{"id":115,"date":"2026-02-05T09:26:49","date_gmt":"2026-02-05T09:26:49","guid":{"rendered":"https:\/\/windeurope.org\/tech2026\/?page_id=115"},"modified":"2026-03-30T13:36:32","modified_gmt":"2026-03-30T12:36:32","slug":"posters","status":"publish","type":"page","link":"https:\/\/windeurope.org\/tech2026\/programme\/posters\/","title":{"rendered":"Posters"},"content":{"rendered":"
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See the list of poster presenters at the Technology Workshop 2026 – and check out their work!<\/h3>\r\n

For more details on each poster, click on the poster titles to read the abstract.<\/p>\r\n

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PO01: A Measurement-Consistent, Power-Weighted 3-D Framework for Wind-Turbine Power-Curve Reconstruction from Operating Data<\/a><\/h3>\r\n

Toan Vu Huy<\/a>, Senior Expert, Construction machinery and Industrial works CONINCO joint stock Company<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO02: A model-based Wind Farm Digital Twin for under-performance & wind speed measurement errors detection<\/a><\/h3>\r\n

Ambroise Cadoret<\/a>, R&D Wind Engineer, GreenWITS<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO03: A Scalable Data-Driven Approach for Estimating and Monitoring Wake Losses in Wind Farms<\/a><\/h3>\r\n

Philip Bradstock<\/a>, Head of analytics, Bitbloom<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO04: Comparison of Deterministic and Probabilistic Post-Construction Long-Term AEP Frameworks Using Operational SCADA Data<\/a><\/h3>\r\n

Daniele D'Ambrosio<\/a>, R&D Wind Expert, 3E<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO05: The Importance of Database Size for Machine Learning Techniques in Power Curve Analysis<\/a><\/h3>\r\n

Rebeca Rivera Lamata<\/a>, Founder and Freelance Consultant, WindColab<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO06: Turbine Power Performance in Variable Atmospheric Stability and Wind Shear<\/a><\/h3>\r\n

Philip Bradstock<\/a>, Head of analytics, Bitbloom<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO07: Wind speed estimation from wind turbine operational data<\/a><\/h3>\r\n

Keno Ohrmann<\/a>, Research associate, Fraunhofer - IWES<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO08: An analytical model for filtering wind farm validation data for a better estimation of interaction loss error.<\/a><\/h3>\r\n

Piyush Singh<\/a>, Energy Yield Specialist, JERA Nex bp<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO09: Harnessing the Winds of Change: Advancing Offshore Yield Assessments with Engineering and Weather-based Models<\/a><\/h3>\r\n

Joseph Anthony Vitulli<\/a>, Senior Consultant - Offshore Wind Advisory, Ramboll<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO10: Informing wind farm availability and grid curtailment from publicly available energy trading data.<\/a><\/h3>\r\n

Piyush Singh<\/a>, Energy Yield Specialist, JERA Nex bp<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO11: Transmission System Availability: Benchmarking and Validation of AEP Losses<\/a><\/h3>\r\n

Andrew Henderson<\/a>, Chief Windfarm Engineer, Copenhagen Offshore Partners<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO12: Methods to correct the nacelle direction of operating wind turbines in a mid-complex site<\/a><\/h3>\r\n

Thea Vanelli<\/a>, PhD student, DTU Wind & Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO13: Monitoring of nonferrous metal passivators in gear oils \u2013 Proactive copper wear detection in wind turbines - Significance for the operation and case studies<\/a><\/h3>\r\n

Stefan Mitterer<\/a>, Business Director Technical Service and Sales, OELCHECK GmbH<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO14: Realistic wake steering assessment: integrating field experience into pre-construction models<\/a><\/h3>\r\n

David Schillebeeckx<\/a>, Wind Data Expert, Laborelec Engie<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO15: Dynamic wind farm flow control at Hollandse Kust Noord wind farm<\/a><\/h3>\r\n

Jan-Willem van Wingerden<\/a>, Professor, TU Delft<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO16: Achieving Measurable AEP Uplift Through Adaptive Control: Open-Source Data and Methods<\/a><\/h3>\r\n

Alex Clerc<\/a>, Controls Product Engineer, RES<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO17: Optimizing co-located battery energy storage systems (BESS) dimensioning and wake steering for wind farm revenue maximization<\/a><\/h3>\r\n

Edvald Edvaldsson<\/a>, CTO, Youwind<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO18: Quantifying the advantages of incorporating curtailment strategy optimization into wind farm layout optimization for different solution search spaces and constraint strengths<\/a><\/h3>\r\n

Moritz Jan Hildemann<\/a>, R&D Engineer, EMD International<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO19: A data-driven methodology for automated KPI assessment of bat curtailment effectiveness in wind farms<\/a><\/h3>\r\n

Maelys Fuchs<\/a>, Wind resources and performance engineer, CGN EUROPE ENERGY<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO20: A parametric correction model for fatigue loads under derated rotor speeds<\/a><\/h3>\r\n

Troels Juul Taudorf<\/a>, R&D Engineer, EMD International A\/S<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO21: From Wind Resource to Wind Value: A Spatial and Temporal Assessment of Long-Term Future Captured Prices across European Markets<\/a><\/h3>\r\n

Sergio Jim\u00e9nez Sanju\u00e1n<\/a>, Senior Data Scientist, DNV<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO22: Bridging the gap between pre-construction AEP assumptions and realized performance: the role of port readiness and installation logistics<\/a><\/h3>\r\n

Enrika Uusitalo<\/a>, Business Development Manager Baltic Offshore Wind, Sarens NV<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO23: Data-Driven Life-Aware Control of Floating Offshore Wind Farms<\/a><\/h3>\r\n

Federico Bellini<\/a>, Ing., Politecnico Di Milano<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO24: Forecasting ice conditions for offshore wind port marshaling operations in cold climates<\/a><\/h3>\r\n

Enrika Uusitalo<\/a>, Business Development Manager Baltic Offshore Wind, Sarens NV<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO25: Modeling wind turbine wake losses an a large scale: a comparison of wind turbine-specific wake decay constants from various data sources<\/a><\/h3>\r\n

Johannes Hirschmann<\/a>, Research Assistant, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO26: A Novel Method for Detecting and Characterizing Wind-Speed and Power Ramp Events Using Swinging Door Algorithm (SDA)<\/a><\/h3>\r\n

Zhi Liang<\/a>, Principal Engineer, University of Chinese Academy of Sciences<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO27: Accelerating Sensor Development via Digital Twin Integration for Predictive Maintenance in Wind Turbine Transmissions<\/a><\/h3>\r\n

Jan Timmer<\/a>, Development Engineer, Inmox GmbH<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO28: Aeroacoustic-based wind turbine blade trailing-edge crack severity identification using physics-informed deep learning<\/a><\/h3>\r\n

Muyao Li<\/a>, PhD candidate, Eindhoven University of Technology<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO29: Blade Vibrations as a Turbine-Level Health Indicator<\/a><\/h3>\r\n

Aida Takhmazova<\/a>, Data Scientist, Turbit Systems GmbH<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO30: Case study: supporting the maintenance management of wind turbine fleets with gearbox defect patterns using condition monitoring systems<\/a><\/h3>\r\n

Emerson Lima<\/a>, Manager of Condition Analysis Center, AQTech Power Prognostics<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO31: Comprehensive study towards effect of wind turbine curtailment on lifetime<\/a><\/h3>\r\n

Menno Kloosterman<\/a>, Principal Engineer, DNV<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO32: Empirical Estimation of AmbientTurbulence Intensity Without theStandard Deviation of Wind Speed<\/a><\/h3>\r\n

Francisco Sousa<\/a>, Student, MEGAJOULE<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO33: Enhanced Anomaly Detection Methodology and Comprehensive Results for Wind Turbines Using Deep Learning and Statistics on CMS Vibration Data<\/a><\/h3>\r\n

Breno Carvalho<\/a>, Performance Engineer, Casa dos Ventos<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO34: Making Maintenance Decisions for Wind Energy Assets Based on Actual Structural and Inspection Data<\/a><\/h3>\r\n

Oleg Ishchuk<\/a>, COO, SDC Verifier<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO35: Preventing Unnoticed Faults: Improving Model Reliability via Distribution-Optimized SCADA Partitioning<\/a><\/h3>\r\n

Jo\u00e3o Campos<\/a>, Performance Engineer , Jungle AI<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO36: Scaling Lessons Learned Across Wind Fleets at Time of Need Using Generative AI and Maintenance History<\/a><\/h3>\r\n

Bora Tokyay<\/a>, Co-Founder and CEO, Kavaken Limited<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO37: Artificial Intelligence for Predictive Maintenance in Renewables (Wind Energy): Benefits, Implementation Challenges, and Strategic Enablers<\/a><\/h3>\r\n

Uzo Okoye<\/a>, DBA Student, Northumbria University<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO38: EPCI Instrumentation reservations are a Bottleneck for SCADA and successful predictive maintenance.<\/a><\/h3>\r\n

Sherif Gebaly<\/a>, Renewables Investment Director, Arcadis<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO39: A Data-Driven Approach for Estimating Wind Turbine Restart Success Probability<\/a><\/h3>\r\n

Gustavo Grubler<\/a>, Data Scientist, AQTech Power Prognostics<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO40: Comparative Analysis of Yaw Misalignment Detection Frameworks: A Validation Campaign in Northeastern Brazil<\/a><\/h3>\r\n

Humberto Pinheiro<\/a>, Associate Professor, UFSM<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO41: Lifetime evaluation based on SCADA data and aeroelastic model validation using mechanical loads testing and lidar measurements<\/a><\/h3>\r\n

Asier Olcoz<\/a>, Senior Project Engineeer, UL Solutions<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO42: Predictive Monitoring of Pitch Bearing End-of-Life: A Case Study on Motor Current Anomaly Detection and Maintenance Validation<\/a><\/h3>\r\n

Nathianne Andrade<\/a>, Head of Performance Engineering, Delfos Energy<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO43: A novel combination of mast and lidar to enable 300m reference measurements<\/a><\/h3>\r\n

Seyi Latunde-Dada<\/a>, Senior Algorithm Scientist, ZX Lidars<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO44: A Practical Approach to 8760 Wind Power Time Series Generation<\/a><\/h3>\r\n

Chaime Malo<\/a>, Wind consultant, S\u00d3LIDA ENERG\u00cdAS RENOVABLES<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO45: A way of estimating representative turbulence intensity by weighting multiple climatologies<\/a><\/h3>\r\n

Alexios Theofilopoulos<\/a>, Mechanical Engineer, Istos Renewables Ltd<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO46: Above and beyond the ZX 300e's 200m classification<\/a><\/h3>\r\n

James Downs<\/a>, Senior Data Analyst, ZX Lidars<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO47: Applying Meso-Micro Coupling in Wind Resource Assessment in Challenging Cases: Validation and Insights<\/a><\/h3>\r\n

Zixiao Jiang<\/a>, Technical director, Meteodyn China<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO48: Approaches to Refining and Reducing Avian Loss Estimates in Wind Farms: A Case Study<\/a><\/h3>\r\n

Arthur Cachia<\/a>, Senior Wind Energy Yield Analyst, EDF Renouvelables<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO49: Beyond Gaussian Assumptions: Empirical Characterization of Intra-10-Minute Wind Speed Variability<\/a><\/h3>\r\n

Francisco Sousa<\/a>, Student, MEGAJOULE<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO50: Combining atmospheric large-eddy simulation (LES) and machine learning (ML) for time-resolved spatial extrapolation of turbulence intensity (TI) measurements.<\/a><\/h3>\r\n

Bernard Postema<\/a>, Scientist, Whiffle BV<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO51: Detecting Thrust Coefficient (Ct) Anomalies<\/a><\/h3>\r\n

Lan Ding<\/a>, Senior Performance Ingenieer, EDF Renouvelables<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO52: Investigation of CERRA as a robust long-term wind resource reference dataset<\/a><\/h3>\r\n

Steve Cordle<\/a>, Senior Wind Analyst, BrightWind<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO53: Long-term correction of short-term wind measurements using Similarity-Guided Multi-Source Ensemble Learning with Domain Generalization<\/a><\/h3>\r\n

Berke Sent\u00fcrk<\/a>, Master Thesis Student, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO54: Loss Modelling for Hourly Wind Production Timeseries in Hybrid Renewable Systems<\/a><\/h3>\r\n

Matthew Zhang<\/a>, Principal Engineer, Wind Energy Resource, Lightsource bp<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO55: Multiscale modelling of wind farm wakes through international collaboration: the EuroWindWakes project<\/a><\/h3>\r\n

Jake Badger<\/a>, Head of Section, Resource Assessment and Meteorology, DTU Wind and Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO56: Zero-Shot Weak Annotation of Avian Species Using Large Vision-Language Models<\/a><\/h3>\r\n

George Dimas<\/a>, Head of R&D, nvisionist S.A.<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO57: A case study of accounting for turbine interaction effects inherent within public wind measurements.<\/a><\/h3>\r\n

Mark Wyper<\/a>, Senior Wind Energy Specialist, JERA Nex bp<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO58: Air\u2013sea interactions in the wake of wind farms<\/a><\/h3>\r\n

Laury Renac<\/a>, Senior Metocean Consultant, REdouble<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO59: Hybrid Sector-Based Wake Superposition Modeling: Assessing Direction-Dependent Wake Effects and Wind Farm Performance Improvements.<\/a><\/h3>\r\n

Selhia Idrissi<\/a>, Wind Engineer, Meteodyn<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO60: Mapping inter\u2011annual wind speed variability in the Southern Baltic: influence on offshore yield assessments<\/a><\/h3>\r\n

Marceli Tauzowski<\/a>, Head of Wind Analytics and Optimisation, PGE Baltica<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO61: Weather regimes for wind resource assessment of large offshore wind farm clusters<\/a><\/h3>\r\n

Oscar Garcia-Santiago<\/a>, PhD Student, DTU Wind and Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO62: Weather2Wakes: A weather-driven surrogate model for mesoscale wake prediction<\/a><\/h3>\r\n

Nicolas G. Alonso-de-Linaje<\/a>, PhD student, DTU Wind & Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO63: What is the optimal hindcast length? Investigating the sensitivity of hindcast length from metocean and wind resource perspectives.<\/a><\/h3>\r\n

Jacob Royle<\/a>, Chief Metocean Consultant, Ramboll<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO64: Evaluating Lidar Turbulence Intensity Calibrations with Load-Relevant KPIs using the Open-Source TACT Python Framework<\/a><\/h3>\r\n

Alexandra Arntsen<\/a>, Data Scientist, NRG Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO65: Integrated Open-Source Framework for Automated Terrain Complexity Assessment, LiDAR Uncertainty Estimation, and Wake Analysis<\/a><\/h3>\r\n

Eug\u00eanio Pozzobon<\/a>, PhD Student\/Researcher, UFSM<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO66: Portfolio effect on long-term energy yield assessments<\/a><\/h3>\r\n

Nicolas Meerts<\/a>, Technical Lead, 3E<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO67: Recalibration of Atlas wind speed using a statistical approach based on onsite measurements and open-source data<\/a><\/h3>\r\n

Yasmine Tarchouna<\/a>, Operational Assessment Engineer, EDF Power Solutions<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO68: STRAIGHT - Increasing quality and efficiency in energy yield estimation for wind farms<\/a><\/h3>\r\n

Lasse Blanke<\/a>, Managing Director, anemos GmbH<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO69: Validating and Extending the \u0394RIX Correction: A Multi-Site Data Pool for WAsP Predictions in Complex Terrain<\/a><\/h3>\r\n

Osaid Qasim<\/a>, Wind Energy Analyst and Energy engineering Master Student, USENS Energy Solutions<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO70: Improved low-uncertainty calibration for reliable and accelerated nacelle lidar deployment<\/a><\/h3>\r\n

Mathieu Pell\u00e9<\/a>, Development engineer, DTU Wind & Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO71: Measuring the wind for a (theoretical) 400m rotor diameter turbine at Test Centre \u00d8sterild<\/a><\/h3>\r\n

Paula G\u00f3mez<\/a>, Senior executive officer at DTU Wind and Energy Systems, DTU Wind and Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO72: Simulation and Compensation of Environmental Sensitivities in Lidar Measurements<\/a><\/h3>\r\n

Andrew Black<\/a>, Research & Applications Engineer, Vaisala<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO73: Efficient Pre-Validation of Floating LiDAR Systems Using a Mobile Onshore Test Stand<\/a><\/h3>\r\n

Holger F\u00fcrst<\/a>, Researcher, Stuttgart Wind Energy (SWE), University of Stuttgart<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO74: Motion compensation applied to dual-lidar FLS<\/a><\/h3>\r\n

Anselme Troiville<\/a>, Senior Wind and Metocean Data Lead, Green Rebel<\/p>\r\n <\/div>\r\n <\/div>\r\n

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PO75: On the Spatial Extent of Turbulence Intensity Measurements from Dual-Doppler Scanning Lidars<\/a><\/h3>\r\n

Farkhondeh Rouholahnejad<\/a>, Researcher, Fraunhofer IWES<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO76: Adjustment Method for Ground-Based Lidar Measurements of Turbulence Intensity Using Perturbation Theory in Flat Terrain<\/a><\/h3>\r\n

Joram de Vries<\/a>, Wind Resource Analyst, Vattenfall<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO77: Bayesian state estimation of 3D wind field from Doppler lidar measurements<\/a><\/h3>\r\n

Petri Varvia<\/a>, Project researcher, University of Eastern Finland<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO78: CFD-Based LiDAR Data Correction: A Large-Scale Validation Across 15 Sites in Brazil<\/a><\/h3>\r\n

Leonardo Gon\u00e7alves Chiquita<\/a>, Project Engineer, Casa dos Ventos<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO79: Integrating Atmospheric Stability into Energy Yield Assessment: A Measurement-Based Framework for Reducing Wake Uncertainty Bias and Including Explicit Stability Uncertainty<\/a><\/h3>\r\n

Sasikumar Duraisamy<\/a>, Wind Resource Manager, Intercontinental Energy<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO80: Simulating the Impact of Flow Complexity and Sampling Schemes on Ground-Based Lidar Wind Measurements<\/a><\/h3>\r\n

Zack Glindon<\/a>, Performance Analyst, ZX Lidars<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO81: Turbulence intensity correction in Doppler lidars using energy dissipation rate estimation<\/a><\/h3>\r\n

Mohammadreza Manami<\/a>, Industrial PhD Candidate, DTU Wind & Energy Systems<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO82: Assessment of engineering and mesoscale models for far-reaching wind farm wakes under atmospheric variability<\/a><\/h3>\r\n

Marion Coquelet<\/a>, Research Engineer, France Energies Marines<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO83: Lidar-Based Assessment of Low-Level Jets over the Sea of Marmara for Nearshore Wind Energy Applications<\/a><\/h3>\r\n

Elif Dilara \u00c7i\u00e7ek<\/a>, Wind resource analyst, usens energy solutions<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO84: Measuring wake effects on the boundary layer height<\/a><\/h3>\r\n

Erik Holtslag<\/a>, Senior consultant, REdouble<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO85: Sensitivity of wind farm-induced gravity waves to rotor tip clearance and turbulence intensity<\/a><\/h3>\r\n

Udhaya Chandiran Krishnan Paranjothi<\/a>, PhD Candidate, Wind Energy Section, TU Delft<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO86: Multidecadal variability vs climate change: is ERA5 fit for purpose to disentangle them?<\/a><\/h3>\r\n

Kai Lochbihler<\/a>, Climate Analyst, Climate Scale<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO87: Comparative Assessment of Mesh-Based and Particle-Based CFD Approaches for Breaking Wave Hydrodynamics Modelling<\/a><\/h3>\r\n

Vignesh R<\/a>, Student, Indian Institute of Technology Madras<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO88: Assessment of climate change risk on wind energy yield<\/a><\/h3>\r\n

Simone Pfau<\/a>, Scientist Wind field modeling, GEO-NET Umweltconsulting GmbH<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO89: Horizontal extrapolation of extreme winds for wind-farm repowering<\/a><\/h3>\r\n

Antonio Sebastiano Piazzoni<\/a>, Head of Wind Energy Assessment, Edison S.p.A.<\/p>\r\n <\/div>\r\n <\/div>\r\n

\r\n
\r\n \r\n <\/div>\r\n
\r\n

PO90: Modelling uncertainty assessment in simulations of wind-farm repowering in complex terrain, including CFD\/LES model-derived uncertainty<\/a><\/h3>\r\n

Paolo Serralunga<\/a>, Wind and Site Engineer, Edison SpA<\/p>\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n<\/section>","protected":false},"excerpt":{"rendered":"

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