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Servo-aero-gravo-elastic (SAGE) scaling and its application to a 13-MW downwind turbine
Reduced scale wind turbines can be extremely cost-effective to test new rotor concepts since prototype costs are heavily dependent on the rotor diameter. Ideally, the scaled model would have the same non-dimensional deflections, dynamics, and control behavior as the full-scale model. This would provide a high-fidelity demonstration of the full-scale performance, which is ideal if the full-scale turbine has significant aeroelastic interactions. To this end, servo-aero-gravo-elastic (SAGE) scaling is developed and applied to a 13-MW turbine that is scaled to a 20% scale model. The scaling preserves the tip-speed ratio, the rotor speed normalized by the flapping frequency, and the tip deflections normalized by the blade length. In addition, the controller employs the same control structure (gain-scheduled pitch control and variable speed torque control) and is scaled dynamically (e.g., matching non-dimensional time constant of the pitch angle, etc.). Furthermore, the thrust, gravity, and centrifugal moments are scaled such that the load angles are preserved as a function of a non-dimensional wind speed. However, the environmental scaling must consider differences in Reynolds number (since this parameter cannot be held constant) and subsequent changes in the axial induction factor. While the presented results showcase these differences during operational conditions, the non-dimensional tip deflections remain comparable through all wind speed ranges, indicating the viability of the SAGE scaling method in matching full-scale aeroelastic responses.
On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL
The response of floating wind turbines (FWT) are susceptible to stochastic wave variations. For the optimal operation of FWT, a comprehensive understanding of the phaseresolved wave dynamics and the consequential system response is crucial for real-time monitoring and control. A multi-variate, multi-step, long short term memory (MLSTM), a type of recurrent neural network (RNN) is used to capture complex system dynamics for real-time application. Results indicate that the integration of a wave prediction-reconstruction (WRP) model substantially enhances prediction accuracy by 50% on average relative to the baseline model. The improvement is consistent across various wave extremity and prediction horizons, thereby significantly broadening the scope for timely and precise predictive capabilities.
Mobility of heavy rare earth elements in magmatic-hydrothermal systems: Experimental determination of DyPO 4 solubility in supercritical fluids
Dysprosium (Dy) is part of the heavy rare earth elements (REE) and a critical component in the production of high-performance super magnets used in wind turbines, electric vehicles, and other green technologies. The HREE phosphate, xenotime, is commonly associated with hydrothermal systems, where the mobility, fractionation, and deposition of REE are controlled by the stability of aqueous complexes and solubility of REE minerals which depend on temperature, pressure, and fluid composition (salinity, pH, and ligand availability). Here, we conducted a series of synthetic DyPO 4 solubility experiments in NaCl-HCl-bearing aqueous solutions (pH of 2 at 25 °C) from 500 to 700 °C and 1.5 kbar using externally heated pressure vessels. DyPO 4 displays a strong prograde solubility with temperature across all studied fluid salinities (0–0.5 m NaCl). The DyPO₄ solubility isotherms at 500 °C, 600 °C, and 700 °C show a complex relationship with salinity where DyPO₄ solubility is highest (39.2–1552 ppm at 500–700 °C) in 0 m NaCl solutions, decreases strongly (21–331 ppm at 500–700 °C) in the 0.06 m NaCl solutions and remains relatively constant in the 0.25 m (42.8–248 ppm) and 0.5 m NaCl (40.2–124 ppm) solutions. Thermodynamic modeling suggests that pH increases as a function of temperature and salinity, and controls solubility due to the stability of chloride complexes (i.e., DyCl +2 and DyCl 2 + ) at 500–600 °C, transitioning to hydroxyl complexes (i.e., Dy(OH) 3 0 ) dominating at 700 °C and relatively alkaline pH. Comparison with NdPO 4 solubility data indicates that the light REE in monazite is significantly more soluble than the heavy REE in xenotime in saline fluids at high temperature (500–700 °C), implying efficient light/heavy REE fractionation in supercritical fluids. The lower solubility of DyPO 4 relative to NdPO 4 suggests that at high temperatures, acidic (pH 2–4), low-salinity (∼5 wt% NaCl) fluids will favor the precipitation of xenotime over monazite as it will cool from 700 °C to below 500 °C. These observations align with paragenetic mineral assemblages in NYF pegmatites (e.g., Baveno, Italy), where xenotime commonly immobilizes heavy REE during the high-temperature hydrothermal stages.
Public Reference Data for Megawatt-Scale Hydrogen Electrolysis – Simulated Wind
The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wind turbine. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen . While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the simulated wind energy profiles, NLR used OpenFAST to simulate a 3.4-MW International Energy Agency (IEA) reference wind turbine. The hour-long wind energy profiles varied over wind turbulence intensity (Class A or Class C) and average wind speed (5, 7, or 9 m/s). To match the power limits of the 1.25-MW electrolyzer and 3.4-MW IEA wind turbine most effectively and to maximize the efficiency of hydrogen production at a given average wind speed, the profiles were sometimes scaled by two times. This means that, in some cases, the experimental setup assumed two 1.25-MW electrolyzers were coupled with the wind turbine, representing a total maximum electrolysis load of 2.5 MW. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}-{average wind speed}-{turbulence class}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “windIEA3.4-5ms-C_2-400.zip” represents the hour-long experiment using the IEA 3.4-MW turbine, subjected to an average wind speed of 5 m/s and Class C wind turbulence, and connected to two 1.25-MW electrolyzers with the power supply set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wind turbine power. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis .
Low-cost Lidar for Efficient Wind Energy Production
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Validation of Numerical Models of the Offshore Wind Turbine From the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III
The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on a Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) Idling below the cut-in wind speed; (2) Rotor-nacelle assembly (RNA) rotation maneuver below the cut-in wind speed; (3) Power production below and above the rated wind speed; and (4) Shutdown. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) Strains and accelerations recorded on the support structure; (2) Pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools. Therefore, strains were calculated based on out-of-plane bending moments, axial forces, and cross-sectional properties of the structural members. Also, a number of issues arose during the validation: (1) The need for a thorough quality check of sensor measurements; (2) The sensitivity of the turbine loads to the controller and airfoil properties, which were only approximated in the modeling approach; (3) The importance of estimating and applying an appropriate damping value for the structure; and (4) The importance of wind characteristics beyond turbulence on the loads. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, probability density functions of strains and accelerometers. A good match was achieved between the measurements and models set up by OC5 Phase III participants.
Control-Oriented Model for Secondary Effects of Wake Steering
This paper presents a model to incorporate the secondary effects of wake steering in large arrays of turbines. Previous models have focused on the aerodynamic interaction of wake steering between two turbines. The model proposed in this paper builds on these models to include yaw-induced wake recovery and secondary steering seen in large arrays of turbines when wake steering is performed. Turbines operating in yaw-misaligned conditions generate counter-rotating vortices that entrain momentum and contribute to the deformation and deflection of the wake at downstream turbines. Rows of turbines can compound the effects of wake steering that benefit turbines far downstream. This model quantifies these effects and demonstrates that wake steering has greater potential to increase the performance of a wind farm due to these counter-rotating vortices especially for large rows of turbines. This is validated using numerous large-eddy simulations for three-turbine, five-turbine, and wind farm scenarios.
Feedforward Control For Extreme Gusts with Remote Sensing: Pilot Study [Slides]
Our objective is to demonstrate feed-forward controller leveraging far-field sensing (i.e., whole-farm sensing as opposed to near-turbine sensing from lidar) that reduces likelihood of premature failure during extreme wind events as per DLCs.
Enhanced joining strength in additive-manufactured polylactic-acid structures fused by embedded heated metallic meshes
Additively manufactured thermoplastic polymers, such as polylactic acid (PLA), hold significant promise for sustainable engineering structures, including wind turbine blades. Upscaling these structures beyond the limitations of 3D printer build volumes is a challenge; fusion joining presents a potential solution. This paper introduces a displacement-controlled resistance welding process for PLA, as an alternative to the typical force-controlled methods. Here, we investigated the bonding quality of resistance-welded and adhesive-bonded PLA beams through three-point bending and measured the surface deformations using digital image correlation. Different metal meshes (30%/0.11 mm Ni—Cu, 34%/0.07 mm Ni—Cu, and 36%/0.25 mm Co—Ni) served as heating elements. The process parameters were varied for the 34%/0.07 mm Ni—Cu mesh to identify an optimum set of parameters. Results showed that this optimized displacement-controlled welding achieved 94% of the original strength of monolithic samples. This indicates that the new welding process not only ensures high-quality bonding and fine surface finishing but also promotes sustainability, recyclability, and economic efficiency in various polymer and composite structural applications.
Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)
Abstract. During the second Wind Forecast Improvement Project (WFIP2; October 2015–March 2017, held in the Columbia River Gorge and Basin area of eastern Washington and Oregon states), several improvements to the parameterizations used in the High Resolution Rapid Refresh (HRRR – 3 km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST – 750 m horizontal grid spacing) numerical weather prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80 m wind speeds (wind turbine hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and three profiling lidars for comparison. Improvements due to the experimental physics (EXP vs. CNT runs) and those due to finer horizontal grid spacing (HRRRNEST vs. HRRR) and the combination of the two are compared, using standard bulk statistics such as mean absolute error (MAE) and mean bias error (bias). On average, the HRRR 80 m wind speed MAE is reduced by 3 %–4 % due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5 % on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80 m wind speed MAE, up to 7 %–8 %. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena. Causes of model weaknesses are identified. Finally, bias correction methods are applied to the 80 m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors.
Southwest Wind Power R&D Test Site Development at the National Wind Technology Center (CRADA CRD-12-00472 Final Report)
Southwest Windpower, Inc. (SWWP) has been designing and distributing small wind turbines for more than 22 years and is the recognized global leader in the design, manufacturing and distribution of small wind systems (400-3000 watts). The company has been a pioneer in the development of wind technology and has built and shipped more than 170,000 wind turbines to over 120 countries worldwide. Headquartered in Flagstaff AZ, SWWP has sales representatives in over 88 countries. Applications for SWWP systems include residential homes, commercial properties, micro grids, remote cabins, telecom transmitters, offshore platforms, water pumping and sailboats. In addition, U.S. Department of Defense uses SWWP’s products in Forward Operating Bases (FOB’s), USGS uses them for remote monitoring of glacier movements and the CIA uses them to provide power seismic monitoring. Today, SWWP plans development of new technologies to address distributed energy market needs and become even more aggressive in international markets. This strategy includes extensive Research, Development, Demonstration & Deployment (RDD&D) activities to improve existing production line into more simple, reliable, and cost-effective wind turbine systems. Important part of such activities is the field testing of the new systems, which should provide: (i) validation of the reliable operation of hardware and software; (ii) validation of numerical models of the system and its components; (iii) field comparison of various control and optimization strategies; (iv) customized reliability testing; (v) customized monitoring and analysis testing; and (vi) IEC certification testing (vii) filed demonstration of system performance and reliability. Such field testing facility should encompass multiple towers and appropriate data acquisition systems. SWWP is exploring to move part of the engineering department to Colorado, and execute its RDD&D strategies in this new location. Partnership with NREL will provide great insights into SWWP’s RDD&D processes and more efficient commercialization by utilizing NWTC as the field testing site, and working close with NREL personnel. NWTC is ideally suited to strain the design. NREL is very experienced with customized and IEC testing. During this project, Southwest Windpower aims to improve the value proposition of distributed renewable energy, and make it a competitive choice in the energy markets. As of today, more then 170,000 small wind turbines, manufactured in the U.S., are installed in more then 180 countries. With the improved value proposition, Southwest Windpower wants to create and address new market needs, substantially increase its sales domestically and internationally, which should have positive impact on the creation of American jobs, competitiveness of U.S. economy, export or U.S. products, and the use of renewable energy.
A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines
Abstract. The article presents a symbolic framework (also called computer algebra program) that is used to obtain, in symbolic mathematical form, the linear and nonlinear equations of motion of a mid-fidelity multibody system including rigid and flexible bodies. Our approach is based on Kane's method and a nonlinear shape function representation for flexible bodies. The shape function approach does not represent the state of the art for flexible multibody dynamics but is an effective trade-off to obtain mid-fidelity models with few degrees of freedom, taking advantage of the separation of space and time. The method yields compact symbolic equations of motion with implicit account of the constraints. The general and automatic framework facilitates the creation and manipulation of models with various levels of complexity by adding or removing degrees of freedom. The symbolic treatment allows for analytical gradients and linearized equations of motion. The linear and nonlinear equations can be exported to Python code or dedicated software. There are multiple applications, such as time domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins. In this article, we describe the method we used to systematically generate the equations of motion of multibody systems and present the implementation of the framework using the Python package SymPy. We apply the framework to generate illustrative land-based and offshore wind turbine models. We compare our results with OpenFAST simulations and discuss the advantages and limitations of the method. The Python implementation is provided as an open-source project.
A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing
Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.
Dynamic Modeling of Full Converter Adjustable-speed Pumped Storage Hydropower (FC AS-PSH)
Full converter adjustable-speed pumped storage hydropower (FC AS-PSH) technology, as one of advanced-PSH technology, is developed from wind turbine technology. By making the synchronous machine connect to the grid through a full-size converter, FC AS-PSH has a wider adjustment range of speed and a better reactive power control capability compared with a doubly-fed asynchronous generator AS-PSH technology. When it plays as an energy backup in the power system, FC AS-PSH can provide a much faster response than conventional-PSH (C-PSH) which makes this technology provide better ancillary service for a high renewable penetrated system. In this paper, the dynamic modeling of FC AS-PSH is fully studied. We develop a detailed model of this technology in the IEEE 14-bus system based on GE Positive Sequence Load Flow (PSLF) platform. Especially, the first governor model is developed based on the Engineer’s Program Control Language (EPCL) user-defined model in this platform. All operation modes are validated and studied under a system contingency. Besides, comparison cases between FC AS-PSH and C-PSH are studied to show advantages providing from FC AS-PSH when it works with renewable energy.
Downwind coning concept rotor for a 25 MW offshore wind turbine
The size of offshore wind turbines over the next decade is expected to continually increase due to reduced balance of station costs per MW and also the higher wind energy at increased altitudes that can lead to higher capacity factors. However, there are challenges that may limit the degree of upscaling which is possible. In this paper, a two-bladed downwind turbine system is upscaled from 13.2 MW to 25 MW, by redesigning aerodynamics, structures, and controls. In particular, three 25 MW rotors have been developed: V1 is the upscaled model, V2 is a partial redesigned model, and V3 is a fully redesigned model. Despite their radically large sizes, it is found that these 25 MW turbine rotors satisfy this limited set of design drivers at the rated condition and that larger blade lengths are possible with cone-wise load-alignment. In addition, flapwise morphing (varying the cone angle with a wind-speed schedule) is investigated in terms of minimizing mean and fluctuating root bending loads using steady inflow proxies for the maximum and damage equivalent load moments. The resulting series of 25 MW rotors, which are the largest ever designed, can be a useful baseline for additional development and assessment.
Numerical investigation of the influence of shear and thermal stratification on the wind turbine tip‐vortex stability
Summary The interaction between wind turbine wakes and atmospheric turbulence is characterised by complex dynamics. In this study, two major components of the atmospheric boundary layer dynamics have been isolated, namely, the mean velocity profile shear and the thermal stratification, to examine their impact on the near‐wake development by undertaking a series of highly resolved large‐eddy simulations. Subsequently, instantaneous flow fields are extracted from the simulations and used to conduct Fourier analysis and 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 indicate that shear can significantly affect the breakup of the wind turbine tip‐vortices and 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. Finally, our analysis shows that the applied perturbation frequency determines the tip‐vortex breakup location as it controls the onset of the mutual inductance instability.
Rigid-flexible coupling multi-body dynamics modeling of a semi-submersible floating offshore wind turbine
A 14-degree-of-freedom (14-DOF) flexible multibody dynamics model is developed and verified for a semi-submersible floating offshore wind turbine (FOWT). The model considers the coupled dynamics of the platform, tower, nacelle, blades, and mooring subjected to external wind and wave loads. The platform is simplified as a rigid body connected to the seabed by the mooring system. The tower and blade are modeled as flexible cantilever beams. The energy method is used to derive the governing equations of motion, where the kinetic energy, potential energy, and work done by external wind-wave forces are all deduced in a global coordinate system. The 5-MW baseline semi-submersible FOWT is used to verify the derived model against the results simulated from FAST developed by the US National Renewable Energy Laboratory (NREL) at two scenarios: free decay state and different wind-wave load cases. Results show that the established FOWT model can well reflect the vibration characteristics of FAST model. The application of the model to the control of platform pitch with a tuned mass damper is studied. Furthermore, the simplified model could provide a low-order method for the structural dynamics analysis and advanced vibration control design for the multi-body components of the semi-submersible FOWT in the future.