Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics

Ryan King, Michael Sprague, Julian Quick, Peter Hamlington

Research output: Contribution to conferencePaperpeer-review

6 Scopus Citations


The propagation of input uncertainty through engineering models allows designers to better understand the range of possible outcomes resulting from design decisions. This could lead to greater trust between modelers and stakeholders in the wind energy industry. In this study, we apply multilevel-multifidelity Monte Carlo sampling to flow over an airfoil, assuming uncertainty in the inflow conditions, and characterize the associated computational savings compared to standard Monte Carlo approaches. The “truth” model is provided by an airfoil simulation with a very fine computational time step, and auxiliary lower-level models are provided by simulations with coarser time steps. Reynolds-averaged Navier Stokes and detached eddy simulations are used to obtain two different model fidelities. The primary quantity of interest for this analysis is the lift, which is examined for a range of angles of attack. We launch an initial set of “trial” samples to determine the optimal allocation of model evaluations, and these trial evaluations are used to inform a larger sampling effort. Using the multilevel-multifidelity approach, we achieve roughly an order of magnitude variance reduction in expected lift as compared to the standard Monte Carlo approach with an equivalent computational cost.

Original languageAmerican English
StatePublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: 7 Jan 201911 Jan 2019


ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

See NREL/CP-5000-72974 for preprint

NREL Publication Number

  • NREL/CP-5000-74498


  • aerodynamics
  • airfoils
  • modeling
  • uncertainty
  • wind energy


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