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50 MW Segmented Ultralight Morphing Rotors for Wind Energy

Technical Report ·
DOI:https://doi.org/10.2172/2375003· OSTI ID:2375003
 [1];  [2];  [3];  [4];  [5];  [6];  [5]
  1. General Electric Research Lab., Schenectady, NY (United States); University of Virginia
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  3. Univ. of Texas at Dallas, Richardson, TX (United States)
  4. Colorado School of Mines, Golden, CO (United States)
  5. Univ. of Virginia, Charlottesville, VA (United States)
  6. Univ. of Colorado, Boulder, CO (United States)
+ Show Author Affiliations

A multi-institutional team designed a 50 Megawatt (MW) rated wind turbine featuring downwind aeroelastic morphing to reduce blade loads and allow an ultralight segmented rotor (mass reduction of about 25% compared to a conventional upwind rotor). The team used a control co-design approach with state-of-the art simulations including for the rotor and tower design using non-linear fluid-structure interactions and control algorithms, and the team also designed, built, and field-tested an aeroelastically-scaled downwind rotor to demonstrate this novel technology and validate the design tool fidelity. In a follow-on phase, these results were used along with an updated Levelized Cost Of Energy (LCOE) methodology to co-design a more detailed set of 25 MW rated turbine designs (including individual pitch control) based on minimum LCOE with highly flexible blades for an Atlantic Ocean offshore fixed-bottom design targeted towards market technology evaluation. A set of upwind and downwind designs at 25 MW rated scales were found to provide the best LCOE, with strong improvements over all previous offshore reference turbines (NREL 5MW, DTU 10 MW and IEA 15 MW). The optimized design would represent the world’s largest offshore turbine design, which combines several state-of-the-art structural, aerodynamic, and control technologies into a new and optimized system concept.

Research Organization:
Univ. of Virginia, Charlottesville, VA (United States)
Sponsoring Organization:
USDOE Office of Artificial Intelligence and Technology (AITO); USDOE Advanced Research Projects Agency - Energy (ARPA-E)
Contributing Organization:
Sandia National Laboratories (SNL)
DOE Contract Number:
AR0000667
OSTI ID:
2375003
Country of Publication:
United States
Language:
English

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Related Subjects

17 WIND ENERGY