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The dataset includes 15-minute average wind speed and direction records from 10 m to 250 m above ground level (AGL) in 5-m range gates. Data were collected by a Scintec SFAS wind profiler installed at the Condon State Airport in Oregon, about 1.8 km northeast of the center of Condon, Ore., and are intended for validating WFIP2 model improvements.
The dataset includes 15-minute average wind speed and direction records from 30 m to 330 m above ground level (AGL) in 10-m range gates. Data were collected by a Scintec MFAS wind profiler installed at the Decker Ranch in Oregon, about 4.4 km southeast of Kent, Ore., and are intended for validating WFIP2 model improvements.
The dataset includes 15-minute average wind speed and direction records from 30 m to 330 m above ground level (AGL) in 10-m range gates. Data were collected by a Scintec MFAS wind profiler installed at the Decker Ranch in Oregon, about 4.4 km southeast of Kent, Ore., and are intended for validating WFIP2 model improvements.
The dataset includes 15-minute average wind speed and direction records from 10 m to 250 m above ground level (AGL) in 5-m range gates. Data were collected by a Scintec SFAS wind profiler installed at the Condon State Airport in Oregon, about 1.8 km northeast of the center of Condon, Ore., and are intended for validating WFIP2 model improvements.
The document describes and discusses the results of a techno-economic analysis for wind driven electrolysis systems. The costs obtained for different plants, including onshore and offshore locations and different plant sizes, are discussed. Large scale wind electrolysis plants show high potential to achieve the target of 2 $/kg. Initial results of experimental tests for a single cell electrolyzer, to be coupled directly with a wind turbine, are also presented and discussed.
Abstract. This work considers the characteristics and drivers of the loads experienced by wind turbine main bearings. Simplified load response models of two different hub and main-bearing configurations are presented, representative of both inverting direct-drive and four-point-mounted geared drivetrains. The influences of deterministic wind field characteristics, such as wind speed, shear, yaw offset, and veer, on the bearing load patterns are then investigated for similarity scaled 5, 7.5, and 10 MW reference wind turbine models. Main-bearing load response in cases of deterministic gusts and extreme changes in wind direction are also considered for the 5 MW model. Perhaps surprisingly, veer is identified as an important driver of main-bearing load fluctuations. Upscaling results indicate that similar behaviour holds as turbines become larger, but with mean loads and load fluctuation levels increasing at least cubically with the turbine rotor radius. Strong links between turbine control and main-bearing load response are also observed.
Sequence 5: Sweep Wind Speed (F,P) This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed was ramped from 5 m/s to 25 m/s by the wind tunnel operator. This was repeated with a decreasing ramp. The yaw angle was maintained at 0°. The blade tip pitch was 3° or 6°. The rotor rotated at 72 RPM. Blade pressure and probe measurements were collected for both pitch angles. The five-hole probes were removed and the plugs were installed for another 3° pitch case. Plastic tape 0.03 mm thick was used to smooth the interface between the plugs and the blade. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to –99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. During post-processing, the probe channels were set to read -99999.99. The 6- minute campaigns were named using the sequence designation 5, followed by DN or UP, which indicates the wind speed ramp direction. The next four digits are 0000, and the sequence digit is at the end.
This dataset includes collected wind speed and direction from 30 m to 600 m above ground level (AGL) in 10-m range gates.
Due to the rising global energy demand and enhanced awareness of the environmental impact of fossil fuels, the Gulf of Mexico, traditionally known for oil extraction, offers a distinct chance to repurpose the existing offshore infrastructure. With the depletion of oil reserves, it is feasible to adapt previously utilized floating platforms for extraction to generate renewable energy, specifically through wind-generated power and hydrogen production. This adaptation seeks to promote a transport system that is more ecologically friendly in the future. Offshore wind turbines serve as the main energy source, with help from battery storage and hydrogen production to enhance the overall system performance, hydrogen creation, fuel, and electricity delivery for sustainable energy production. The system is divided into two distinct cases, each evaluated for cost, performance, and feasibility, with a focus on minimizing both the Levelized Cost of Energy (LCOE) and the Levelized Cost of Hydrogen (LCOH). The first case examines the integration of offshore wind turbines with hydrogen production. Excess electricity generated by wind turbines is directed toward hydrogen production via electrolysis. The hydrogen produced can be used as fuel for vehicles or transported to the shore via pipelines. The second case investigates a technology that combines wind turbines with battery storage. The batteries possess an ability to supply electricity for a continuous duration of 4 hours maximum each day. The main objective is to reduce the LCOE by considering the battery's charging and discharging cycles, together with the uncertain attributes of wind power and battery deterioration. The produced energy can be distributed for onshore applications or utilized for the purpose of offsetting offshore loads such as subsea oil and gas production, transportation, etc. The offshore hydrogen-battery hybrid system is improved via three advanced algorithms, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). In Case 1, PSO improves hydrogen production by efficiently managing the electrolyzer’s power consumption, decreasing production costs significantly. Particle Swarm Optimization (PSO) is applied to improve the efficiency of the electrolyzer, reducing production costs and achieving an optimized CAPEX of $240.00 million (from an initial $300.00 million) and OPEX of $9.60 million per year. This system produces 4,720,000 kg of hydrogen annually, with a Levelized Cost of Hydrogen (LCOH) of $6.40/kg and an annual profit of $9.27 million. In Case 2, GWO effectively reduces the overall energy cost by improving the charge-discharge management of batteries, which extends battery life and optimizes their use. The second case focuses on integrating battery storage, optimized using the Grey Wolf Optimizer (GWO), which enhances battery charge-discharge cycles, extending battery life and lowering costs. This system achieves an optimized CAPEX of $204.80 million (from an initial $256.00 million) and OPEX of $9.29 million per year, producing 310883.39 MWh of electricity annually at a Levelized Cost of Energy (LCOE) of $86.13/MWh, with an annual profit of $6.25 million. The implementation of a comprehensive strategy results in a substantial reduction in costs, improved energy efficiency, and a dependable supply of both electric power and hydrogen, emphasizing the benefits of converting offshore oil platforms for clean energy transition. This study explores a clean strategy to enable cost-effective repurposing of offshore O&G platforms. Both cases highlight the economic and technical feasibility of transitioning offshore oil platforms to clean energy systems, demonstrating substantial cost reductions and reliable energy and hydrogen supplies for sustainable energy production.
This datset includes collected wind speed and direction from 30 m to 200 m above ground level (AGL) in 10-m range gates.
This dataset includes collected wind speed and direction from 30 m to 200 m above ground level (AGL) in 10-m range gates.
The dataset includes collected wind speed and direction from 30 m to 200 m above ground level (AGL) in 10-m range gates.
This dataset includes collected wind speed and direction from 30 m to 200 m above ground level (AGL) in 10-m range gates.
The dataset includes collected wind speed and direction from 30 m to 200 m above ground level (AGL) in 10-m range gates.
Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.
Drew Point, an unlithified ice-rich permafrost coastline along the Alaskan Beaufort Sea, is among the most rapidly eroding Arctic coastlines, with an average erosion rate of 19 m/yr from 2007 to 2019. We use 16 high-resolution remote sensing datasets (satellite, airborne, and UAV imagery) to analyze erosion mechanisms (thermal abrasion and denudation) in relation to environmental forcings along a 1.5 km stretch of coastline during the 2018 and 2019 open water seasons. In a striking contrast, 2019 exhibited the highest mean erosion rate (34.5 m) within the 2007–2019 record, while 2018 had the second lowest (11.2 m). Block failure contributed to sub-seasonal erosion rates 6 to 21 times higher than thermal denudation, with staggered block fall timing, lag responses post-storm, and non-storm block collapse influencing overall erosion magnitude and timing. To quantify wind effects, we developed wind sums, a metric combining cumulative wind speed and directional data that can be used as a proxy for integrated storm intensity capable of incorporating lagged responses that correlated strongly with erosion at sub-seasonal and annual scales. Our findings emphasize the dominant role of wind during periods of open water and air temperature during the thaw season in driving permafrost coastline erosion dynamics, while highlighting the importance of spatiotemporally high-resolution datasets for understanding Arctic coastal change dynamics.
Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 site. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.