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1. A Digital Process for Reconstructing Wind Turbine Blade Geometry from Point Cloud Data

   Kiernicki, C.; Kakkad, S.; Bermek, M.; Gentry, R. (July 2022, 5th International Conference on Structures and Architecture)

   The first generation of wind turbines are being retired, and a tremendous number of wind tur-bine blades are coming out of service. Architects and engineers are developing re-use ideas for these blades and are wrestling with their complex geometries and materiality. This paper details a four-phase process for reconstructing the geometry of wind turbine blades, starting from a point-cloud scan and finishing with a digital model that represents the blade and its associated properties. The process builds on earlier work that created an airfoil database to store the nor-malized coordinates of publicly available airfoil profiles. This profile database is traversed to match airfoil shapes to cross-sections found in the point-cloud. Root, transition, and airfoil shapes are matched to cross-sections along the full blade to reconstruct the outer geometry. Based on data from the interior of the blade, the structural spar box is reconstructed. The addi-tion of thickness and material property data allows for calculation of section properties at multi-ple stations along the blade. The resulting 3D geometry and the associated data is used for ar-chitectural design and engineering calculations to develop second-life applications for wind blades. The paper demonstrates the workflow through examples from a GE 37-meter blade and an LM 13.4-meter blade. 

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2. Aerodynamic rotor design for a 25 MW offshore downwind turbine

   https://doi.org/10.1016/j.apenergy.2023.122035  

   Jeong, Michael; Loth, Eric; Qin, Chris; Selig, Michael; Johnson, Nick (January 2024, Applied Energy)

   Continuously increasing offshore wind turbine scales require rotor designs that maximize power and performance. Downwind rotors offer advantages in lower mass due to reduced potential for tower strike, and is especially true at large scales, e.g., for a 25 MW turbine. In this study, three 25 MW downwind rotors, each with different prescribed lift coefficient distributions were designed (chord, geometry, and twist) and compared to maximize power production at unprecedented scales and Reynolds numbers, including a new approach to optimize rotor tilt and coning based on aeroelastic effects. To achieve this objective the design process was focused on achieving high power coefficients, while maximizing swept area and minimizing blade mass. Maximizing swept area was achieved by prescribing pre-cone and shaft tilt angles to ensure the aeroelastic orientation when the blades point upwards was nearly vertical at nearly rated conditions. Maximizing the power coefficient was achieved by prescribing axial induction factor and lift coefficient distributions which were then used as inputs for an inverse rotor design tool. The resulting rotors were then simulated to compare performance and subsequently optimized for minimum rotor mass. To achieve these goals, a high Reynolds number design space was developed using computational predictions as well as new empirical correlations for flatback airfoil drag and maximum lift. Within this design space, three rotors of small, medium and large chords were considered for clean airfoil conditions (effects of premature transition were also considered but did not significantly modify the design space). The results indicated that the medium chord design provided the best performance, producing the highest power in Region 2 from simulations while resulting in the lowest rotor mass, both of which support minimum LCOE. The methodology developed herein can be used for the design of other extreme-scale (upwind and downwind) turbines. 

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3. Digital Twin – Driven Machine Learning Optimization Framework for Multizone Curing Control in Wind Turbine Blade Manufacturing

   https://doi.org/10.5194/wes-2025-226  

   Kamath, Sahil; Adab, Niloufar; Srivastava, Rajat; Mao, Zhiqi; Mehrdad, Ehsan; Liu, Yingjian; Nolet, Stephen; Wilson, Joseph; Chen, Xu; Lu, Hongbing; et al (November 2025, Copernicus Publications)

   Abstract. The Vacuum-Assisted Resin Infusion Molding (VARIM) process is widely used in wind turbine blade manufacturing due to its cost-effectiveness and reliability. However, challenges such as prolonged curing cycles and defects caused by non-uniform cure remain persistent. To address these issues, multizone heating systems have been developed to enable independent temperature control across blade sections. Yet, optimizing the temperature profile in each zone is computationally intensive, requiring detailed modelling of curing kinetics and heat transfer mechanisms. To overcome these challenges, in this work, a machine learning (ML) based digital twin of the VARIM process was developed using a time-distributed long short-term memory (LSTM) network trained on data generated by a high-fidelity multiphysics solver. The model achieved a predictive accuracy of 96.7 % in replicating the resin curing behavior. Its time-distributed architecture effectively captures the spatial – temporal dependencies across multiple zones, allowing precise prediction of the degree-of-cure evolution. Paired with a gradient-free optimization algorithm, the digital twin reduced curing time by 12.5 % while improving cure uniformity. This AI-driven framework eliminates costly trial-and-error experimentation, and provides a scalable, adaptive solution for improving both quality and productivity in wind turbine blade manufacturing, with strong potential for extension to other composite manufacturing processes. 

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4. BladeBridge - Design and Construction of a Pedestrian Bridge using Decommissioned Wind Turbine Blades

   Zhang, Z.; Ruane, K.; Huynh, A.; McDonald, A.; Leahy, P.; Alshannaq, A.; Gentry, R.; Nagle, A.; Bank, L. (July 2022, 5th International Conference on Structures and Architecture)

   The purpose of this study was to develop a replicable methodology for testing the capabilities and characteristics of a wind turbine blade in a structural re-use application with the specific goal of creating and demonstrating an efficient and commercially viable wind blade pedestrian bridge design. Wind energy experienced a dramatic increase in popularity following the turn of the century and it is now a common source of renewable energy around the world. However, while wind turbines are able to produce clean energy while in service, turbine blades are designed for a fatigue life of only about 20 years. With the difficulty and costs associated with recycling the composite material blades used on the turbines, wind power companies choose to dispose of decommissioned blades in landfills instead. The Re-Wind BladeBridge project aims to promote a more sustainable life cycle for wind power by demonstrating that decommissioned wind turbine blades have the capability to be repurposed as structural elements in bridges. This paper presents an analysis and characterization of a LM 13.4 wind blade from a Nordex N29 turbine, along with a design for a pedestrian bridge using two LM 13.4 wind blades to create a 5-meter span bridge. Software developed by the Re-Wind team called “BladeMachine” was used to generate the engineering properties of the blade at multiple sections along the blade length. Resin burnout tests and mechanical testing in tension and compression were performed to determine the material and mechanical properties of the composite materials in the blade. Additionally, a four-point edgewise bending test was performed on a 4-meter section of the wind blade to evaluate its load carrying behavior. The results of these tests revealed that the LM 13.4 blades are suitable to be re-utilized as girders for a short-span pedestrian bridge. An overview of the design of the BladeBridge currently under construction in County Cork, Ireland is presented, including details on the architectural and structural design processes. 

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5. Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

   https://doi.org/10.1002/we.2917  

   Mata, Storm A; Pena-Martínez, Juan José; Bas-Quesada, Jesús; Palou-Larrañaga, Felipe; Yadav, Neeraj; Chawla, Jasvipul S; Sivaram, Varun; Howland, Michael F (June 2024, Wind Energy)

   Wind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production. 

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