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DOE OSTI2020

Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains

Continued growth of wind turbine physical dimensions is examined in terms of the implications for wind speed, power and shear across the rotor plane. High-resolution simulations with the Weather Research and Forecasting model are used to generate statistics of wind speed profiles for scenarios of current and future wind turbines. The nine-month simulations, focused on the eastern Central Plains, show that the power scales broadly as expected with the increase in rotor diameter (D) and wind speeds at hub-height (H). Increasing wind turbine dimensions from current values (approximately H = 100 m, D = 100 m) to those of the new International Energy Agency reference wind turbine (H = 150 m, D = 240 m), the power across the rotor plane increases 7.1 times. The mean domain-wide wind shear exponent (α) decreases from 0.21 (H = 100 m, D = 100 m) to 0.19 for the largest wind turbine scenario considered (H = 168 m, D = 248 m) and the frequency of extreme positive shear (α > 0.2) declines from 48% to 38% of 10-min periods. Thus, deployment of larger wind turbines potentially yields considerable net benefits for both the wind resource and reductions in fatigue loading related to vertical shear.

17 WIND ENERGY↗

DOE OSTI2020

The Power Curve Working Group's assessment of wind turbine power performance prediction methods

Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze data from 55 wind turbine power performance tests from nine contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the baseline method, which uses the conventional definition of a power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also determine that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power curve modeling and establishes the groundwork for future collaborations.

17 WIND ENERGY↗

DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗

DOE OSTI2024

Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines: Preprint

This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be timeconsuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the towerbase damage equivalent loading and better tracking of the rated generator speed and power.

closed-loop control↗

DOE OSTI2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

17 WIND ENERGY↗

DOE OSTI2020

Hydrokinetic Turbines in Yawed Conditions: Toward Synergistic Fluvial Installations

Laboratory experiments were performed in critical mobility conditions to study the effects of an in-stream horizontal axis turbine in yawed conditions. The misalignment between the rotor axis and the incoming flow velocity is observed to alter the near and far wake of the turbine, as well as the scour and deposition patterns in the proximity of the monopile support tower. Various hydraulic conditions may lead to such misalignment, e.g., as a result of complex fluvial bathymetries distorting the flow or as a strategy to steer the wake away from downstream units and maximize energy production in a turbine array. The research first investigates the simplest case of a single turbine deployed along the channel centerline and oriented at different yaw angles to study the wake deflection and the turbine performance. A second set of experiments is performed moving the turbine relatively close to a nonerodible lateral wall to explore a potential passive yaw control strategy devoted to protect the banks from erosion by steering the wake toward the channel center and favor deposition along the bank.

42 ENGINEERING↗

DOE OSTI2023

Turbine-based Power System Tool

Advanced research tools are essential to illustrate the effects of turbine-based power system operations on the electric grid network. This paper presents the development of an open-source tool to visualize the operation of turbine-based electricity generation. The tool exposes power system models, control parameters, and corresponding values of the turbine model and its electrical system, allowing them to customize the simulation according to their needs. The developed tool enables users to simulate power profiles for different turbinebased energy generation such as wind, tidal, and gas turbines. It allows users to investigate the details of the generated profiles of various types of turbine systems at various scales. It can provide valuable insights into model development and facilitate the analysis of integrated power systems. By enabling access to turbine-based operations visualization, the tool aims to bridge the gap between advanced research tools and users, facilitating broader adoption of renewable energy technologies and aiding in developing sustainable power systems.

Kini, Roshan L.↗

DOE OSTI2020

Sensitivity of Wind Turbine Array Downstream Effects to the Parameterization Used in WRF

The Weather Research and Forecasting (WRF) Model has been extensively used for wind energy applications, and current releases include a scheme that can be applied to examine the effects of wind turbine arrays on the atmospheric flow and electricity generation from wind turbines. Herein we present a high-resolution simulation using two different wind farm parameterizations: 1) the “Fitch” parameterization that is included in WRF releases and 2) the recently developed Explicit Wake Parameterization (EWP) scheme. We compare the schemes using a single yearlong simulation for a domain centered on the highest density of current turbine deployments in the contiguous United States (Iowa). Pairwise analyses are applied to diagnose the downstream wake effects and impact of wind turbine arrays on near-surface climate conditions. On average, use of the EWP scheme results in small-magnitude wake effects within wind farm arrays and faster recovery of full WT array wakes. This in turn leads to smaller impacts on near-surface climate variables and reduced array–array interactions, which at a systemwide scale lead to summertime capacity factors (i.e., the electrical power produced relative to nameplate installed capacity) that are 2%–3% higher than those from the more commonly applied Fitch parameterization. It is currently not possible to make recommendations with regard to which wind farm parameterization exhibits higher fidelity or to draw inferences with regard to whether the relative performance may vary with prevailing climate conditions and/or wind turbine deployment configuration. However, the sensitivities documented herein to the wind farm parameterization are of sufficient magnitude to potentially influence wind turbine array siting decisions. Thus, our research findings imply high value in undertaking combined long-term high-fidelity observational studies in support of model validation and verification.

17 WIND ENERGY↗

DOE OSTI2021

Welding of Haynes 282 to Steels to Enable Modular Rotors for Advanced Ultra Super-Critical Steam Turbines

Steam turbines for an Advanced Ultra Super Critical (AUSC) fossil fired power plant will operate at temperatures well above those of current commercial steam cycles with inlet temperatures more than 760 C , which is beyond the capabilities of alloy steels presently used for steam turbine applications and requires advanced materials. Large components such as steam turbine rotors may be made of nickel based super alloys such as Haynes 282 (H282). However, monolithic forgings of superalloys in the sizes required for large steam turbines rotors can be prohibitively expensive besides many technical challenges. To minimize cost and alleviate the related technical challenges, superalloy use needs to be limited to locations on the steam turbine rotor where strength and temperature requirements cannot be met by conventional steels. This is possible if nickel-based superalloys can be successfully welded to steels and the related technical challenges - machining parts made of dissimilar welded materials, non-destructive examination of such welds for flaw detection; and, material properties of such hybrid components – are sufficiently addressed. In this technology development project, we successfully welded H282 to plates up to ~ 75 mm (~ 3 inches) to a 3.5NiCrMoV steel of similar thickness. Advanced ultrasonic inspection technique called Phased Array Ultrasonic Testing (PAUT) was employed to examine the dissimilar H282-Steel welds, into which flat bottomed side drilled holes (SDH) of various diameters were introduced, to determine the minimum detectable feature sizes; it was shown that with PAUT SDH of dia. down to 0.5 mm could be detected in the base alloys and SDH with dia. down to 2.4 mm could be detected in the weld metal under multiple orientations successfully. An autonomous machining process monitoring system was developed and demonstrated whereby the forces acting on the cutting tool could be actively monitored as the cutting tool transitioned from H282 to Steel across the weld using which the machining parameters can be potentially altered without interruption to extend tool life. This project successfully achieved its objectives of - i. Developing a welding methodology and viable welding geometries, to successfully join H282 to steel 3.5CrMoNiV steel to enable manufacture of modular steam turbine rotors for AUSC applications (conditions of at least 760 °Celsius and 3,100 psia (pounds per square inch absolute pressure) and evaluate the material properties of the welded specimen. ii. Employ the advanced ultrasonic inspection technique to the dissimilar weld metal joint and determine the minimum detectable feature sizes. iii. Develop effective machining techniques to machine such hybrid structures with online tool force monitoring and effect machine state metrics for optimal results.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Acoustic Particle Velocity Measurements around a Tidal Current Turbine

Quantifying the underwater sound produced by tidal turbines is essential both to understand their potential environmental impacts and to understand how that sound might interfere with the intended application of the turbine (e.g., powering acoustic monitoring systems). In this project, we measured the sound radiated by a small-scale crossflow tidal turbine. The turbine was deployed from October 2023 to March 2024 in the tidal channel at the entrance to Sequim Bay, WA. Acoustic measurements were made with three different sensor packages: a commercial-off-the-shelf vector sensor (operated by PNNL), a vector sensor array (operated by Integral Consulting), and drifting hydrophones (operated by UW). Acoustic recordings from the three sensors highlight changes in the turbine acoustic signature over the course of a tidal cycle and throughout the 6-month turbine deployment. Our results also highlight the utility of acoustic vector sensors for localizing sound attributable to marine energy devices in acoustically complex environments.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Deploying Taller Turbines in Complex Terrain: A Hill Flow Study (HilFlowS) Perspective

Terrain-induced flow acceleration is presented for the summertime, peak power season at Lawrence Livermore National Laboratory’s Site 300 for the Hill Flow Study (HilFlowS). HilFlowS, designed as an adjunct field campaign to the Department of Energy’s Second Wind Forecasting Improvement Project (WFIP2), provides wind profile observations at a second location in complex terrain for validating numerical atmospheric model simulations and for better understanding flow behavior over hills for wind power generation. One unique feature of HilFlowS was the inclusion of an undergraduate university student who helped plan and execute the experiment as well as analyze wind data from two remote sensing laser detection and ranging (lidar) instruments deployed along parallel ridgelines. HilFlowS examines the trend of building higher into the atmosphere for the purpose of increasing wind turbine power production and evaluates the wind resource in the Altamont Pass Region of Northern California for a set of wind turbines of differing hub-heights and rotor-disk diameters found in the area. The wind profiles show strongly channeled onshore flow above both hills, enhanced by strong subsidence aloft, which produces a wind maximum (Umax) around z = 10 m and strong negative shear throughout all of the evaluated rotor-disks for much of the summer wind season. Under these conditions, shear becomes more negative with increasing hub-height and increasing rotor-disk size. Rotor-disk equivalent wind speed (Uequiv), a measure of the average wind speed across the entire rotor-disk, is compared to hub-height, rotor length, and rated capacity factor for the set of turbines. Uequiv is most closely related to turbine hub-height and is negatively correlated given the low altitude of Umax. Based on these results, building the largest capacity, large rotor-disk wind turbine at the lowest possible hub-height appears to provide turbines in the Altamont with a fast, near-surface, onshore wind resource during the peak power season.

17 WIND ENERGY↗

DOE OSTI2026

Power output of turbines mounted on tension-leg platforms subjected to fully developed ocean gravity waves

A concern in the deployment of large wind turbines on ocean floating platforms is the effect of floating-platform motions on their electrical power generation. Further, it is not clear how floating motions influence waking, which might affect the combined power generation of collections of turbines. We examine the average power output of a single and a collection of NREL 5 MW wind turbines mounted on a tension-leg platform (TLP) under the action of fully developed ocean wave motions, coupling floating motions with the large-eddy simulation (LES) of atmospheric and rotor dynamics. The ocean dynamics enter as fully developed waves derived from the Pierson–Moskowitz spectrum. To assess the influence of ocean motions, we performed simulations over the full range of wind speeds in the operational range of the turbine, reporting comparisons of average power output when the platforms are allowed to move to when they are held rigidly in place. In all simulations, we find that the effects of the TLP floating-platform's induced motions have a minor effect on single and multiple turbine power production and wake deficits. Even when using coherent and large amplitude harmonic-floating-induced perturbations, any significant wake modifications from floating motions are confined to the near-wake region, where downstream turbines are unlikely to be located. The relatively small amplitude of TLP motions relative to pre-existing turbulent fluctuations are the primary reason for low wake and power modifications downstream.

Restrepo, Juan [ORNL] (ORCID:0000000326092882)↗

DOE OSTI2026

Impact of atmospheric turbulence on performance and loads of wind turbines: knowledge gaps and research challenges

Wind energy harvesting from the atmosphere takes place in the atmospheric boundary layer. The boundary layer shear and buoyancy create three-dimensional turbulent eddies spanning a range of scales that form a continuous forward cascade of kinetic energy to the smallest scales of motion where energy is dissipated. Large-scale atmospheric circulations modulate the boundary layer turbulence, characterized by coherence and intermittency. As wind turbines grow in size and the integrated control of both turbines and wind farms spans greater distances, the relationship between the scales of atmospheric turbulence and the design and operation of wind energy facilities has entered new territory. The boundary layer turbulence impacts both wind turbine power production and turbine loads. Optimizing wind turbine and wind farm performance requires an understanding of how turbulence affects both wind turbine efficiency and reliability. While the characteristics of atmospheric boundary layer turbulence have been observed and studied in detail over the last few decades, there are still significant gaps in our understanding of the impact of turbulence on wind power resources and wind farm operations. This paper outlines the current state of turbulence research relevant to wind energy applications and points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.

Kosović, Branko [Johns Hopkins Univ., Baltimore, M↗

DOE OSTI2020

Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

17 WIND ENERGY↗

DOE OSTI2020

Detecting faults in wind turbines

A wind turbine generator fault detection method is described. The method includes obtaining a first signal from a generator of a wind turbine and a second signal from a vibration sensor coupled to the wind turbine, the first signal representing an output current of the generator, and the second signal being a time-sampled signal representing vibrations of a bearing in the wind turbine. Determining a shaft rotation frequency signal from the first signal, the shaft rotation frequency signal representing a time-varying rotational speed of a shaft of the wind turbine. Resampling an envelope of the second signal based on the shaft rotation frequency signal to provide a third signal, the third signal being an angular sampled signal. Detecting, by the at least one processor, a fault in the bearing of the wind turbine by identifying a characteristic signature of a bearing fault in the third signal.

Qiao, Wei↗

DOE OSTI2022

Implications of Shear and Thermal Stratification on Wind Turbine Tip-Vortex Stability

The interaction between wind turbines in a wind farm through their wakes is a phenomenon that has been studied for decades and is still relevant today. Turbines clustered together in arrays will often operate in the wake of other upstream turbines which may lead to significant power losses and fatigue loads. For modern large-scale wind turbines, the mean shear velocity profile and thermal stratification are major components of the atmospheric boundary layer so it is important to understand their impact on near-wake development. Additionally, veer is present due to the rotation of the Earth. The impact of shear, thermal stratification and veer on the stable wake length of turbines with a dynamic control strategy is studied numerically in this work using a suite of highly resolved large-eddy simulations. Instantaneous flow fields are extracted from the simulations and used to conduct proper orthogonal decomposition (POD) and compute the mean kinetic energy fluxes by different POD modes to better understand the tip-vortex instability mechanisms. Our findings show that the dynamic pitch control scheme is able to shorten the stable wake length to about 1.5R in uniform flow. Shear can significantly affect the break up of wind turbine tip-vortices as well as the shape and stable length of the wake, whereas thermal stratification seems to only have limited contribution to the spatial development of the near-wake field. Veer causes the wake boundary to skew but has a limited impact on the wake length.

length thermal stratification↗

DOE OSTI2022

Detecting faults in wind turbines

Qiao, Wei↗