Uncertainty Quantification for Airfoil Icing Using Polynomial Chaos Expansions

Abstract

The formation and accretion of ice on the leading edge of a wing can be detrimental to airplane performance. Complicating this reality is the fact that even a small amount of uncertainty in the shape of the accreted ice may result in a large amount of uncertainty in aerodynamic performance metrics (e.g., stall angle of attack). The main focus of this work concerns using the techniques of Polynomial Chaos Expansions (PCE) to quantify icing uncertainty much more quickly than traditional methods (e.g., Monte Carlo).

Data-Driven Low-Dimensional Modeling and Uncertainty Quantification for Airfoil Icing

“…This phenomenon agrees with similar findings in a related study. 2 Although horn position is the most dominant parameter, the other parameters do affect performance; this can be revealed by examining the correlations in Table 1. As expected, taller horns give worse performance than shorter ones.…”

“…This is in contrast to prior related studies, in which the ice shape variations that were examined were generated in a somewhat heuristic fashion. 2 The framework we adopt includes a combination of techniques from low-dimensional modeling and uncertainty quantification. Specifically, given a database of ice shapes, we first identify a low-dimensional set of shape parameters by using the Proper Orthogonal Decomposition (POD) of the dataset.…”

Quantification of Airfoil Aerodynamic Uncertainty due to Pressure-Sensitive Paint Thickness

“…predictions [11]. There are varied uncertainty quantification approaches such as Polynomial Chaos method, sensitivity analysis approach, Monte Carlo method, surrogate model approach [12], etc. Bunker [15] carried out a study of uncertainty of geometrical and operational variations for highly cooled turbine blades by Monte Carlo simulation.…”

Resampling strategies to improve surrogate model‐based uncertainty quantification: Application to LES of LS89

“…In fact, the diversity of uncertainties on the CFD boundary conditions or initial conditions as well as on model parameters (input data, geometry, simplification of the model physics, etc) limits the validity of the simulations: the quantity of interest (QoI) can be easily affected and shadowed by the conjugation of all types of uncertainties. This assessment explains why uncertainty quantification (UQ) is now becoming a mandatory step in application‐oriented modeling for operational and industrial purposes . It not only provides insight into the level of uncertainty in the numerical simulation results but also gives access to the sensitivity analysis (SA), which aims at describing the respective influences of the input parameters on the QoI.…”

“…This assessment explains why uncertainty quantification (UQ) is now becoming a mandatory step in application-oriented modeling for operational and industrial purposes. 5,6 It not only provides insight into the level of uncertainty in the numerical simulation results but also gives access to the sensitivity analysis (SA), which aims at describing the respective influences of the input parameters on the QoI. The inclusion of UQ in a design optimization cycle hence allows manufacturers to design quicker and obtain better, cheaper, and more robust (ie, more stable) products.…”