Aerodynamics Research To Improve Large Wind Turbine Performance and Reliability

NREL’s Aerodynamics of Large Turbines (ALTius, which is Greek for “higher”) project is driving knowledge of aerodynamics of large turbines higher.

As part of ALTius, NREL is providing up to $6.25 million to industry and academia to generate scientific data that can enable the design and development of cost-effective, high-performance large commercial wind turbines.

Important Dates

December 2024: Notice of intent posted

Jan. 17, 2025: Request for proposals opened

Jan. 23, 2025: Informational Webinar

March 17, 2025: Proposal submissions due

April 2025 (Anticipated): Selections announced

Modern offshore wind turbines are the largest rotating machines ever built by humankind. As these turbines grow larger to capture more wind energy (beyond 10 MW of capacity), they offer significant opportunities for cost reduction but also expose knowledge gaps that increase the risk to their performance and reliability.

To address these challenges in the field of aerodynamics, NREL, on behalf of the U.S. Department of Energy’s Wind Energy Technologies Office, has issued a request for proposals (RFP) from industry and academia to advance necessary data and tools that can accelerate the development of cost-effective, high-performance large commercial turbines. The RFP will offer up to $6.25 million in funding to eligible entities.

The Focus of This Research

The research targets two key knowledge gaps:

  • Understanding aerodynamics at large scales
    Large wind turbines of 10–15 MW and beyond—such as those being deployed offshore today—operate in unique aerodynamic conditions (high Reynolds numbers). The current lack of high-quality, open-source experimental data to characterize the behavior of airfoils (the curved surface of a wind turbine blade) and validate the tools used to design and analyze them creates uncertainties. Generating these data will improve the accuracy of design tools, leading to the development of  more reliable and efficient turbines.

  • Addressing loads in nonoperational conditions.
    Large turbine blades can face significant stress from vibrations when turbines are in nonoperational states (when idling or parked), such as during installation, maintenance, or in extreme weather events. Current simulation techniques struggle to predict unsteady, three-dimensional (3D) aerodynamic and aeroelastic effects, leading to over-designed and costly blades or designs that are vulnerable to damage under certain conditions.

By providing validation-quality, open test data to the wind R&D community, the selected projects will:

  • Enhance the reliability and aerodynamic performance of large wind turbines

  • Accelerate and assure the reduction of the levelized cost of wind energy by enabling the design of turbines that can reliably and cost-effectively avoid or withstand excessive loads and vibrations.

Funding Details and Eligibility

NREL will award up to $6.25 million to U.S.-based research teams from industry and academia, with funding distributed across two topic areas. This initiative represents a significant investment in the aerodynamics of large-scale wind turbine technology, fostering innovation and collaboration between industry and academia to overcome technical barriers.

Eligible teams must be a domestic institution for higher education, a for-profit or nonprofit entity, a state or local governmental entity, or a Tribal Nation.

Topic Areas

Topic Area 1: High Reynolds Number Airfoil Aerodynamics Validation Datasets

Applicants will design and carry out experiments to generate and disseminate high-quality data about how airfoils perform at a specific range of conditions that match those experienced by large wind turbines. Key aspects include:

  • Primary testing: The main testing will take place at a national research facility, capable of providing crucial data to validate, develop, or improve computer models that can predict aerodynamic performance, scaling, and robustness accurately.

  • Secondary testing: Conducted in lower Reynolds number, even ‘quieter’, wind tunnels to expand the datasets, these tests will cover sensitivity to turbulence, wind hitting the airfoils at extreme angles, and any other experiments that could improve understanding of relevant flow physics.

  • Funding: The total funding for this topic is $4.75 million to the applicant, with an additional approximately $3 million from DOE directly supporting the national test facility's (the National Full-Scale Aerodynamics Complex’s) operations.

Topic Area 2: Aerodynamic Characterization of Nonoperational Loads Phenomena

Applicants will conduct experiments to characterize how wind turbine blades behave aerodynamically and produce publicly available validation-quality datasets that improve or develop computer models for predicting how blades behave when idling, parked, and/or in extreme weather. Key aspects include:

  • Testing scenarios: Experiments can use blades that are scaled down or full size, rigid or aero-elastically representative. Tests will need to capture the unsteady (changing) 3D aerodynamics of the blades in relevant situations and provide complete descriptions of all boundary and initial conditions. Additional tests on airfoil sections may also be designed to replicate the key conditions causing these vibrations.

  • Funding: Approximately $1.5 million is allocated for this topic, with one or two awards expected.

Learn more about the topic areas and solicitation process in the request for proposals document.

Contact

Contact ALTius with further questions about the ALTius RFP.

Industry and academic organizations interested in forming teams and looking for partners can put their names into a Microsoft form teaming list. We expect applicants generally to bring expertise in and, hence, select for themselves appropriate roles in:

  • Aerodynamic instrumentation and testing (wind tunnels)
  • Test system integration and management
  • Aerodynamic design analysis
  • Aerodynamics/aeroelasticity of wind turbines
  • Physics-based model development and validation.

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