Abstract
The effects of atmospheric icing can be anticipated by Computational Fluid Dynamics (CFD). Past studies show that the convective heat transfer influences the ice accretion and is itself a function of surface roughness. Uncertainty quantification (UQ) could help quantify the impact of surface roughness parameters on the reliability of ice accretion prediction. This paper aims to quantify ice accretion uncertainties and identify the key surface roughness correction parameters contributing the most to the uncertainties in a Reynolds-Averaged Navier-Stokes (RANS) formulation. Ice accretion simulations over a rough flat plate using two thermal correction models are used to construct a RANS database. Non-Intrusive Polynomial Chaos Expansion (NIPCE) metamodels are developed to predict the convective heat transfer and icing characteristics of the RANS database. The metamodels allow for the computation of the 95% confidence intervals of the output probability distribution (PDF) and of the sensitivity indexes of the roughness parameters according to their level of influence on the outputs. For one of the thermal correction models, the most influential parameter is the roughness height, whereas for the second model it is the surface correction coefficient. In addition, the uncertainty on the freestream temperature has a minor impact on the ice accretion sensitivity compared to the uncertainty on the roughness parameters.
Highlights
Icing represents a major threat to in-flight safety
One relevant figure is shown for each aspect listed, and the remaining data for all possible metamodel/thermal correction model combinations are summarized in tables
This paper has introduced an efficient methodology to quantify the sensitivity of the ice accretion characteristics to the thermal correction parameters on a rough flat plate
Summary
Ice accretion leads to increased aircraft weight, aerodynamic efficiency loss and a potential increase of up to more than 60% in drag [1]. The aerodynamic efficiency loss is determined by the geometry of the ice shape, mainly the thickness and extension [3]. Developments in numerical simulations, especially RANS-based simulations (Reynolds Averaged Navier-Stokes), have enabled the development of icing codes, useful for the effective anticipation of the icing process [4]. Even if icing codes share similar mathematical models and numerical methods, the predicted ice shapes can vary greatly for the same atmospheric conditions [5]. The discrepancies between shapes can be explained by various reasons, both mathematical and numerical, among them the discrepancy between the convective heat transfer models. The fraction of the shape discrepancy that could be attributed to the thermal modeling remains to be quantified
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
