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IACMI Project 4.10: Blade Finishing Automation (Final Report)

IACMI Project 4.10, the Innovative Wind Turbine Blade Finishing with Advanced Automated Technology project, performed research addressing the challenges and opportunities associated with automating wind turbine blade finishing operations, including blade trimming and grinding. These activities, developed and conducted in close cooperation with wind industry partners, included: • The development of new automation technology and systems; • The integration of automation technology and components; • The improvements of sensor technology and their deployment in automation systems • The elimination of extensive manual programing for the automation of wind blade finishing; • The rethinking of the approach to specific work tasks for automation systems; • The demonstration at scale of innovative wind turbine blade finishing automations systems. The IACMI Wind TA team worked with GE Renewable Energy and LM Wind Power to establish the metrics of the automated blade finishing system developed in the project. In addition, the project team helped to integrate the advanced automated systems into wind turbine blade production facilities. This project was divided into two phases. Phase I provided project management, preliminary techno-economic model development, early US wind turbine industry outreach, and early research into robotic solutions for automated wind blade finishing. This second phase continued with project management, as well as final techno-economic model development, advanced research into robotic solutions for automated wind blade finishing, as well as specifications and procurement of a robotic system for automated wind blade finishing. The 4.10 project leveraged the capabilities and facilities established through the Institute for Advanced Composites Manufacturing Innovation (IACMI), the Colorado Office of Economic Development and International Trade (OEDIT) and the National Renewable Energy Laboratory (NREL). The IACMI Wind Technology Area (TA) designed and prepared for deployment a robotic automated wind turbine blade finishing system at the NREL/IACMI Composites Manufacturing Education and Technology (CoMET) facility. The results of this project led to follow-on funding to build upon and expand research in automated wind turbine blade finishing.

17 WIND ENERGY↗

2020 Component Innovation Awardee: Carter Wind Turbines

With support from the Competitiveness Improvement Project's Component Innovation Award, Carter Wind Energy aims to address the need for lower-cost, more-reliable distributed wind energy generation. The company plans to increase efficiency and improve the energy capture of its Carter Model 300 midsize, self-erecting wind turbine. This fact sheet provides an overview of Carter Wind Energy's project, how the company will achieve the goals of the award, and how the project fits within the overall Competitiveness Improvement Project.

CIP↗

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗

Enabling Innovation in Wind Turbine Design Using Artificial Intelligence

The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.

aerodynamics↗

Design Innovations for Deployable Wind Turbines

Deployable wind turbines have the potential to produce on-site electricity for defense and disaster relief needs, both increasing resiliency and overall energy production diversity.

17 WIND ENERGY↗

2022 Component Innovation Awardee: Carter Wind Turbines

Carter Wind Turbines will develop a competitively priced, portable, ultra-lightweight, self-erecting turbine that will expand the viability of wind energy systems for commercial and residential customers in areas far from population centers. The new turbine is a taller version of the Carter Model 300, which was developed with funding from previous Competitiveness Improvement Project (CIP) awards. Compared to conventional turbines, the new assembly is cheaper to ship, can be erected in one day without any cranes or heavy excavation equipment, and is compatible with the wide range of grid configurations and site conditions found in remote areas.

CIP↗

2021 Component Innovation Awardee: Windurance, LLC

Distributed wind turbine manufacturers seeking to enter the market are often hampered by two challenges: not enough capital and no specific expertise in developing certified electronic equipment. A harsh reality is that off-the-shelf power electronics are neither certified nor cost-effective. This lack of certified controller equipment in the distributed wind energy industry adds expense and impedes market penetration related to certification for individual wind turbines, wind system projects, and installations. Normally, these certification costs would be borne repeatedly by individual turbine manufacturers or developers on a model-by-model or project-by-project basis, resulting in cost and time delays as well as uncertainty and risk for project developers and prospective owners. Windurance seeks to eliminate these challenges by developing and obtaining third-party certification of a standardized wind turbine controller. This will facilitate development, certification, and production while supporting efficiencies not easily achievable by individual manufacturers. Windurance's Distributed Wind Industry Turbine Controller will enable manufacturers to apply proprietary turbine- specific configurations and functionality. When applicable, manufacturers can expand on a Windurance-provided software framework to add unique or proprietary functionality.

CIP↗

The Wind Turbine Rotors of the Future: A Research Agenda from the Big Adaptive Rotor Project

This white paper outlines a roadmap to inform future research efforts around the wind turbine rotors of the future. The document is authored by researchers that have been working on the Big Adaptive Rotor (BAR) and related initiatives at the National Renewable Energy Laboratory and Sandia National Laboratories. The Wind Energy Technologies Office of the U.S. Department of Energy has been funding BAR since 2018. The learnings from BAR have been documented in dozens of publications. This new paper identifies unresolved critical challenges in wind turbine rotor technology and structures them in four large topic areas: 1. Predictive and validated numerical tools. 2. Design methods and standards. 3. Improvements in manufacturing. 4. Technological innovations. Although wind turbines have not changed in their core architecture, with the market almost entirely dominated by three-bladed upwind rotors, a vast amount of research has allowed to introduce technological innovations resulting in a reduction in levelized cost of energy that made wind competitive with all traditional sources of electricity. Focusing on the rotor, blades will keep following the trend of mass and stiffness reduction, will adopt segmentation, and will keep growing in length, both land-based and offshore. To support this continuous innovation, this white paper argues that research efforts in rotor technology at the national labs are best directed to the first three topic areas and to low technology-readiness-levels (1-3) innovations, leaving the task of implementing higher-TRL (4-9) technological innovations to industry stakeholders. This research roadmap is also well aligned with the upcoming roadmap defined during the International Energy Agency 109th Topical Expert Meeting on the Grand Challenges of Wind Energy.

17 WIND ENERGY↗

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗

2021 Component Innovation Project Awardee: Bergey Windpower

In recent years, the cost of small wind turbines for homes, farms, and small businesses has decreased dramatically thanks to advances in American technology made possible by research and development support from the U.S. Department of Energy. But further reductions are needed to realize the gigawatt-scale potential of distributed wind energy to help electrify rural America, reduce carbon emissions, and create jobs. Currently, for residential- and farm-scale wind turbines with direct-drive, permanent-magnet alternators, the alternator is the highest-cost component of the wind turbine (excluding the tower). The Bergey Windpower Excel 15 wind turbine incorporates advanced technology in its rotor and controls and will soon incorporate advanced power electronics - but its alternator is based on technology that is more than a decade old. To help reduce capital expenditures of the Excel 15 wind turbine, Bergey Windpower is developing an advanced, lower- cost, permanent-magnet alternator.

CIP↗

2021 Component Innovation Awardee: Siva Powers America

Siva Powers America earned a Competitiveness Improvement Project (CIP) Component Innovation award that will enable the company to evolve its 250/50 wind turbine design, discontinue its existing 13.4-meter wind turbine blades, and develop new 14.4-meter blades. These longer blades, which have additional extenders at the hub, will increase the rotor diameter to 35 meters, allowing for improved energy production at sites with lower wind resources. A collaboration with the National Renewable Energy Laboratory (NREL) includes a redesign, build, and on-site structural test for the new prototype blades. This will ultimately allow Siva Powers America to pursue recertification of the SIVA 250/50 to meet international standards.

CIP↗

2020 Component Innovation Awardee: Windurance LLC

Currently, there is no wind system power-conversion component available to wind turbine OEMs that is certified to applicable standards for use in the U.S. distributed wind energy market. Off-the-shelf industrial drives have features that are not needed by distributed wind OEMs and lack other features that would be valuable for the operation and control of wind turbine generating systems. In addition to adding costs for OEMs, these industrial drives increase uncertainty and risk for project developers and prospective owners. All of this presents barriers and increases costs for the development of distributed wind in the United States. With funding from CIP, Windurance plans to develop an industry-specific power-conversion component that will offer application-specific features at lower cost for distributed wind OEMs.

CIP↗

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗

Introduction to and comparison of deep learning and optimization approaches to analytical wake modeling of a tilted wind turbine

This paper introduces innovative optimization and deep learning techniques to enhance the prediction of complex wake dynamics in the downstream wind velocity of tilted wind turbines. Traditional methods for calibrating the Bastankhah wake model often lead to increased errors in wind velocity distribution due to overfitting of the local wake characteristics. To address this issue, we propose an additional global optimization step to reduce errors in wind velocity predictions with respect to various wake parameters. Despite this improvement, the Bastankhah model's axisymmetric Gaussian wake shape limits its accuracy for complex wake structures. Therefore, we also propose a deep learning approach, which demonstrates promising results by accurately modeling complex wake shapes across a broader range of tilt angles with minimal computational cost. The deep learning approach achieves near-identical predictions to high-fidelity large-eddy simulations, representing a promising advancement in wake modeling.

17 WIND ENERGY↗

2020 System Optimization Awardee: Pecos Wind Power

With support from the Competitiveness Improvement Project's System Optimization Award, Pecos Wind Power will help lower the cost of distributed wind technology and expand deployment. To do so, the company will optimize their PW85 distributed wind turbine to achieve a production levelized cost of energy of $0.099 per kilowatt hour, which is a 48% cost reduction in distributed wind energy costs. This reduction is a critical step toward Pecos Wind Power's ultimate target of $0.089 per kilowatt-hour. This fact sheet provides an overview of Pecos Wind Power's project, how the company will achieve the goals of the award, and how the project fits within the overall Competitiveness Improvement Project.

CIP↗

2020 System Optimization Awardee: Carter Wind Turbines

With support from the Competitiveness Improvement Project's System Optimization Award, Carter Wind Energy aims to lower the cost of distributed wind technology and expand deployment. To do so, the company will improve the cost-effectiveness and reliability of midsize wind turbines for remote, off-grid power applications, creating new wind energy deployment opportunities worldwide. This fact sheet provides an overview of Carter Wind Energy's project, how the company will achieve the goals of the award, and how the project fits within the overall Competitiveness Improvement Project.

CIP↗

2020 Small Wind Turbine Certification Awardee: Star Wind Turbines

With support from the Competitiveness Improvement Project's Small Turbine Certification Award, Star Wind Turbines aims to earn third-party certification for their small wind turbine model STAR74-6. Certification will help instill public confidence in the product's performance and safety standards. This fact sheet provides an overview of Star Wind's project, how the company will achieve the goals of the award, and how the project fits within the overall Competitiveness Improvement Project.

CIP↗

2020 Awardee of Two Small Wind Turbine Certification and Listing Awards: QED

With support from the Competitiveness Improvement Project's Small Wind Turbine Certification and Listing awards, QED Wind Power LLC will expand their ability to manufacture and distribute their 20-kilowatt, small wind turbine model to larger national markets. QED Wind Power aims to achieve this by conducting and completing testing of their small wind turbine and achieve safety and performance certification and listing with support from the National Renewable Energy Laboratory. This fact sheet provides an overview of QEDWind Power's project, how the company will achieve the goals of the two awards, and how the project fits within the overall Competitiveness Improvement Project.

CIP↗