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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

Evolving Multi-hazard Machine Learning Modeling for Advanced Risk-Informed Infrastructure Resilience Assessment

The socioeconomic impacts of pipeline incidents have escalated over the past three decades, revealing the limitation of traditional risk modeling methods when applied to extensive pipeline networks. This research aims to develop machine learning (ML) models that effectively identify, rank, and predict the diverse hazards and socioeconomic consequences associated with pipeline incidents. Utilizing historical data on pipeline incidents alongside weather and oceanographic data from the 1980s onward, the Houston metropolitan area serves as a testbed for the proposed methodologies. The research segments the combined datasets into three consecutive periods, demonstrating the efficacy of the updated model in predicting future events, particularly concerning precipitation rate data. Despite the challenges posed by a relatively limited dataset, local-level ML modeling offers valuable insights into the spatial and temporal dynamics of multiple hazards that contribute to pipeline incidents. These findings hold significant implications for future research, particularly in understanding and mitigating risks in various locations across the Gulf Coast and other coastal regions.

42 ENGINEERING↗

Advancing Offshore Wind Resource Characterization Using Buoy-based Observations

As countries continue to implement sustainable and renewable energy goals, the need for affordable low-carbon technologies, including those related to offshore wind energy, is accelerating. The U.S. federal government recognizes the environmental and economic benefits of offshore wind development and is taking the necessary steps to overcome critical challenges facing the industry to realize these benefits. The U.S. Department of Energy (DOE) is investing in buoy-mounted lidar systems to facilitate offshore measurement campaigns that will advance our understanding of the offshore environment and provide the observational data needed for model validation, particularly at hub height where offshore observations are particularly lacking. On behalf of the DOE, Pacific Northwest National Laboratory manages a Lidar Buoy Program that facilitates meteorological and oceanographic data collection using validated methods to support the U.S. offshore wind industry. Since being acquired in 2014, two DOE lidar buoys have been deployed on the U.S. east and west coasts; and their data represent the first publicly available multi-seasonal hub height data to be collected in U.S. waters. In addition, the buoys have undergone performance testing, significant upgrades, and a lidar validation campaign to ensure the accuracy and reliability of the lidar data needed to support wind resource characterization and model validation. The Lidar Buoy Program is providing valuable offshore data to the wind energy community, while focusing data collection on areas of acknowledged high priority.

offshore wind energy, lidar buoy, wind resource ch↗

Assessment of Model Hub Height Wind Speed Performance Using DOE Lidar Buoy Data

As the offshore wind industry continues to gain momentum both in the U.S. and globally, reanalysis products remain essential tools for the lifecycle of a wind farm. From project siting and production estimates to determining construction and maintenance windows, reanalysis products play an important role. These regional and global long-term models also serve as inputs to higher resolution models. With such wide-ranging impacts on the offshore wind industry, the need for validation of reanalysis products is strong, particularly at typical turbine hub heights. Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinelTM buoys for the U.S. Department of Energy (DOE) in order to collect meteorological and oceanographic data, including hub height wind speed, in areas of interest for offshore wind development. This work uses DOE buoy observations off the coasts of New Jersey and Virginia to evaluate the performance of Modern Era Retrospective Analysis for Research and Applications-2 (MERRA-2), the North American Regional Reanalysis (NARR), the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th generation (ERA5), and the National Oceanic and Atmospheric Administration (NOAA) assimilation system Rapid Refresh (RAP). Biases and degrees of correspondence are determined for each reanalysis product in order to provide insights on the performance and uncertainty for long-term wind resource characterization. With the baseline metrics determined, this work proceeds with an investigation into the sources of large deviations between the modeled and observed hub height wind speeds during the East Coast lidar buoy deployments. Consistent sources of reanalysis model error include stably stratified flow conditions, high wind shear, flow parallel to the coastline, and tropical and winter storms. For the near shore New Jersey location, offshore winds (i.e. blowing from land to water) tend to be associated with model overestimation of observed hub height wind speed, while winds parallel to the coastline are correlated with model underestimation of wind speed. Similarly, near the Virginia buoy, during southerly winds (parallel to the coastline) models underestimate the observed wind speed due to coastal upwelling. Reanalysis model wind speeds underestimate the hub height winds with increasing stable atmospheric stratification. Reanalysis model biases are seen when the observed wind speeds approach or exceed typical turbine cut-out speeds (around 20 m s-1). Large model errors during winter and tropical storm events, high wind speed events, and ramp events are often the result of wind speed timing and magnitude offsets. Overall, ERA5 provides the most successful representation of observed offshore hub height wind speeds at the U.S. East Coast buoy locations and is therefore best suited as an input boundary condition for model case studies. ERA5 performs well for a variety of atmospheric phenomena, including storms and sea breezes, however RAP, the next most successful reanalysis model, performs best in capturing the frequency of ramp events.

17 WIND ENERGY↗

Ocean Variability Beneath the Filchner-Ronne Ice Shelf Inferred From Basal Melt Rate Time Series

Fourteen phase-sensitive radars (ApRES) were deployed on the Filchner-Ronne Ice Shelf (FRIS) to measure variability in its basal melt rate. Melt rates from sites along the Ronne Depression vary seasonally, consistent with the dynamics of the propagation of seasonal dense water from the western ice front into the cavity. Several sites at the back of the FRIS cavity feature a signal with two seasonal maxima. Sub-ice shelf oceanographic data available from one of the sites indicate that this signal is caused by two different pathways followed by the same source water. Inter-annual variability is strongest along a direct flow pathway between western Ronne Ice Front and western Berkner Island. Highest melting occurred in 1999 and 2018, following anomalously low summer sea-ice concentrations in front of the ice shelf. Inter-annual melt rate variability at the back of the FRIS cavity is limited. If present, it is expressed as a suppression or delay in the arrival of the seasonal melt rate minimum, which can be understood in terms of inter-annual stratification changes and variable inflow pathways toward the sites. Long term mean ApRES melt rates agree with estimates from satellite data over eastern FRIS. However, the satellite estimates overstate the area of active basal freezing in the western part of the ice shelf. Furthermore, the temporal melt rate variability from the satellite estimates exaggerates the range of variability at both seasonal and inter-annual time scales with any correspondence between the in-situ and remotely derived inter-annual variability being limited to a single site.

54 ENVIRONMENTAL SCIENCES↗

Powering the Blue Economy Foundational Research & Development Topics Reliability for Marine Energy Power Systems

Ocean observing platforms are deployed across the world’s oceans to collect meteorological and oceanographic data. Current technology makes use of solar panels and battery banks to provide power to these platforms, which can be challenging at high latitudes due to lack of solar insolation and cold temperatures. Marine renewable energy (MRE) generation offers a potential improvement in consistent power generation throughout the year for this use on ocean observation buoys. This report analyzes existing reliability of high latitude coastal weather buoys and tsunami detection buoys to determine performance standards that MRE generation sources must meet to become viable power sources for these remote systems. Using measurements recorded from the U.S. National Data Buoy Center’s coastal weather buoys and tsunami detection buoys, the analysis calculated the historic data availability – or the percentage of time a measurement is recorded – to evaluate the reliability of ocean observation buoys. The results show that coastal weather buoys operate at 74% data availability and the tsunami detection systems operate at an average 72% data availability. The results describe the total reliability of the system, including many different types of failures not associated with the power system such as mooring failures or equipment failures caused by extreme weather. Power system outages may only account for a small portion of the system failures. Reliability is an important design criterion for the power system of ocean observation platforms. Future power systems that would be designed for these buoys, such as an MRE generator, need to have a minimum reliability of 72% and 74% to meet the overall performance of the weather buoys and tsunami buoys, respectively. However, power system reliability likely needs to be much higher than overall system reliability, so that power system outages do not negatively impact the overall performance of the buoy.

42 ENGINEERING↗

NDBC Buoy 46011

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are a part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46012

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46013

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46014

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are a part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46022

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46025

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46026

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46027

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46028

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46042

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46047

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46053

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗

NDBC Buoy 46054

The purpose of this dataset is to provide filtered, averaged buoy surface meteorological and oceanographic data and standardize the data format into NetCDF. The buoy was deployed by NDBC. These data are part of the observational database created for supporting the floating offshore wind project within the DOE-funded ORACLE project.

17 WIND ENERGY↗