Nicholas Hamilton

Nicholas Hamilton

Senior Research Engineer | Wind Energy | Atmospheric Science | Turbulent Fluid Mechancis | Dynamical Systems

RAAW: Rotor Aerodynamics, Aeroelastics, and Wake

Project Overview

The Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) experiment is a collaboration between NREL and GE Vernova to better understand the physics governing wind turbine wake generation, evolution, and recovery. As Principal Investigator, I lead this $8M+ project combining highly-instrumented commercial turbines with advanced remote sensing to connect blade loading, structural dynamics, and wake behavior.

Through partnership with GE Vernova, RAAW benefited from access to:

  • Industry experts on the design, control, and operation of utility scale wind turbines
  • Production turbine control systems and operational setpoints
  • Validated aeroelastic models for multi-physics simulation and benchmarking
  • Real-world testing environment for advanced measurement and control strategies

This collaboration accelerates technology transfer from fundamental research to grid-scale deployment, directly impacting wind plant economics.

Scientific Goals

RAAW addresses critical knowledge gaps in wind turbine aerodynamics:

  • Rotor-Wake Coupling: How do blade pitch, yaw, and structural dynamics influence near-wake formation?
  • Wake Meandering: What drives large-scale wake wandering and how does it affect downstream turbine performance?
  • Load Validation: Can we validate aeroelastic simulation tools using coordinated wake and turbine operational data in a dynamic sense (second by second)?

Technical Approach

RAAW deployed cutting-edge measurement systems:

  • Instrumented Turbines: Blade root strain gauges, nacelle loads, high-frequency SCADA, and blade pitch/yaw controllers providing millisecond-resolution operational data
  • Multi-Lidar Array: 6+ scanning Doppler lidars capturing three-dimensional wake structure from near-wake to 10+ rotor diameters downstream
  • Tower Measurements: High-frequency velocity, temperature, and turbulence statistics for model validation
  • Photogrammetry: Target-tracking of more than 150 unique points distributed on rotor blades and tower providing unique defelection and torsion measurements

Funding & Partnerships

  • Funding: $8M+ from DOE Wind Energy Technologies Office
  • Duration: 2021-2025
  • Key Partner: GE Vernova (formerly GE Renewable Energy)
  • Collaborators: NREL, Sandia National Laboratories, University of Colorado Boulder

Future Directions

RAAW data will support:

  • Physics-informed machine learning for wake prediction
  • Digital twin development for wind plant operations
  • Uncertainty quantification frameworks for turbine loading
  • Next-generation wind plant control optimization

See the Project Page for datasets and more information!