About the project

• Background

  Global wind power is expected to grow three times over the next decade to avoid the worst impact of climate change. The wind power plants are constructed with turbines having large rotor diameters to allow maximum energy yield from the oncoming wind. Special attention is given to reducing the cost of energy and increasing profitability, and this is anticipated to be achieved by developing optimized design, analysis, prediction, and monitoring tools capable of simulating aerodynamic flows accurately in offshore wind farms. To this end, traditional analytical tools employed to model aerodynamic flows in a wind farm are fast but include simplifications that can compromise the overall integrity of solutions. On the other hand, high-fidelity flow models based on computational fluid dynamics (CFD) are more accurate but at the same time require significantly high computational resources to obtain meaningful results.

  The Ph.D. project aims to conduct research and innovation in flow simulation in offshore wind turbines by combining high-fidelity computational fluid dynamics (CFD) models with machine learning (ML) models. The developed state-of-the-art models are expected to be integrated into future digital twin technology to fulfill the industrial needs of computationally efficient tools and form a basis for condition monitoring, prediction, analysis, and maintenance of offshore wind turbines. Key research areas that will be exploited in the Ph.D. research project will be physics-based and data-driven Reduced Order Models (ROM) to simulate and predict wind farm flows, especially in the wake region. make it shortes as a background to project introduction

• Objective

  The iCOWE project aims to revolutionize offshore wind farm operational management by developing innovative methods and systems that harness digital technologies. Central to this effort is the creation of a sophisticated toolbox that enables intelligent simulations for more accurate wind energy performance prediction, significantly reducing the need for human intervention. The project seeks to advance the use of digital twin technology, driving transformative research that redefines product lifecycle management within the offshore wind industry. By enabling smarter control and predictive capabilities for wind turbine operations, iCOWE aims to lower operational costs, enhance efficiency, and contribute to the sustainable growth of offshore wind energy.

• Key research findings

  ✅ Validated Simulation Accuracy: Numerical results using IDDES showed strong agreement with experimental data, confirming the reliability of the blade-resolved CFD approach.

  🌪️ Significant Wake Recovery with Roll Motion: Roll-induced platform motion enhanced turbulence and accelerated wake recovery, reducing wake deficit by up to 50% at 10 rotor diameters (10D) compared to the stationary case.

  🔁 Effect of Platform Motion Type: While pitch motion led to moderate improvements, roll motion had a more substantial impact on wake mixing and recovery, due to its rotational nature disrupting the flow more effectively.

  📈 Amplitude Influence: Increasing motion amplitude caused earlier onset of coherent wake structures—by 34% for pitch and 42% for roll—demonstrating that higher amplitudes can enhance downstream flow regeneration.

  📊 Wake Structure Insights: Probability density function (PDF) analysis revealed that roll motion widens the lateral wake, increasing turbulence, while pitch motion produces a more balanced and stable wake profile both laterally and vertically.

• Funding

  This project has received funding from the Internal PhD Scheme at Norwegian University of Life Sciences.

• Participants

  

  Arvind Keprate

  Professor at OsloMet

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Publications

Highlights