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Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.

• Wind Energy
• Turbulent Flow Simulation
• Flow Control & Optimization
• +5 more

Wout Weijtjens is a Research Fellow at Vrije Universiteit Brussel, affiliated with the Acoustics & Vibration Research Group in Applied Mechanics. His research focuses on structural health monitoring (SHM) of offshore wind turbines, fatigue analysis, and vibration-based damage detection using advanced signal processing and machine learning techniques. Current projects include FIRMEST (fatigue assessment of offshore wind turbine substructures) and FOOS (Forced Oscillations in turbines). His research interests span: Operational modal analysis for offshore structures Machine learning applications in SHM Fatigue life prediction under environmental variability Sensor networks for infrastructure monitoring Wind turbine dynamics under harsh conditions Recent publications demonstrate a consistent focus on developing predictive maintenance frameworks through multivariate sensor data analysis, uncertainty quantification in SHM systems, and validation of computational models against full-scale field measurements. Article trends emphasize machine learning integration with physical models for improved fatigue life assessment. Awards and recognitions include: Best Paper Award (2nd place, 2022) Poster Award (2017) Solvay Award (2015) As principal investigator on multiple grants including VLADBC7 and VLADBC9 projects, he supervises PhD candidates in vibration-based SHM and leads experimental validation at OWI-Lab's Large Climate Chamber. His team develops IoT monitoring solutions for civil infrastructure through the SMART TOWERS initiative.

• Structural Health Monitoring
• Offshore Wind Turbines
• Fatigue Analysis
• +2 more

Lesley De Cruz is a Research Fellow at Vrije Universiteit Brussel's Electronics and Informatics department within the School of Electronics and Computer Science, with concurrent research affiliation at the Royal Meteorological Institute of Belgium since 2012. Current projects include climate-resilient urban modeling through BRGEOZ464 and flood prediction systems under FWOAL1127. Research focuses on AI-driven climate modeling with expertise in nonlinear dimension reduction (DIRESA framework), urban climate resilience, and renewable energy meteorology. Key methodologies integrate deep learning with environmental physics for applications including real-time flood prediction offshore wind farm optimization LEGO-based urban climate prototyping regional climate downscaling Recent publications (2023-2025) demonstrate strong trends in geospatial AI for environmental systems, with 64% focusing on climate change impact analysis, 54% on urban applications, and 51% on control measure development. The DIRESA framework represents a significant contribution to nonlinear dimension reduction in climate informatics. Awards include: Matìère Grise Science Communication Trophy (2024) Major media recognition for breakthrough rainfall prediction model (2021) Supervises multiple PhD candidates and leads public engagement initiatives including CurieuCity and Dag Van de Wetenschap. Current grants total 12 active projects (2021-2029) with €2.8M+ funding, primarily focused on climate adaptation and sustainable technology development. Labs and teams include the Climate Informatics Group at VUB and collaborative networks with Ghent University and the Flemish Institute for Carbon-Aware Technologies.

• Climate Modeling
• Regional Climate Downscaling
• AI for Environmental Systems
• +3 more

Prof. Damien Ernst is a faculty member at the Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liège (Belgium). His research focuses on energy systems, remote renewable energy hubs, reinforcement learning, and optimization algorithms. He actively contributes to decarbonization strategies for hard-to-abate sectors like shipping and aviation. Academic Rank: Professor University: University of Liège School: Montefiore Institute Department: Electrical Engineering and Computer Science Research interests include: Design and techno-economic analysis of remote renewable energy systems Reinforcement learning applications in energy and optimization Energy transition policy and sustainability modeling Machine learning techniques for energy system transparency Integration of nuclear, hydrogen, and battery technologies His recent publications demonstrate expertise in: Remote Renewable Energy Hub (RREH) design Small Modular Reactor (SMR) integration Asymmetric Actor-Critic algorithms Energy system optimization and modeling Grid stability and hosting capacity analysis Explainable AI for closed-domain question answering Prof. Ernst leads PhD projects at the intersection of Operations Research and Energy Systems, focusing on energy systems models' transparency and optimization techniques.

• Energy Systems
• Operations Research
• Remote Renewable Energy Hubs
• +5 more

Pieter-Jan Daems is a postdoctoral researcher at Vrije Universiteit Brussel's Engineering Technology department, specializing in the Acoustics & Vibrations Research Group. With an h-index of 6, his work focuses on wind turbine dynamics and machine learning applications in renewable energy systems. Active in wind turbine research (100% focus) Specializes in modal analysis (78%) and farm-wide monitoring (35%) Expertise in condition monitoring (33%) and damping analysis (19%) Engages with machine learning (22%) for anomaly detection (19%) His recent publications demonstrate a multidisciplinary approach combining mechanical engineering, data science, and renewable energy systems. Key research themes include turbine-level power prediction, wake loss analysis, and drivetrain dynamics investigation. While no formal awards are listed, his collaborations with university researchers and industry experts indicate strong academic engagement.

• Wind Energy
• Acoustics
• Vibration Analysis
• +3 more

Pietro D'Antuono is a postdoctoral researcher affiliated with the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on fatigue analysis, structural health monitoring, and offshore wind turbine systems. He has contributed to advancing methodologies for data-driven lifetime assessment of support structures and foundations, integrating physics-informed machine learning and probabilistic modeling. His work bridges mechanical engineering, civil engineering, and renewable energy systems. Research Interests: Offshore wind turbine structural dynamics Fatigue and damage mechanics Data-driven predictive models Structural health monitoring Materials degradation analysis Key Contributions: Developed the py-Fatigue open-source toolbox for fatigue assessment. Authored 24+ peer-reviewed publications and datasets on structural reliability and offshore energy systems. Awards: Best Paper Award (2nd place, ex-aequo) at an international conference (2022). Professional Activities: Presenter at conferences on topics like fatigue modeling, data-driven methodologies, and structural health monitoring. Contributed to international research collaborations in Europe and beyond.

• Offshore Wind Turbines
• Structural Health Monitoring
• Fatigue Analysis
• +5 more

Georgios Anogiatis serves as Associate Professor at KU Leuven's Faculty of Engineering Technology, Department of Civil Engineering, based at the Bruges Campus. He heads the Hydraulics and Geotechnics subdivision and holds key committee roles including membership in the Council of the Faculty of Engineering Technology, Civil Engineering Department Council, and Faculty Council for International Policy. His research centers on soil-structure interaction dynamics, specializing in pile foundation behavior under seismic and cyclic loading. He develops continuum analytical models for geotechnical systems including retaining walls and embedded foundations, with emphasis on wave propagation in layered soils and p-y/t-z curve development. Current work integrates physics-informed neural networks and probabilistic methods to address offshore wind turbine foundation challenges. Recent publications (2023-2025) reveal a concentrated focus on offshore renewable energy infrastructure, particularly soil-pile interaction mechanisms in inhomogeneous soils, cyclic foundation response, and computational efficiency enhancements. Key themes include decommissioning strategies for wind turbine foundations, sediment bed system development for ocean basin testing, and probabilistic performance assessment under seabed variability. He leads multiple major research initiatives including the €2.1M ATHENA project (2022-2026) on analytical programming efficiency and the 2024-2029 decommissioning strategies project for offshore wind foundations. His grant portfolio demonstrates strong industry-academia collaboration in offshore renewable energy validation and coastal engineering applications. Anogiatis directs research activities at KU Leuven's Bruges Campus Hydraulics and Geotechnics group, leveraging the Coastal & Ocean Basin facility for experimental validation of offshore foundation systems. His team specializes in centrifuge testing, wave basin experiments, and computational modeling for dredging, coastal protection, and offshore renewable energy infrastructure.

• Geotechnical Engineering
• Soil-Structure Interaction
• Pile Foundations
• +4 more

Nicole Van Lipzig is a full professor at the Department of Earth and Environmental Sciences, Faculty of Science, KU Leuven. Her research focuses on climate change impacts, wind energy meteorology, and mesoscale atmospheric processes. She leads multiple long-term projects spanning 2022-2028, including wind farm co-design in the North Sea and climate adaptation strategies in Belgium. Faculty of Science Advisory Committee Chair KU Leuven Institute for Energy and Society (KIES) member Urban Studies Institute (LUSI) member Her sabbatical project (2023-2025) involves collaborations with global institutions like Technical University of Denmark and NREL, aiming to strengthen wind energy research through industry partnerships and academic exchanges. Current students include Borgers, R., Jamaer, S., and Neirynck, J.

• Climate Change Impact Modeling
• Wind Energy Meteorology
• Mesoscale Atmospheric Processes
• +3 more

Yacine Bel-Hadj is a researcher at the Vrije Universiteit Brussel within the Acoustics & Vibrations Research Group under the Faculty of Engineering Sciences. She holds a doctoral degree in Applied Mechanics and is actively involved in structural health monitoring (SHM) research. Her work focuses on anomaly detection, deep learning applications, and vibration-based monitoring for critical infrastructure like transmission towers, wind turbines, and bio-composite bridges. Research Interests: Her expertise includes Structural Health Monitoring (SHM) systems for civil infrastructure Machine learning in anomaly detection and damage localization Modal analysis and vibration-based diagnostics Environmental adaptation in SHM Smart materials and sustainable engineering solutions Publications: Her recent work emphasizes innovative methods such as self-supervised learning frameworks, automated operational modal analysis (OMA), and population-based monitoring strategies. She has published extensively in journals like Structural Health Monitoring and conference proceedings. Awards/Grants: No specific awards mentioned, but her research has been supported through institutional grants and collaborations. She served as an editorial board member for Wind Energy (May 2023–May 2024). Labs/Teams: Primary affiliation with the Acoustics & Vibrations Research Group, collaborating on projects involving fiber-optic sensing, bio-composite materials, and offshore wind turbine monitoring.

• Structural Health Monitoring
• Anomaly Detection
• Modal Analysis
• +4 more

Rebeca Marini is an academic affiliated with the Faculty of Engineering at Vrije Universiteit Brussel (VUB), Belgium. She is part of the Engineering Technology Acoustics & Vibration Research Group and holds the position of Researcher. Her work focuses on acoustics, vibration engineering, and renewable energy systems, with a strong emphasis on wind energy and condition monitoring technologies. Her research activity spans 2022–2024, yielding 3 publications in high-impact venues like the Journal of Physics: Conference Series and ISMA-USD proceedings. She actively participates in peer-review activities and conferences such as WindEurope Annual Events, delivering talks on topics like wind turbine performance analysis and LiDAR-based wind field reconstruction. Though no formal student advisement or grants are explicitly listed, her contributions lie in advancing offshore wind energy systems through innovative hybrid monitoring approaches and signal processing techniques for fault detection.

• Acoustics
• Vibration Engineering
• Renewable Energy Systems
• +3 more

Bruno Stuyts is a Visiting Professor at Ghent University and a postdoctoral researcher in the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on offshore wind energy, geotechnical engineering, and soil-structure interaction, with emphasis on data-driven modeling and in-situ monitoring. He leads projects like WINDSOIL (2020–2024) and SOIL-TWIN (2020–2023), addressing soil-structure interaction and smart monitoring of offshore foundations. His work integrates geophysics, computational mechanics, and artificial intelligence to improve offshore wind foundation design and geohazard assessment. Key research interests include seabed response under wave loading, eigenfrequency analysis of wind turbines, and seismic inversion for subsurface characterization. He collaborates globally on projects involving pore pressure monitoring, data fusion, and real-time modeling of monopile foundations. His contributions span experimental studies (e.g., 3D seabed response experiments) and numerical simulations (e.g., finite element modeling of monitored monopiles). Stuyts has contributed to over 30 peer-reviewed articles, covering topics like CPT-based correlations for shear modulus, scour protection effects on turbine stiffness, and Bayesian estimation techniques. His work bridges academic research and industry applications, aiming to optimize offshore infrastructure reliability through advanced data science and geotechnical innovations. He actively participates in conferences, presenting on data science applications, seismic inversion, and offshore wind challenges. Collaborations include institutions like Zenodo (dataset sharing) and ASME. Current efforts focus on integrating geohazard mapping, AI-driven geotechnical analysis, and open-source modeling frameworks for sustainable offshore energy systems.

• Offshore Wind Energy
• Geotechnical Engineering
• Soil-Structure Interaction
• +3 more

Christof Devriendt is a Professor at the Department of Applied Mechanics, Vrije Universiteit Brussel (VUB), Belgium, affiliated with the Acoustics & Vibrations Research Group. He leads major research initiatives including the Belgian Offshore Wind Innovation Centre and innovative wind energy systems projects. His work focuses on offshore renewable energy structures, structural health monitoring, and vibration analysis. With an h-index of 27 and 3139 citations, he has authored over 218 publications and supervised numerous research projects. Research Interests: Modal analysis of offshore wind turbines, fatigue assessment of foundations, health monitoring systems, and fluid-structure interactions in coastal engineering. His work addresses challenges in offshore wind energy infrastructure durability and operational safety. Recent Projects: FWOIRI19: Fan Array Wind Tunnel Development (2025–2028) FOD175: Clay Strength Research (2024–2027) FOD177: Offshore Wind Innovation Hub (2024–2027) Awards: Received the Best Paper Award (2nd place) in 2022 for collaborative research on wind turbine monitoring. His contributions span 49 academic activities including conference presentations and international collaborations.

• Offshore Wind Energy
• Structural Health Monitoring
• Modal Analysis
• +3 more

Jan Helsen is a Professor of Engineering Technology at Vrije Universiteit Brussel (VUB), leading the Acoustics & Vibrations Research Group. His primary affiliation is within the Faculty of Engineering Technology, focusing on advanced research in renewable energy systems, offshore wind technology, and predictive maintenance. He actively collaborates with industry partners and governmental institutions, as seen in projects like the Belgian Offshore Wind Innovation Centre (2024–2027) and the INSPIRE initiative under the Sustainable Blue Economy Partnership. Helsen’s research emphasizes wind turbine reliability, fault detection algorithms, and AI-driven operational optimization. His work bridges mechanical engineering principles with data science, addressing challenges such as drivetrain health monitoring, wake effect modeling, and hybrid energy systems. Recent contributions include modular deep learning frameworks for wind farm power forecasting and reinforcement learning strategies for hybrid wind-hydrogen plants. He has secured significant funding through projects totaling over €10M, including the €3M ICON (Cloud-edge AI for Offshore Wind O&M) and the €2.5M INSPIRE initiative. Helsen’s team publishes extensively in journals like Mechanical Systems and Signal Processing and Wind Energy Science , with a focus on practical solutions for offshore energy challenges. His lab also pioneered datasets on hyperspectral analysis of additive manufacturing processes, demonstrating interdisciplinary reach. Key collaborations include INSA Lyon (France) for joint PhD programs and the Royal Meteorological Institute for weather simulation studies. Helsen’s work is cited over 1,078 times (h-index 16), reflecting his influence in advancing offshore wind infrastructure resilience and smart energy systems.

• Wind Turbines
• Fault Detection
• Modal Analysis
• +5 more

Patrick Guillaume is a Professor at the Department of Applied Mechanics, Vrije Universiteit Brussel (VUB), Faculty of Engineering. His research focuses on vibration and acoustics, structural health monitoring, and additive manufacturing. Expertise in Experimental and Operational Modal Analysis Specializes in Directed Energy Deposition and Flight Flutter Analysis Active in Structural Health Monitoring for offshore wind turbines and rotating machinery His recent publications (2023–2025) include work on 3D-DIC for aerospace structures, real-time temperature control in additive manufacturing, and hyperspectral data analysis. These articles highlight his contributions to mechanical systems, laser technologies, and structural dynamics. Guillaume has supervised multiple doctoral and master's theses, including research on lithium-ion battery lifetime modeling, hybrid aeroacoustics, and wind turbine blade analysis. He participates in workshops and academic ceremonies organized at VUB. He leads projects like FWOTM1172 (thermal history in hybrid additive manufacturing) and IOF3024 (industrial valorization of monitoring strategies). His work often involves interdisciplinary collaboration with teams specializing in 3D printers, laser technologies, and data structures.

• Vibration and Acoustics
• Experimental Modal Analysis
• Structural Health Monitoring
• +7 more

Xavier Chesterman is a postdoctoral researcher at Vrije Universiteit Brussel (VUB), Belgium, affiliated with the Department of Informatics and Applied Informatics and the Acoustics & Vibration Research Group. His work focuses on advancing wind turbine operational reliability through data-driven condition monitoring solutions. His research spans wind turbine condition monitoring , anomaly detection , and data fusion with emphasis on rare failure scenarios and slowly progressing damage. Key methodologies include machine learning applications for SCADA data analysis, multi-modal sensor integration, and fleet-wide diagnostic systems for both onshore and offshore wind farms. Analysis of Chesterman's 8 research outputs (2021-2025) reveals a clear trajectory toward scalable solutions for wind farm operations. His recent 2025 publications demonstrate significant progress in farm-wide drivetrain event tracking and offshore turbine failure prediction, building upon foundational 2023-2024 work in normal behavior modeling and pattern mining techniques. As a postdoctoral researcher under Professor Jan Helsen's supervision, Chesterman actively contributes to VUB's wind energy research initiatives. His work suggests involvement in funded projects related to renewable energy analytics, with opportunities for collaboration in wind turbine health monitoring systems and predictive maintenance frameworks. The Acoustics & Vibration Research Group provides a specialized environment for vibration analysis and condition monitoring innovation in renewable energy systems.

• Wind turbines
• Condition monitoring
• Anomaly detection
• +3 more