Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics
Abstract
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 multilevelmultifidelity 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 lowerlevel models are provided by simulations with coarser time steps. Reynoldsaveraged 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 force, 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 multilevelmultifidelity 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.
 Authors:

 University of Colorado
 National Renewable Energy Laboratory (NREL), Golden, CO (United States)
 Publication Date:
 Research Org.:
 National Renewable Energy Lab. (NREL), Golden, CO (United States)
 Sponsoring Org.:
 USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE4W)
 OSTI Identifier:
 1547242
 Report Number(s):
 NREL/CP500074498
 DOE Contract Number:
 AC3608GO28308
 Resource Type:
 Conference
 Resource Relation:
 Conference: Presented at the AIAA SciTech 2019 Forum, 711 January 2019, San Diego, California
 Country of Publication:
 United States
 Language:
 English
 Subject:
 17 WIND ENERGY; wind energy; airfoils; uncertainty; aerodynamics; modeling
Citation Formats
Quick, Julian, Hamlington, Peter E., King, Ryan N, and Sprague, Michael A. Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics. United States: N. p., 2019.
Web. doi:10.2514/6.20190542.
Quick, Julian, Hamlington, Peter E., King, Ryan N, & Sprague, Michael A. Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics. United States. doi:10.2514/6.20190542.
Quick, Julian, Hamlington, Peter E., King, Ryan N, and Sprague, Michael A. Sun .
"Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics". United States. doi:10.2514/6.20190542.
@article{osti_1547242,
title = {Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics},
author = {Quick, Julian and Hamlington, Peter E. and King, Ryan N and Sprague, Michael A},
abstractNote = {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 multilevelmultifidelity 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 lowerlevel models are provided by simulations with coarser time steps. Reynoldsaveraged 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 force, 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 multilevelmultifidelity 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.},
doi = {10.2514/6.20190542},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}