Data-Driven Approaches in Computational Mechanics for the Aerohydroelastic Analysis of Offshore Wind Turbines
DATA-DRIVEN OFFSHORE aims to enhance offshore wind turbine design by integrating experimental data into aerohydroelastic simulations, improving predictive capabilities and enabling efficient upscaling beyond 20 MW.
Projectdetails
Introduction
A massive upscaling of offshore wind turbines is necessary to reach the goals of the European Green Deal. However, current methodologies for analysis and design are at their limits.
Challenges in Current Methodologies
One of the major bottlenecks is that it is so far not possible to directly integrate experimental data into aerohydroelastic simulations of offshore wind turbines. By and large, including these data into the aerohydroelastic analysis is indirectly accomplished through the offline adjustment of those parameters that define instances of existing models.
Although such a practice can reasonably improve the short-time predictive capability, the underlying models remain unmodified. Thus, further physics available in the data remains inaccessible. In the numerical simulation context, this represents a main challenge to taking advantage of the experimental data in their entirety.
Proposed Solution: DATA-DRIVEN OFFSHORE
In this context, DATA-DRIVEN OFFSHORE proposes to simultaneously integrate these highly valuable data into aerohydroelastic simulations through data-driven computational mechanics. Such an approach is one of the most advanced computing frameworks and relies on the reformulation of classical boundary and initial value problems in solid and fluid mechanics such that:
- Constitutive models
- Boundary conditions
- Applied loads
are directly replaced by some form of experimental data.
Expected Outcomes
DATA-DRIVEN OFFSHORE will thus enable, for the first time, the investigation of the aerohydroelastic behavior of an offshore wind turbine relying truly on experimental data, capturing the hidden features that these contain.
This will:
- Greatly improve the predictive capabilities with respect to existing models
- Allow the conception of less-conservative designs
- Enable upscaling beyond 20 MW of rated power
- Increase efficiency while reducing the cost per unit of power produced
Thus, it will contribute to triggering a change of paradigm for future generations of offshore wind turbines.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.000.000 |
Totale projectbegroting | € 2.000.000 |
Tijdlijn
Startdatum | 1-4-2023 |
Einddatum | 31-3-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- UNIVERSITETET I BERGENpenvoerder
Land(en)
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This project aims to develop and validate a theory for intermediate-strain turbulence using machine learning and advanced simulations to enhance engineering applications like wind energy and UAV efficiency.
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Vergelijkbare projecten uit andere regelingen
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Sustainable Wind Turbines Software (SWTS)Dit project onderzoekt hoe data en AI kunnen worden ingezet om de kosten van offshore windturbines te verlagen, hun levensduur te verlengen en de elektriciteitsopbrengst te verbeteren. | Mkb-innovati... | € 18.760 | 2020 | Details |
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GET: DIRECTE WINDTURBIE BLADMETINGENTarucca ontwikkelt een continu monitoringssysteem voor windturbines met fotonische sensoren en machine learning om efficiëntie en opbrengst te verhogen. | Mkb-innovati... | € 20.000 | 2022 | Details |
Optimized Calibration of Windpark Energy (Opti- Wind)OPTECS ontwikkelt een AI om de energie-output en kwaliteit van offshore windparken te verbeteren, met als doel de duurzame energieproductie in Nederland te verhogen. | Mkb-innovati... | € 20.000 | 2022 | Details |
Hydrofoil Offshore Wind - HOWFlying Fish onderzoekt de haalbaarheid van een nieuw type Zero Emissie Crew Transfer Vessel met draagvleugels voor offshore windparken, resulterend in een rapport voor verdere ontwikkeling. | Mkb-innovati... | € 20.000 | 2022 | Details |
Sustainable Wind Turbines Software (SWTS)
Dit project onderzoekt hoe data en AI kunnen worden ingezet om de kosten van offshore windturbines te verlagen, hun levensduur te verlengen en de elektriciteitsopbrengst te verbeteren.
Ontwikkeling van een Wave Predictor en Wave Analyzer
Dit project richt zich op het verbeteren van de voorspelbaarheid van golfhoogtes in de offshore windenergie om risico's te verlagen en de inzetbaarheid van schepen te vergroten.
GET: DIRECTE WINDTURBIE BLADMETINGEN
Tarucca ontwikkelt een continu monitoringssysteem voor windturbines met fotonische sensoren en machine learning om efficiëntie en opbrengst te verhogen.
Optimized Calibration of Windpark Energy (Opti- Wind)
OPTECS ontwikkelt een AI om de energie-output en kwaliteit van offshore windparken te verbeteren, met als doel de duurzame energieproductie in Nederland te verhogen.
Hydrofoil Offshore Wind - HOW
Flying Fish onderzoekt de haalbaarheid van een nieuw type Zero Emissie Crew Transfer Vessel met draagvleugels voor offshore windparken, resulterend in een rapport voor verdere ontwikkeling.