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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Nocturnal flight activity of northern gannets Morus bassanus and implications for modelling collision risk at offshore wind farms

Assessing the potential impacts of proposed offshore wind farm developments on seabird populations requires estimation of nocturnal flight activity of seabirds for input into collision risk models. One of the seabirds considered most at risk from collision with offshore wind turbines is the northern gannet Morus bassanus. The recommended correction for gannet nocturnal flight activity is currently a highly precautionary value. Here we use data from tracking studies to derive evidence-based correction factors for nocturnal flight activity of adult gannets during the breeding and nonbreeding seasons, and of immature gannets during the summer prospecting phase. Flight and diving activity of gannets was minimal during the night, astronomical and nautical twilight, for adults during the breeding season and nonbreeding season, and for immatures. Some flight activity occurred during the short period of civil twilight, but on average at about half the level seen during the day. Based on evidence from numerous tracking studies, we recommend that precautionary values of the nocturnal (sunset to sunrise) flight activity factor for estimating collision risk should be 8% of daytime flight activity during the breeding season and 3% of daytime flight activity during the nonbreeding season. Use of these evidence-based correction factors will improve the accuracy, and reduce the uncertainty of collision risk models, providing a more reliable assessment of the impacts of offshore wind farms on gannets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Measuring Rotation and Manure Effects in an Iowa Farm Soil Health Assessment

Core Ideas Farms with known management histories contribute to soil health research. Rotation‐based systems may increase soil organic carbon when combined with appropriate tillage. Inherent soil properties must be considered in soil health assessments. Rotation effects were more noticeable than manure effects on soil health groupings. Data from on‐farm sites with known management histories are needed to quantify soil biological, chemical, and physical properties influencing carbon stocks and soil health. Surface (0–15 cm) and deep core (0–122 cm) soil samples were collected from fields under two rotations in Boone County, IA. The first was a 5‐yr corn [ Zea mays (L.)], soybean [ Glycine max (L.) Merr.], corn, oat [ Avena sativa (L.)], and alfalfa [ Medicago sativa (L.)] rotation to which 0, 18, or 36 Mg ha −1 of a manure/biosolids mixture was applied prior to planting corn. The second was an 8‐yr rotation with 6 yr of mixed grass and legume pasture followed by corn and an oat crop within which the pasture mixture was reestablished. Soil samples were collected evenly across the predominant soil map units (SMUs). Bulk density (BD), soil organic C (SOC), water‐stable aggregates (WSA), microbial biomass carbon (MBC), pH, Mehlich‐3 and diethylenetriaminepentaacetic acid (DTPA) extractable nutrients, electrical conductivity (EC), and nitrogen (total‐, NH 4 –, and NO 3 –N) were measured. Surface SOC data were consistent with Soil Survey values for the various SMUs. Crop rotation effects were more noticeable than manure/biosolid application rate effects. Data from this study were combined with previously published SOC data in Iowa. Results suggest extended rotation systems or those with cover crops may increase SOC 8 ± 4 g kg −1 compared to corn–soybean rotations (33 vs. 25 g kg −1 ). This study provides on‐farm reference values for soil health assessment tools and draws attention to the importance of inherent soil properties for these assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Session 3: Agrivoltaics Pathways [Slides]

This presentation was developed for a webinar series for the USAID's South Asia Group for Energy (SAGE) and South Asia Regional Energy Partnership (SAREP). The presentation includes insights into agrivoltaic site assessment planning, including what is needed for a farm assessment, solar panel system design from the agricultural perspective, variables that go into crop selection, and environmental impact, sustainability, and agritourism, technical parameters for developing an agrivoltaics project, including what makes up a PV feasibility study, understanding variables that go into technology and equipment selection, agrivoltaic installation and agricultural integration, and monitoring and maintenance throughout the life of the project, and agrivoltaic financial planning, risk mitigation, and debt equity issues.

14 SOLAR ENERGY↗
DOE OSTI2017

A comparison of methods for assessing power output in non-uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. In conclusion, we show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.

17 WIND ENERGY↗
DOE OSTI2016

A framework for guiding sustainability assessment and on-farm strategic decision making

Responding to future challenges and societal needs, various actions are taken in agriculture to evolve towards more sustainable farming practices. These actions imply strategic choices and suppose adequate sustainability assessments to identify, measure, evaluate and communicate sustainable development. However, literature is scarce on the link between strategic decision making and sustainability assessment. As questions emerge on how, what and when to measure, the objective of this paper is to construct a framework for guiding sustainability assessment and on-farm strategic decision making. Qualitative research on own experiences from the past and a recent project revealed four categories of actual needs farmers, advisors and experts have regarding sustainability assessment: context, flexibility, focus on farm and farmer and communication. These stakeholders' needs are then incorporated into a two-dimensional framework that marries the intrinsic complexity of sustainability assessment tools and the time frame of strategic decision making. The framework allows a farm-specific and flexible approach leading to harmonized actions towards sustainable farming. As this framework is mainly a procedural instrument to guide the use of sustainability assessment tools within strategic decision making, it fits to incorporate, even guide, future research on sustainability assessment tools themselves and on their adoption on farms. - Highlights: • How to link sustainability assessment and on-farm strategic decision making is unclear. • Two-dimensional framework incorporating stakeholders' needs regarding sustainability assessment • Linking complexity of sustainability assessment tools and the time frame of strategic decision making • Farm-specific and flexible approach to harmonize action towards sustainable farming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Hanford Tank Farm Occupational Exposure and Risk Assessment Plan: Health Process Project (Rev.1)

The overall objective of the Hanford Tank Farm Occupational Exposure and Risk Assessment Plan (hereafter referred to as the “Assessment Plan”) is to provide the Tank Operations Contractor (TOC), Washington River Protection Solutions (WRPS), and the U.S. Department of Energy (DOE) with a plan for developing a transparent, peer-reviewed process for assessing potential health risks associated with worker exposures to chemical emissions from the Hanford tank farms. Revision 1 (2018) incorporates changes to the original draft plan prepared in 2016 to reflect updated strategies based on initial implementation of the recommended actions.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2019

Impact of GSA2018 Flow Update, Alternative Dispersion Model, and Increased Grid Resolution on Tank Closure Performance Assessments

The F-Area Tank Farm (FTF) and H-Area Tank Farm (HTF) Performance Assessments (PAs) involve simulations of radionuclide and chemical waste migration through groundwater to exposure points. These solute transport simulations are based on a circa 2004 “GSA/PORFLOW” flow model [WSRC-TR-2004-00106]. The GSA/PORFLOW model was significantly updated in 2016-2017 [SRNL-STI-2017-00008 Rev. 1]. Impacts of the “GSA2016” flow model on the F- and H-Tank Farm PAs were assessed in SRNL-STI-2017-00445, Impacts of Updated GSA Groundwater Flow Models on the FTF, HTF and SDF PAs. Since then (2018-2019) further revisions resulted in a “GSA2018” flow model [SRNL-STI-2018-00643], which has been adopted for the 2019 Saltstone PA revision [SRR-CWDA-2019-00001 Revision A]. This study assesses the impacts of the GSA2018 flow model on the FTF and HTF PAs. Also assessed are the impacts of an alternative plume dispersion model and increased grid resolution that have been incorporated in the 2019 Saltstone PA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Collaborative TPL Assessment of Wave Energy Converters and Farms

The Technology Performance Level (TPL) assessment is a holistic methodology to assess a wave energy converter (WEC) technology’s ability to achieve [continental grid] market competitiveness and acceptability via criteria-based consideration of key cost, performance, environmental, safety, and societal drivers. The TPL assessment can be applied at all technology development stages and associated technology readiness levels (TRLs).

Technology Performance Level↗
DOE OSTI2025

Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering

Wake steering strategies are employed to increase the overall power production of wind farms by deflecting wakes of upstream turbines away from downstream ones. The gain in net power comes at the expense of increased fatigue loads experienced by downstream turbines. In this work we investigate performance and fatigue loading characteristics of a small farm consisting of five aligned International Energy Agency Wind Technology Collaboration Programme 15 MW wind turbines. A parametric study is performed where, for every wind direction from −20 to 20°, the yaw misalignment angle varies from −25 to 25°. This setup allows us to investigate asymmetries and identify optimal conditions for a given wind direction. In general, we find that positive yaw configurations are preferred and that yaw configurations that result in attractive power differences when compared to a baseline no-yaw scenario (25 %) come with significant increase in fatigue loading (we use the standard deviation and damage-equivalent load (DEL) of the blade-root, low-speed shaft, and tower-base moments as proxies for fatigue load). We find that for any given positive wind inflow angle, yaw angles between −2.5 and 15° yield power differences of 10 %–20 % over a no-yaw baseline, and positive yaw is preferred because of lower fatigue loading. For any given negative wind inflow angles, positive yaw also results in lower magnitudes of standard deviation and DEL for the channels investigated. A small power loss of up to 2 % is observed for some positive yaw angles under negative wind directions (as compared to symmetric negative yaw and positive wind cases), but improvements in terms of loads exceed 25 % and may be enough to justify a positive yaw configuration under negative winds as well. We show that such behavior can be explained by partial waking and the direction of the rotation of the rotor.

17 WIND ENERGY↗
DOE OSTI2017

Scaled Wind Farm Technology (SWiFT) Facility (Safety Assessment, Rev. 0)

This Safety Assessment (SA) documents the hazard analysis conducted for the Scaled Wind Farm Technology (SWiFT) Facility. The Sandia National Laboratories (SNL) Authorization Basis process requires Safety Assessment documents for all moderate-hazard industrial facilities. Together with the Primary Hazard Screening (PHS) Document [SNL11A00204), the SA documents the SWiFT safety basis, which is defined as the safety analysis and hazard controls that provide reasonable assurance that a DOE facility can be operated in a manner that adequately protects the workers, collocated/onsite workers, the public, and the environment. The SA specifically addresses the potential impact of hazards on the worker, collocated worker, and public.

17 WIND ENERGY↗
DOE OSTI2020

Conducting Industrial Hygiene Exposure Assessments at Scale Using Non-Parametric Upper Tolerance Limits at the Hanford Site - 20439

Chemical vapors are generated in the radioactive waste storage tanks at the Hanford Site due to ongoing chemical and radiological activity. Hanford's Tank Operations Contractor conducts exposure assessments for individual tank farms to identify, evaluate, and recommend controls or other worker protection measures for chemical, physical, and biological hazards present within each tank farm. These exposure assessments conform to the American Industrial Hygiene Association assessment strategy and recommended statistical procedures. The exposure assessment process involved manually processing tens of thousands of records that were collected over several years and entered into a central database. Working with Pacific Northwest National Laboratory, this process has been largely automated, providing a scalable analysis process that reduced processing time by over 90%, while providing defensible results and improved data quality. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

The Performance of a Spectral Wave Model at Predicting Wave Farm Impacts

For renewable ocean wave energy to support global energy demands, wave energy converters (WECs) will likely be deployed in large numbers (farms), which will necessarily change the nearshore environment. Wave farm induced changes can be both helpful (e.g., beneficial habitat and coastal protection) and potentially harmful (e.g., degraded habitat, recreational, and commercial use) to existing users of the coastal environment. It is essential to estimate this impact through modeling prior to the development of a farm, and to that end, many researchers have used spectral wave models, such as Simulating WAves Nearshore (SWAN), to assess wave farm impacts. However, the validity of the approaches used within SWAN have not been thoroughly verified or validated. Herein, a version of SWAN, called Sandia National Laboratories (SNL)-SWAN, which has a specialized WEC implementation, is verified by comparing its wave field outputs to those of linear wave interaction theory (LWIT), where LWIT is theoretically more appropriate for modeling wave-body interactions and wave field effects. The focus is on medium-sized arrays of 27 WECs, wave periods, and directional spreading representative of likely conditions, as well as the impact on the nearshore. A quantitative metric, the Mean Squared Skill Score, is used. Results show that the performance of SNL-SWAN as compared to LWIT is “Good” to “Excellent”.

environmental impacts↗
DOE OSTI2020

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

17 WIND ENERGY↗
DOE OSTI2022

Environmental Effects Assessment for Proposed Offshore Wind Farm off the Coast of Grays Harbor, Washington

Grays Harbor Wind LLC (GHW) is proposing to develop a floating offshore wind farm offshore of west Grays Harbor County, Washington (Grays Harbor). The proposed GHW Offshore Wind Project (Project) would entail construction, installation and operation of a 1,000-megawatt (MW) offshore wind farm consisting of approximately 75 floating units, each containing a floating foundation and wind turbine generator (WTG). The Project location is approximately 25 miles (21.7 nautical miles [nmi]) offshore west of Grays Harbor, at waters depths of 360 to 700 feet. The Pacific Northwest National Laboratory (PNNL) was contracted by Herrera Environmental Consultants, Inc. on behalf of GHW to carry out this preliminary scoping study to evaluate baseline conditions and potential effects on fish and marine mammals from development and operation of a floating offshore wind farm installed within a designated area off the coast of Washington1. Floating offshore wind units installed in an ocean environment as part of the Project would interact with marine wildlife. This Study report provides an initial data aggregation and analysis, using publicly available data, of the Project effects, both negative and positive, on the marine environment. The scope of this assessment is limited by the fact that the Project development is presently at the conceptual level. Data on marine organisms were aggregated and evaluated; however seabirds were evaluated by Herrera Environmental Consulting and, along with bats, were not included in the scope of this study. Significant additional work is necessary to characterize ocean, seafloor, and environmental conditions; select appropriate floating offshore wind technologies; identify construction methods and locations; and assess facility locations, including electrical interconnection. Data aggregation and analysis of seabirds and bats is also needed. Evaluation of the full range of potential environmental effects would be conducted following an award of a lease from the Bureau of Ocean Energy Management (BOEM) as part of the leasing, National Environmental Policy Act (NEPA)/State Environmental Policy Act (SEPA) environmental review and permitting processes. While this initial data aggregation and analysis uses best available public scientific information and current assumptions about the Project configuration, the effects discussed herein are based on the status of review to-date and may change as Project-specific details are developed.

17 WIND ENERGY↗
DOE OSTI2017

A New Framework for Quantifying Lidar Uncertainty

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards discuss uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device. However, real-world experience has shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we propose the development of a new lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from an operational wind farm to assess the ability of the framework to predict errors in lidar-measured wind speed.

17 WIND ENERGY↗
DOE OSTI2018

Behaviour related flight speeds of Sandwich Terns and their implications for wind farm collision rate modelling and impact assessment

Highlights: • Flight speeds of Sandwich Terns vary with behavioural states. • Flight speed is a vital part of wind turbine collision rate models (CRM) for birds. • The variation in flight speed has major consequences for CRM output. • CRM output is vital for adequate Environmental Impact Assessments. - Abstract: Accurate quantification of flight speeds is a prerequisite to accurately predict the numbers of collision victims of proposed wind farms using collision rate models that are a vital part of Environmental Impact Assessments. We used GPS-loggers on Sandwich Terns to collect novel data on instantaneous flight speeds during foraging trips, separated for different behavioural stages, and applied these estimates in a widely used collision rate model. Average flight speed during a foraging trip corrected for individual variation and flight type was 36.9 ± 12.3 SD km h{sup −1} and flight speed was highest during inbound commuting (44.4 ± 12.0 km h{sup −1}) and lowest during foraging (29.9 ± 10.7 km h{sup −1}). Our results show significant differences in flight speeds of Sandwich Terns between behaviour stages during foraging trips, which resulted in divergent estimates of collision victims due to wind turbines depending on the function of the area in which wind farms are proposed. Since these conclusions are likely to hold for many other bird species, we conclude that behaviour of birds in a proposed wind farm is a factor to take into account when modelling collision rates as part of the Environmental Impact Assessment.

17 WIND ENERGY↗