water quality in coastal ecosystems. During Hurricane Irma’s passage through northern Florida, water movements driven by wind velocities up to 105 km h-1 caused a salinity peak in an estuary/blackwater river complex. Water quality was monitored across the 15 km site to detect the magnitude and duration of disturbance. Saline water intruded 15 km inland into a freshwater portion of the river that peaked at a salinity of 2 psu. Due to the volume of precipitation from the hurricane, significant runoff of freshwater and dissolved organic matter (DOM) caused a decrease in salinity, dissolved oxygen (DO), and Chlorophyll-a concentrations while increasing turbidity and fluorescent dissolved organic matter (fDOM). The disturbance caused rapid changes observed by in-situ water quality monitors over a 3-week period, but some effects persisted for longer periods as shown by 3-month weekly water sampling. This disturbance caused shifts in DOM loading, altered salinity dynamics, and reshaped landscapes due to wind and wave surge both in upland marsh and downstream estuary. Hurricane disturbance temporarily and abruptly alters the aquatic continuum, and observations of system response can help us understand the mechanisms associated with ecosystem resilience and recovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗
DOE OSTI2025

Metagenome-assembled genomes from Wind River Basin floodplain sediments Riverton, Wyoming site (June to October 2019)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken at three time points from June 12, 2019 to October 23,2019 at a location (PTT1) close to DOE Legacy Management well 855 at the Riverton, Wyoming floodplain site in the Wind River Basin (WRB). The groundwater at this site exhibits persistent U, Mo, and sulfate plumes and is one of the field sites in focus for the SLAC Groundwater Quality SFA program. Sediment samples were collected from 60 to 180 cm below surface every 30cm for microbial analyses through metagenomic sequencing. 15 metagenomes were sequenced through JGI and can be found under Gold sequencing project: Gs0131241. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 780 MAG fasta files and a csv file with quality, taxonomic classification (GTDB RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. A sample metadata file (samples.csv) that contains site information has also been included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Critical transverse compressive stresses of straight and bent CORC® wires with and without impregnation

CORC® wires are a promising superconductor for accelerator magnet applications. While their excellent uniaxial tensile properties have been well established, potential degradation under transverse compression remains a concern for accelerator magnets, in which transverse compression is a primary stress experienced by superconductors. To evaluate the critical transverse compressive stresses of rare-earth barium copper oxide conductors, we developed an experimental system that enables testing of samples both with and without impregnation in liquid nitrogen. Furthermore, because bending strain induced during coil winding may influence the critical compressive response of CORC® wires, the apparatus was also modified to allow testing under the bending condition. In this study wires were tested for five configurations: (1) straight, non-impregnated, (2) bent, non-impregnated, (3) straight, Stycast 2850 FT-impregnated, (4) bent, Stycast 2850 FT-impregnated, and (5) bent, paraffin wax-impregnated. The transverse pressures corresponding to 3% and 5% reductions in the critical current are reported. In conclusion, the effects of wire bending and impregnation on the critical transverse pressure are analyzed, and the implications for transverse stress levels in accelerator magnet conductors are discussed.

CORC® wire↗
DOE OSTI2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

17 WIND ENERGY↗
DOE OSTI2025

Metagenome-assembled genomes from Wind River Basin floodplain sediments Riverton, Wyoming site (May to September 2017)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken roughly every month in the period May 18 to September 13 in 2017 at a location (Pit2) close to DOE Legacy Management well 855 at the Riverton, Wyoming floodplain site in the Wind River Basin (WRB). The groundwater at this site exhibits persistent U, Mo, and sulfate plumes and is one of the field sites in focus for the SLAC Groundwater Quality SFA program. Cores were taken with a hand-auger and separated into 5-20 cm segments based on soil horizonation down to 150 cm depth below surface. Each segment was subsampled for microbial analyses. Corresponding 16S rRNA gene amplicon data is available at the NCBI Single Read Archive (SRA) Database BioProject ID PRJNA626616, and soil geochemistry data at doi:10.15485/1631972. 40 metagenomes were sequenced through JGI and can be found under Gold sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 6993 MAG fasta files and a csv file with quality, taxonomic classification (GTDB RS220), and metagenome accessions for MAGs generated from the Wind River Basin (WRB). This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗
DOE OSTI2023

Genetic identification of avian samples recovered from solar energy installations

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or “feather spots”—known to be of avian origin but not readily assignable to species via morphology—may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

14 SOLAR ENERGY↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Radiation Damage Mitigation in FeCrAl Alloy at Sub-Recrystallization Temperatures

Traditional defect recovery methods rely on high-temperature annealing, often exceeding 750 °C for FeCrAl. In this study, we introduce electron wind force (EWF)-assisted annealing as an alternative approach to mitigate irradiation-induced defects at significantly lower temperatures. FeCrAl samples irradiated with 5 MeV Zr 2+ ions at a dose of 10 14 cm −2 were annealed using EWF at 250 °C for 60 s. We demonstrate a remarkable transformation in the irradiated microstructure, where significant increases in kernel average misorientation (KAM) and low-angle grain boundaries (LAGBs) typically indicate heightened defect density; the use of EWF annealing reversed these effects. X-ray diffraction (XRD) confirmed these findings, showing substantial reductions in full width at half maximum (FWHM) values and a realignment of peak positions toward their original states, indicative of stress and defect recovery. To compare the effectiveness of EWF, we also conducted traditional thermal annealing at 250 °C for 7 h, which proved less effective in defect recovery as evidenced by less pronounced improvements in XRD FWHM values.

FeCrAl alloys↗