In-flight Icing consists of the accumulation of ice over the surfaces of flying crafts, namely aircraft and helicopters. It occurs when those fly through visible moisture, i.e., clouds, at temperatures below the freezing point. This phenomenon is undesirable because it compromises the safety and performance of the flying crafts. The physics of the phenomenon is complex, and it is still behind its full comprehension. Moreover, the characterization of the icing environments and their replication in experimental facilities is subject to large uncertainties. These might arise from phenomena like the complex physics of clouds, the accuracy of the measuring devices, or the resolution to reproduce flight and cloud properties, among others. This entails a reduction on the predictive accuracy of numerical models that seek to reproduce ice shapes and to assess the performance of ice protection systems. For these reasons, this research field could greatly benefit from the deployment of Uncertainty Quantification (UQ) techniques to account from epistemic and aleatory uncertainties in model predictions. In this chapter, an overview of the study field is presented to motivate collaborations between practitioners of UQ and researchers of the field. First, an overview of the physical phenomenon is introduced. Moreover, research methodologies are described with their identified sources of uncertainty. Next, the state-of-the-art modeling techniques are described together with their capabilities of replication of experiments. The characteristic modeling equations are presented as well. Finally, technologies for ice protection systems are deployed with the current regulation for flying in icing conditions.

In-flight Icing: Modeling, Prediction, and Uncertainty

Barbara Arizmendi Gutierrez;Myles Morelli;Gianluca Parma;Marta Zocca;Giuseppe Quaranta;Alberto Guardone
2021-01-01

Abstract

In-flight Icing consists of the accumulation of ice over the surfaces of flying crafts, namely aircraft and helicopters. It occurs when those fly through visible moisture, i.e., clouds, at temperatures below the freezing point. This phenomenon is undesirable because it compromises the safety and performance of the flying crafts. The physics of the phenomenon is complex, and it is still behind its full comprehension. Moreover, the characterization of the icing environments and their replication in experimental facilities is subject to large uncertainties. These might arise from phenomena like the complex physics of clouds, the accuracy of the measuring devices, or the resolution to reproduce flight and cloud properties, among others. This entails a reduction on the predictive accuracy of numerical models that seek to reproduce ice shapes and to assess the performance of ice protection systems. For these reasons, this research field could greatly benefit from the deployment of Uncertainty Quantification (UQ) techniques to account from epistemic and aleatory uncertainties in model predictions. In this chapter, an overview of the study field is presented to motivate collaborations between practitioners of UQ and researchers of the field. First, an overview of the physical phenomenon is introduced. Moreover, research methodologies are described with their identified sources of uncertainty. Next, the state-of-the-art modeling techniques are described together with their capabilities of replication of experiments. The characteristic modeling equations are presented as well. Finally, technologies for ice protection systems are deployed with the current regulation for flying in icing conditions.
2021
Optimization Under Uncertainty with Applications to Aerospace Engineering
978-3-030-60166-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1157804
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