Data-driven Roughness Estimation for Glaze Ice Accretion Simulation École de Technologie Supérieure

Format:

Book

Conference/Event

Author/Creator:

Ignatowicz, Kevin, author.

Contributor:

Beaugendre, Héloïse

Morency, François

Conference Name:

International Conference on Icing of Aircraft, Engines, and Structures (2023-06-20 : Vienna, Austria)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

In-flight ice accretion on aircraft is a major weather-related threat. Industries use both experimental investigations in icing conditions and ice accretion solvers based on computational fluid dynamics (CFD) for aircraft development. An ice accretion solver couples airflow over the geometry, water droplets impingement, and phase change to compute the ice accretion. Such a solver usually relies on a two-equation model: a mass balance and an energy balance. Past studies highlighted the importance of the roughness-sensitive convective heat loss for energy balance. Uncertainties persist in the CFD models given the complexity of the ice accretion phenomenon, which usually mixes solid ice with liquid runback water (glaze ice). A major uncertainty is related to the surface roughness pattern, which is difficult to measure in experiments. The calibration of the roughness pattern for a CFD test case was seldom investigated in literature. Among the available calibration tools, the Bayesian calibration constitutes a powerful data-driven approach suitable for roughness pattern estimation. The objective of the paper is to set up a methodology for the roughness pattern calibration on an airfoil in glaze ice conditions. Specifically, this methodology determines the roughness pattern needed to minimize the root mean square error between the numerical and experimental accretions. First, an ice accretion solver implemented in SU2 CFD generates a roughness-sensitive ice shape database. Second, a Polynomial Chaos Expansion (PCE) metamodel replaces the database. Finally, a Bayesian inversion is performed on the metamodel to determine the roughness pattern producing a realistic ice shape. The fidelity of an ice shape prediction is measured with a root mean square (RMS) error on the iced portion of the airfoil. Such methodology produces promising results, giving an accretion with a RMS error of less than 0.4% of the chord length compared to the experimental accretion thickness

Notes:

Vendor supplied data

Publisher Number:

2023-01-1449

Access Restriction:

Restricted for use by site license