Development and Structural Assessment of a Novel Hybrid Mechanical Adhesive Snap-Fit Joint for Segmented Wind Turbine Blades

Abstract

Wind power has become a cornerstone technology for meeting the world’s growing demand for clean energy. To capture more power per turbine, manufacturers are building ever-larger rotors, with individual blade lengths now exceeding 100 metres. Although these longer blades boost efficiency—especially offshore—they also introduce major challenges in fabrication, assembly, transportation, and installation.

Segmenting blades into joined sections offers one potential remedy, but success depends on joints that avoid segmentation’s pitfalls: increased complexity, reduced strength, added weight, and shortened fatigue life compared with monolithic blades. Traditional techniques such as adhesive bonding and mechanical fasteners have underperformed in segmented designs, constraining commercial viability and driving the search for new solutions. Hybrid joints combining adhesive bonding with mechanical interlocks have succeeded elsewhere, yet remain untested in wind-blade segmentation.

For the first time, this thesis introduces and evaluates a novel hybrid mechanical snap-fit joint for segmented turbine blades. By merging adhesion with the mechanical interlocking of a snap-fit geometry, the design exploits tailored stiffness to steer load paths and balance structural integrity with ease of assembly. An integrated approach, spanning concept design, finite-element modelling validated by experiments, and manufacturability analysis, demonstrates the joint’s feasibility. Numerical simulations demonstrate that the joint is capable to withstand realistic blade operating loads without static failure—despite applying conservative assumptions at every step of the design process—laying a solid foundation for future optimisation and large-scale trials.

Overall, this work establishes a framework for designing and evaluating hybrid
mechanical snap-fit joints in segmented blades, offers key insights for industry adoption, and paves the way for fatigue testing and full-scale prototype development.

Date of Award	9 Dec 2025
Original language	English
Awarding Institution	University of Bristol
Supervisor	Vincent Karel Maes (Supervisor), Terence Macquart (Supervisor), Byung Chul (Eric) Kim (Supervisor) & Alberto Pirrera (Supervisor)