Inalia

Offshore Wind

SIMEWind

SIMEWind is Inalia´s commercial Structural Health Monitoring (SHM) system for Floating Offshore Wind Turbines (FOWT) that is being developed to overcome limitation such us sensor durability, monitoring costs and data quantity.

It offers two versions: SIMEWind 1st UNIT is the customised structural monitoring system for FOWT demonstrative units, and SIMEWind SHM, is a scalable monitoring system designed for deployment in commercial offshore wind farms.

Can be integrated into barge, semi-submersible, spar or TLP type substructures; concrete, steel or hybrid.

SIMEWind Modules

Mooring lines

Floating structure

Dynamic cable

Tower interface

WHY INALIA

Our Monitoring and Analysis Tools

SIMEWind combines structural data sensors, meteorological -meteocean data, virtual models and machine learning algorithms.

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Data acquisition: Installation of appropriate sensors and equipment

Fiber optics (FBG, DSS, DTS), accelerometers, IMU sensors and equipment.

Inalia has experience and knowledge of sensors and protections in marine environments. Inalia has installed and tested different types of sensors and protections in submerged and splash zones of the #Harslab floating laboratory to analyze their durability in harsh environments.

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Data analysis

Interpretation and analysis of the data by specialist in physical-virtual modelling implementing real data (digital twins). Orcaflex hydrodynamic modelling, Cradle CFD fluidodynamic virtual models, and structural FEM models. 

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Virtual modelling

Orcaflex hydrodynamic modelling, Cradle CFD fluidodynamics, structural Finite Element modelling MSC. Hexagon Nastran – Marc-.

Virtual models are useful to analyze the asset structural and hydrodynamic performance and define the appropriate monitoring strategy.  Inalia also integrates virtual model in data interpretation phases.

Data Machine learning & AI analysis

Inalia integrates Machine Learning and AI tools in long monitoring campaigns to process data, identify patterns, make predictions, generate insights, and anticipate structural diagnoses for predictive maintenance.

Proyecto subvencionado por el CDTI y financiado por la Unión Europea – NextGeneration EU