Forschungsprojekt
Formal workflows for intelligent digital twins in wind energy
Wind energy is a key technology for reaching global net zero goals. Intelligent digital twins (DT) are particularly important for the wind energy sector due to their potential to enhance decision-making during operation of large-scale fleets; however, intelligent DTs require further research and development, especially with respect to workflow implementation. The goal of this SNF project is to investigate the application of formal workflows for intelligent digital twins (DTs) in wind energy. This will be done by focusing on the following four objectives:
- Objective 1: Study and propose a high-level characterisation of workflows for intelligent DTs in the wind energy context. This will result in a concept diagram for workflows at high abstraction level for a leading edge erosion (LEE) use case - a critical topic in wind energy research - providing wind energy researchers and modellers working on LEE with a common basis for creating their DT instances, which will help them share experiences and also share data and results between instances, and provides teachers with a specific use case for giving examples of DT applications. Further, a concept diagram for other use cases will provide wind energy researchers and modellers working on any given topic with a common basis for creating their DT instances, which will help them share experiences and also share data and results between instances.
- Objective 2: Develop methods for implementing workflows for intelligent DTs in the wind energy context. This will provide wind energy researchers and modellers in the industry, as well as any other digital twin creator interested in intelligent DTs (especially where dynamics are important), with new methods, insights and examples for effectively integrating various system components, for efficiently implementing them in a sound and robust manner, and for expanding existing data storage and management systems to support workflow processes by enriching them with essential semantics.
- Objective 3: Create an intelligent DT instance for the LEE use case and investigate the benefits of applying the workflow methods. This will provide wind energy researchers and modellers in the industry, as well as any other DT creator interested in intelligent DT (especially where dynamics are important), with a basis on which to build their own intelligent and interoperable DTs, as well as with the possibility to enter their own data into the system and improve their decision-making in their own projects. Further, recommendations for future improvements to the intelligent DT instance will provide wind energy researchers and modellers in the industry, as well as any other DT creator interested in intelligent DTs, with key insights into the challenges of building such DTs, as well as possible solutions.
- Objective 4: Propose a methodology to help domain experts implement intelligent DTs in the context of wind energy. This will provide creators and users of intelligent DTs with a methodology to implement and use intelligent DTs effectively, taking into account possible technical, social and environmental factors, and it will provide researchers in transdiscplinary collaboration methods with an example of how to apply their methods in practice, helping them to refine and improve their methods, and it will provide teachers of wind energy and other related topics such as structural dynamics, mechanics, materials science and meteorology with a basis for effectively integrating domain knowledge with computer science, helping the new generation of domain experts to effectively implement digital and AI-driven technologies.
Laufzeit: 01.01.2026 - 31.12.2029
Projektfinanzierung:
SNF
Projektteam:
Dr. Sarah Barber
IET Institut für Energietechnik Fachbereichsleiterin Wind Energy Innovation
+41 58 257 42 62sarah.barber@ost.ch
Yuriy Marykovskiy
IET Institut für Energietechnik Wissenschaftlicher Mitarbeiter
+41 58 257 13 69yuriy.marykovskiy@ost.ch
