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Title: Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics: Preprint

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 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 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 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.

Authors:
 [1];  [2];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Colorado
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 (EE-4W)
OSTI Identifier:
1493398
Report Number(s):
NREL/CP-5000-72974
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the American Institute of Aeronautics and Astronautics SciTech Forum 2019, 7-11 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: Preprint. United States: N. p., 2019. Web. doi:10.2514/6.2019-0542.
Quick, Julian, Hamlington, Peter E., King, Ryan N, & Sprague, Michael A. Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics: Preprint. United States. doi:10.2514/6.2019-0542.
Quick, Julian, Hamlington, Peter E., King, Ryan N, and Sprague, Michael A. Tue . "Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics: Preprint". United States. doi:10.2514/6.2019-0542. https://www.osti.gov/servlets/purl/1493398.
@article{osti_1493398,
title = {Multifidelity Uncertainty Quantification with Applications in Wind Turbine Aerodynamics: Preprint},
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 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 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 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.},
doi = {10.2514/6.2019-0542},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
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