Presentation Information
[Poster 1-06]Environmental Digital Twin Framework for Offshore Wind Farms
*Hamid Afshari1 (1. Dalhousie University (Canada))
Digital twins (DT) are increasingly being adopted to support the planning, operation, and management of offshore wind farms (OWFs). While most existing DTs focus on the structural integrity and operational performance of turbines, comparatively little attention has been devoted to representing the complex bidirectional interactions between OWFs and their surrounding marine and atmospheric environments. We propose a conceptual framework for an environmental digital twin that captures both the influence of environmental conditions on wind turbine performance and degradation, and the impacts of wind farm operations on local atmospheric and oceanographic processes.
The proposed DT integrates physics-based simulation models with continuous streams of observational data to maintain an up-to-date representation of the coupled wind farm–environment system. Environmental inputs may include meteorological measurements, oceanographic observations, and ecological indicators. These data may be acquired through a combination of turbine-mounted sensors, oceanographic buoys, autonomous surface and underwater vehicles, satellite remote sensing, and aerial drones. Data assimilation techniques would serve to continuously calibrate and update numerical models, ensuring that the DT remains representative of current conditions.
The resulting framework provides a platform for advancing scientific understanding of the interactions between OWFs farms and the surrounding environment. By integrating diverse environmental datasets with dynamic simulation models, the DT enables improved assessment of wake effects, hydrodynamic changes, sediment transport, underwater noise propagation, water quality, and potential ecological responses under varying operational and environmental conditions. This capability can support cumulative environmental impact assessments, improve the transparency and traceability of environmental monitoring, and provide regulators, industry, researchers, and coastal communities with a common evidence base for evaluating project performance. As a living representation of an OWF, the environmental DT has the potential to strengthen stakeholder confidence, facilitate more informed decision-making, and contribute to the social licence necessary for the sustainable development and long-term acceptance of offshore wind energy.
The proposed DT integrates physics-based simulation models with continuous streams of observational data to maintain an up-to-date representation of the coupled wind farm–environment system. Environmental inputs may include meteorological measurements, oceanographic observations, and ecological indicators. These data may be acquired through a combination of turbine-mounted sensors, oceanographic buoys, autonomous surface and underwater vehicles, satellite remote sensing, and aerial drones. Data assimilation techniques would serve to continuously calibrate and update numerical models, ensuring that the DT remains representative of current conditions.
The resulting framework provides a platform for advancing scientific understanding of the interactions between OWFs farms and the surrounding environment. By integrating diverse environmental datasets with dynamic simulation models, the DT enables improved assessment of wake effects, hydrodynamic changes, sediment transport, underwater noise propagation, water quality, and potential ecological responses under varying operational and environmental conditions. This capability can support cumulative environmental impact assessments, improve the transparency and traceability of environmental monitoring, and provide regulators, industry, researchers, and coastal communities with a common evidence base for evaluating project performance. As a living representation of an OWF, the environmental DT has the potential to strengthen stakeholder confidence, facilitate more informed decision-making, and contribute to the social licence necessary for the sustainable development and long-term acceptance of offshore wind energy.
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