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ResearchProjects

iCOWE Project

The project is focused on enhancing the efficiency of offshore wind energy management by integrating physics-based and data-driven methods, including Machine Learning and Artificial Intelligence. It aims to develop models that can provide predictions much faster than traditional Computational Fluid Dynamics (CFD) methods, enabling real-time monitoring and management of offshore wind farms.

01 Jan 2023 - 31 Dec 2026

Norwegian University of Life Sciences (Internal funding)

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 advance flow simulation for offshore wind turbines by integrating high-fidelity computational fluid dynamics (CFD) models with machine learning (ML) methods. The developed state-of-the-art models are expected to be integrated into future digital twin technology to meet industrial needs for computationally efficient tools and to serve as a basis for condition monitoring, prediction, analysis, and maintenance of offshore wind turbines. Key research areas to be explored in the Ph.D. project will include physics-informed and data-driven surrogate model to simulate and predict the wake flow of a floating offshore wind turbine.

  • Objective

    The iCOWE project aims to revolutionize offshore 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 to predict wake flow around floating wind turbines. 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.

    ⚡ Energy-Transport Perspective on Wake Recovery: An energy-budget decomposition showed that differences in wake recovery are governed primarily by the direction and spatial organization of advective kinetic-energy transport, rather than turbulence increase alone, providing a new physics-based diagnostic framework for motion-affected FOWT wakes.

  • Funding

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

  • Participants
    Arvind Keprate

    Arvind Keprate

    Professor at OsloMet

Timeline

03 October 2025: Research Article published: Coherent flow structures in the wake of a model floating wind turbine under pitch and roll motions

This research numerically studies the wake dynamics of a model FOWT under platform motion using high-fidelity CFD. It was found that higher oscillation amplitudes shift coherent wake structures closer to the rotor. The pitch motion drives pulsating wakes at low Strouhal numbers, shifting to meandering at high Strouhal numbers, whereas the rolling motion causes vortex merging, thereby increasing lateral spreading and mixing. Also, the optimal Strouhal number range (
  0.3–0.6) enhances wake recovery and aids wind farm design.

25 July 2025: Research Article published: Advances in computational intelligence for floating offshore wind turbines aerodynamics: Current state review and future potential

Our review article has been published in Renewable and Sustainable Energy Reviews (Elsevier). This journal is ranked Level 2 in Norway and is highly recognized in the field of renewable energy research. In this work, we provide a comprehensive overview of computational methods for floating offshore wind turbines (FOWTs), from low-fidelity engineering models to high-fidelity CFD approaches. We also highlight recent advances in data-driven modeling and AI applications for improving FOWT design and performance. The article further discusses the impact of wake aerodynamics, validation efforts, and the future opportunities for integrating AI and physics-based models in wind energy research. This publication is the result of strong collaboration across multiple institutions, and we hope it contributes to advancing sustainable offshore wind energy technologies.

9-12 June 2025: Presented research work at the WAKE conference in Visby Sweden

The iCOWE project was represented at the Wake Conference 2025, held at Uppsala University’s Gotland Campus in Visby, Sweden. Haris Hameed Mian presented the paper “Nonlinear Wake Dynamics of a Model Floating Offshore Wind Turbine Under Pitch and Roll Motions,” which explores the influence of platform-induced motions on wake behavior. The study uses advanced CFD simulations with an Improved Delayed Detached Eddy Simulation (IDDES) approach to analyze how pitch and roll dynamics affect turbulence, wake recovery, and downstream wind conditions. The findings provide valuable insights for optimizing floating wind farm layouts and support the objectives of the iCOWE project. The work is now published in the IOP Journal of Physics: Conference Series and reflects ongoing efforts to advance high-fidelity modeling techniques for offshore wind energy systems.

15-17 Janunary 2025: Presented posters in ERRA DeepWind 2025

Presented two posters at DeepWind 2025, showcasing cutting-edge advancements in wind farm optimization. The first poster focuses on Wind Farm Layout Optimization, where a machine learning-enhanced wake modeling approach, combined with SLSQP optimization, improves energy output at the Horns Rev 1 wind farm. The second poster explores Leveraging Data-Driven Techniques with LiDAR and SCADA Data, applying XGBoost and Bi-LSTM to analyze offshore wind farm performance, offering valuable insights into wind speed predictions and turbine efficiency. These studies highlight the power of data-driven methodologies in enhancing renewable energy solutions.

1st June 2024: Completed three-month research stay at Fraunhofer IWES, Oldenburg, Germany

During his time, the research was conducted on advancing and refining floating offshore wind turbine modeling and simulation using OpenFOAM, contributing to the cutting-edge research in this critical area of renewable energy technology. The financial support for this was provided from the internal funding scheme at the Norwegian University of Life Sciences (project number 1211130114).

Publications

Highlights

H H Mian, RSER Journal (2025)
Wake of Floating Wind Turbine
Rotor_Pitch_motion
Moored FOWT Floater
FOWT Floater Mesh
A neural network-based motion prediction of semi-submersible floating platform for offshore wind
Hydrodynamic Response of Semi-Submersible FOWT Floaters: A Numerical Investigation of Wave and Mooring Parameter Dependencies
Aerodynamic and Structural Assessment of Floating Wind Turbines Under Pitch Motion
Numerical investigation of blade roughness impact on the aerodynamic performance and wake behavior of horizontal axis wind turbine