Architecting a digital twin for wind turbine rotor blade aerodynamic monitoring

Abstract

Digital twins play an ever-increasing role in maximising the value of measurement and synthetic data by providing real-time monitoring of physical systems, integrating predictive models and creating actionable insights. This paper presents the development and implementation of the Aerosense digital twin for aerodynamic monitoring of wind turbine rotor blades. Employing low-cost, easy-to-install microelectromechanical (MEMS) sensors, the Aerosense system collects aerodynamic and acoustic data from rotor blades. This data is analysed through a cloud-based system that enables real-time analytics and predictive modelling. Our methodological approach frames digital twin development as a systems engineering problem and utilises design patterns, design thinking, and a co-design framework from applied category theory to aid in the development process. The paper details the architecture, deployment, and validation of a ‘Digital Shadow’-type twin with simulation/prediction functionalities. The solution pattern is discussed in terms of its implementation challenges and broader applicability. By providing a practical solution to integrating all the digital twin components into a holistic system, we aim to help wind energy specialists learn how to transform a conceptual idea of a digital twin into a functional implementation for any application.