Aircraft icing severity analysis considering three uncertainty types

Based on the enhanced Messinger model proposed by Myers, an aircraft icing severity prediction model involving both parameter uncertainties and ontological ambiguity is proposed. In this method, the ice thickness at which glaze ice first appears is employed as the aircraft icing severity index. The random variables and interval variables are used to describe the uncertainties of the input parameters with effects on the icing severity index. In the process of predicting the aircraft icing severity grade, the membership function is introduced to describe ambiguity in how the icing severity grades are defined. To quantitatively measure the icing severity level, the generalized possibility index considering hybrid random and interval parameter uncertainties and ontological ambiguity is defined. Subsequently, the computational algorithm of the generalized possibility index is developed. Finally, the feasibility and rationality of the proposed icing severity analysis model and its solution method are demonstrated by a wind tunnel experiment.