Data-integrated uncertainty quantification for the performance prediction of iced airfoils

TL;DR

This paper develops a comprehensive uncertainty quantification framework for predicting the effects of ice formation on airfoil performance, integrating experimental data, advanced simulation, and multiple UQ methods to improve accuracy.

Contribution

It introduces a novel combination of experimental ice shape data, PCA, and advanced UQ methods like MLMC and MFMC with LES for iced airfoil performance prediction.

Findings

MFMC outperforms other UQ methods in variance reduction

All three UQ methods accurately predict mean and standard deviation

Simulation framework is highly automated and efficient in HPC environments

Abstract

Airfoil icing is a severe safety hazard in aviation and causes power losses on wind turbines. The precise shape of the ice formation is subject to large uncertainties, so uncertainty quantification (UQ) is needed for a reliable prediction of its effects. In this study, we aim to establish a reliable estimate of the effect of icing on airfoil performance through UQ. We use a series of experimentally measured wind tunnel ice shapes as input data. Principal component analysis is employed to construct a set of linearly uncorrelated geometric modes from the data, which serves as random input to the UQ simulation. For uncertainty propagation, non-intrusive polynomial chaos expansion (NIPC), multi-level Monte Carlo (MLMC) and multi-fidelity Monte Carlo control variate (MFMC) methods are employed and compared. As a baseline model, large eddy simulations (LES) are carried out using the discontinuous Galerkin flow solver FLEXI. UQ simulations are carried out with the in-house framework PoUnce. Its focus is on a high level of automation and efficiency considerations in a high performance computing environment. Due to the high number of samples, the simulation tool chain of the baseline model is completely automatized, including a new structured boundary layer grid generator for highly irregular domain shapes. The results show that forces on the airfoil vary considerably due to the uncertain ice shape. All three methods prove to be suited to predict mean and standard deviation. In the Monte Carlo techniques, the choice and performance of low-fidelity models is shown to be decisive for estimator variance reduction. The MFMC method performs best in this study. To our knowledge, there are no UQ studies of iced airfoils based on LES, let alone with advanced UQ methods such as MLMC or MFMC. The present study thus represents a leap in accuracy and level of detail for this application.