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Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

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Isolated Building Wake Experiment At Texas Tech University’s Wind Engineering Research Field Lab

Texas Tech University’s (TTU’s) Wind Engineering Research Field Lab (WERFL) building is an idealized rectangular building on the scale of a typical suburban house. It is surrounded almost entirely by unobstructed planes and is built on a turn table. WERFL is ideal for providing measurements of an idealized building under real-world conditions including atmospheric stability and variable relative wind angles. The intent of this data set is for verification and validation of computational fluid dynamics model simulations of flow around buildings. The primary focus of this dataset features 24 3D sonic anemometers (20 downwind and 4 upwind) and 10 2D sonic anemometers (9 downwind and 1 upwind) located in the immediate vicinity of the WERFL building. It also includes a variety of instruments intended to measure the ambient meteorological parameters and the undisturbed wind profile.

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Status of Sundstrand research

Wind shear detection status is presented in the form of view-graphs. The following subject areas are covered: second generation detection (Q-bias, gamma bias, temperature biases, maneuvering flight modulation, and altitude modulation); third generation wind shear detection (use wind shear computation to augment flight path and terrain alerts, modulation of alert thresholds based on wind/terrain data base, incorporate wind shear/terrain alert enhancements from predictive sensor data); and future research and development.

Bateman, Don↗

Wind Energy Facilities

This book takes readers inside the places where daily discoveries shape the next generation of wind power systems. Energy Department laboratory facilities span the United States and offer wind research capabilities to meet industry needs. The facilities described in this book make it possible for industry players to increase reliability, improve efficiency, and reduce the cost of wind energy -- one discovery at a time. Whether you require blade testing or resource characterization, grid integration or high-performance computing, Department of Energy laboratory facilities offer a variety of capabilities to meet your wind research needs.

wind research, wind research facilities, DOE labor↗

Adaptive-wall wind-tunnel research at Ames Research Center: A retrospective

This paper reviews adaptive-wall wind-tunnel research conducted at Ames Research Center between 1978 and 1988. This research focused on developing ways to apply the concept of adaptive walls in transonic test sections with ventilated walls. In the approach pursued at Ames, local mass flow through slotted test-section walls was controlled by adjusting the pressure in compartments of a segmented plenum; the flow measurements required to test compatibility of the wind-tunnel flow with free-air boundary conditions were made using a laser velocimeter; empirical influence coefficients were used to predict how pressure changes in the plenum would effect the flow. Both two- and three-dimensional proof-of-concept experiments were conducted in a small indraft wind tunnel. Subsequently, a two-dimensional test section was demonstrated in the Ames 2x2 ft. Transonic Wind Tunnel-a wind tunnel representative of a production facility. The 2x2 ft.tests showed large reductions of wall interference; however, the time required to make the necessary flow measurements and plenum pressure adjustments far exceeded what would be acceptable for production testing. This was primarily because the imaginary surface where free-air compatibility was tested had to be separated from the test section walls to avoid the complex, viscous flow adjacent to the walls. It is unlikely that the Ames approach to adaptive walls will be applied in production wind tunnels. The two biggest unresolved problems are how to quickly and accurately make the necessary flow measurements and how to predict the effects of wall adjustments. In contrast, flexible-wall technology for two-dimensional testing is ready for application in production wind tunnels. However, neither the ventilated- nor flexible-wall approach has been shown to be a technically viable and cost-effective solution to the three-dimensional adaptive wall problem.

Schairer, Edward T.↗

Grand Challenges Revisited: Wind Energy Research Needs for a Global Energy Transition

Wind will be a foundational energy source in the electricity grid at the heart of a future integrated energy system, replacing traditional electricity generators powered by fossil fuels and providing grid reliability services in addition to energy. Future capabilities and functions of the wind energy sector will evolve apace with the future expansion and needs of global energy infrastructure; however, wind turbines designed today will not be able to provide the services needed to form and stabilize the grid as a majority supplier. In 2017, organizers for the IEA Wind Technical Experts Meeting (TEM) #89 Grand Vision for Wind Energy workshop assembled a group of experts to consider the question of how to enable a future in which wind energy supplies more than 50% of global electricity consumption. More than 70 experts representing 15 countries attended the workshop and provided diverse perspectives for the Grand Vision for Wind Energy. The IEA Wind TEM #109 was a subsequent gathering that was convened Feb. 28-March 1, 2023, in Boulder, Colorado, USA. The IEA Wind TEM #109 meeting aimed to bring together the leaders of all working groups and the IEA Wind Technology Collaboration Programme (TCP) to identify gaps in scientific knowledge, design, and deployment practice as well as identify recommendations for collaborative pathways, initiatives, and prioritized long-term research needs that can be addressed by IEA Wind. This report captures the outcomes of this meeting of international experts: five Grand Challenge areas (The Atmosphere, The Turbine, The Plant and Grid, Environmental Co-Design, and Social Science). In addition, meeting participants identified eight crosscutting topic areas that are discusses within this report (Environment-Turbine, Turbine-Atmosphere, Atmosphere-Grid/Plant, Grid/Plant-Turbine, Grid/Plant-Environment, Atmosphere-Environment, Turbine-Social, and Social-Grid/Plant).

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Cryogenic wind tunnel research - A global perspective

The development of cryogenic wind tunnels is reviewed and 13 cryogenic wind tunnels currently operating in England, France, Germany, Japan, and the U.S. are described. A table illustrating the characteristics of these tunnels is presented, including test gases, test section sizes, speed ranges, stagnation pressure and temperature, and running time. The research conducted using the various wind tunnels is outlined and the operation of each of the tunnels is considered.

Dress, D. A.↗

University of Colorado - Center for Research and Education in Wind (CREW): Cooperative Research and Development Final Report, CRADA Number CRD-11-446

Enabled by petascale supercomputing, the next generation of computer models for wind energy will simulate a vast range of scales and physics, spanning from turbine structural dynamics and blade-scale turbulence to mesoscale atmospheric flow. A single model covering all scales and physics is not feasible. Thus, these simulations will require the coupling of different models/codes, each for different physics, interacting at their domain boundaries.

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Plans and status of the NASA-Lewis Research Center wind energy project

Wind energy is investigated as a source of energy. The wind energy program that is managed by the NASA-Lewis Research Center is described. The Lewis Research Center's Wind Power Office, its organization, plans, and status are discussed. Major elements of the wind power project included are: an experimental 100 kW wind-turbine generator; first generation industry-built and user-operated wind turbine generators; and supporting research and technology tasks.

Thomas, R.↗

Wind Energy Facilities

This book takes readers inside the places where daily discoveries shape the next generation of wind power systems. Energy Department laboratory facilities span the United States and offer wind research capabilities to meet industry needs. The facilities described in this book make it possible for industry players to increase reliability, improve efficiency, and reduce the cost of wind energy -- one discovery at a time. Whether you require blade testing or resource characterization, grid integration or high-performance computing, Department of Energy laboratory facilities offer a variety of capabilities to meet your wind research needs.

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Plans and status of the NASA-Lewis Research Center wind energy project

This report describes that portion of the national five-year wind energy program that is being managed by the NASA-Lewis Research Center for the ERDA. The Lewis Research Center's Wind Power Office, its organization and plans and status are briefly described. The three major elements of the wind energy project at Lewis are the experimental 100 kW wind-turbine generator; the first generation industry-built and user-operated wind turbine generators; and the supporting research and technology tasks which are each briefly described.

Thomas, R.↗

Humidity Measurements at NASA Glenn Research Center Wind Tunnels

This presentation reviews the humidity measurements systems used at the large-scale wind tunnels at the NASA Glenn Research Center (as of July 2023), including the 9- by 15-Foot Low Speed Wind Tunnel, 8- by 6-Foot Supersonic Wind Tunnel, and the 10- by 10-Foot Supersonic Wind Tunnel. Effects of hygrometer accuracy and saturation vapor pressure model choice on Mach number are also investigated. Information in this presentation is intended for discussion with the AETC-supported Wind Tunnel Characterization Working Group during the 2023 Face-to-Face Meetings July 25-27 at NASA Glenn Research Center.

Humidity↗

The use of NASTRAN in the design of wind tunnel research aircraft

The relationship between NASTRAN and the wind tunnel model design process is discussed. Specific cases illustrating the use of NASTRAN for static, heat transfer, dynamic, and aeroelastic analyses are presented. Advantages and disadvantages of using NASTRAN are summarized.

Cooper, Michael↗

A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development (Final Technical Report)

During the two-year project period, the consortium members have developed control algorithms for enhancing the reliability of wind turbine components. The consortium members have developed advanced operation and planning tools for accommodating the high penetration of variable wind energy. The consortium members have developed extensive education and research programs for educating the stakeholders on critical issues related to the wind energy research and development. In summary, The Consortium procured one utility-grade wind unit and two small wind units. Specifically, the Consortium procured a 1.5MW GE wind unit by working with the world leading wind energy developer, Invenergy, which is headquartered in Chicago, in September 2010.

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A Synchronized Sensor Array for Remote Monitoring of Avian and Bat Interactions with Offshore Renewable Energy Facilities

Wind energy production in the U.S. is projected to increase to 35% of our nation’s energy by 2050. This substantial increase in the U.S. is only a portion of the global wind industry growth, as many countries strive to reduce greenhouse gas emissions. A major environmental concern and potential market barrier for expansion of wind energy is bird and bat mortality from impacts with turbine blades, towers, and nacelles. Carcass surveys are the standard protocol for quantifying mortality at onshore sites. This method is imperfect, however, due to survey frequency at remote sites, removal of carcasses by scavengers between surveys, searcher efficiency, and other biases as well as delays of days to weeks or more in obtaining information on collision events. Furthermore, carcass surveys are not feasible at offshore wind energy sites. Near-real-time detection and quantification of interaction rates is possible at both onshore and offshore wind facilities using an onboard, integrated sensor package with data transmitted to central processing centers. We developed and experimentally tested an array of sensors that continuously monitors for interactions (including impacts) of birds and bats with wind turbines. The synchronized array includes three sensor nodes: (1) vibration (accelerometers and contact microphones), (2) optical (visual and infrared spectrum cameras), and (3) bioacoustics (acoustic and ultrasonic microphones). Accelerometers and contact acoustic microphones are placed at the root of each blade to detect impact vibrations and sound waves propagating through the structure. On-board data processing algorithms using wavelet analysis detect impact signals exceeding background vibration. Stereo-visual and infrared cameras were placed on the nacelle to allow target tracking, distance, and size calculations. On-board image processing and target detection algorithms identify moving targets within the camera field of view. Bioacoustic recorders monitor vocalizations and echolocations to aid in identifying organisms involved in interactions. Data from all sensors are temporarily stored in ring (i.e., circular) buffers with a duration varying by sensor type. Detection of target presence or impact by any of the sensors can trigger the archiving of data from all buffers for transmission to a central data processing center for evaluation and post-processing. This mitigates the risk of “data mortgages” posed by continual recording and minimizes personnel time required to manually review event data. We first conducted individual component tests at laboratories and field sites in Corvallis and Newport, Oregon, and Seattle and Sequim, Washington. We conducted additional component tests on research wind turbines at the North American Wind Research and Training Center, Mesalands Community College (MCC; General Electric 1.5 MW turbine), New Mexico, and the National Wind Technology Center, National Renewable Energy Laboratory (NREL; Controls Advanced Research Turbines 3 [CART 3] 600 kW Westinghouse turbine), Colorado. We conducted fully integrated system tests at NREL in October 2014 and April 2015. We used only research wind turbines so that we could conduct controlled, experimentally generated impacts using empty and water-filled tennis balls shot from a compressed air cannon on the ground. The approx. 57-140 g tennis balls (depending on water content) were at the upper mass range for bats, but lower mass range for marine birds. Therefore, the ability to detect collisions of most seabirds is likely greater than our experiments demonstrate, but possibly lower for some bats depending on the background signal of a given turbine. Vibration data demonstrated that background signals of operating turbines varied markedly among the CART 3 under normal operation (greatest), GE (moderate), and CART 3 during idle rotation (generator not engaged; least). In total, we measured 63 experimental blade impacts on the two research turbines. Impaction detection was dependent on background signals, position of impact on the blade (a tip strike resulted in the strongest impact signal), and impact kinetics (velocity of ball and whether the ball struck the surface of the blade or the leading edge of the blade struck the ball). Overall detection percentage ranged from 100% for the “quietest” conditions (CART 3 idle rotation), down to 35% for the noisiest (CART 3 normal operation). Impact signals were detected from sensors on more than one blade (i.e., blades other than the blade struck) 50% - 75% of the time. Stereo imaging provided valuable metrics, but increased data processing and equipment cost. Given the cost of cameras with sufficient resolution for target identification, we suggest mounting cameras directly on the blades to continuously view the entire rotor swept area with the fewest number of cameras. Bioacoustic microphones provide taxonomic identification, as well as information on ambient noise levels. They also assist in identifying environmental conditions such as hail storms, high winds, thunder, lightning, etc., that may contribute to a collision or a false positive detection. We demonstrated a proof of concept for an integrated sensor array to detect and identify bird and bat collisions with wind turbines. The next phase of research and development for this system will miniaturize and integrate sensors from all three nodes into a single wireless package that can be attached directly to the blade. This next generation system would use all “smart” sensors capable of onboard data processing to drastically reduce data streams and processing time on a central computer. A provisional patent for the blade mounted system was submitted by Oregon State University and recorded by the U.S. Patent and Trademark Office (application no. 62313028). Eventually, technology and industry advances will allow this low cost monitoring system to be designed into materials during manufacturing so that all turbines could be monitored with either a subset or full suite of sensors. As standard equipment on all commercial turbines, the sensor suite would allow the industry to effectively monitor whether individual turbines were causing mortalities or not and under what circumstances. It would also provide real-time evaluation of mechanical and structural integrity of a turbine via vibration, image, and acoustic data streams, thereby permitting modifications in operation to limit environmental or mechanical damage.

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Distributed Real-Time Simulation and its Applications to Wind Energy Research

This paper presents a novel approach - distributed Real-Time Simulations (RTS) - for innovative wind energy and grid integration research. Distributed RTS is performed with Digital Real-Time Simulators (DRTS) in different geographical locations and connected over the Internet. Geographically distributed RTS supports - higher computation capability for RTS, leveraging distributed assets, novel testing techniques and hybridized smart grid testbeds. This paper discusses some of the basic aspects of distributed RTS with the communication delay as a probabilistic variable. To leverage hardware resources and enable flexible collaboration of national laboratories, the distributed RTS connection between the simulators at Idaho National Laboratory (INL) and National Wind Test Center (NWTC) at National Renewable Energy Laboratory (NREL) is established. The main objective of this distributed lab setup is to perform wind turbine characterization and assessment of wind integration studies in large scale grids. Several systematic steps based on integrating models are carried out to assess the impact of communication delay on distributed RTS.

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