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Journal of Environmental Accounting and Management 11(3) (2023) 329--339 | DOI:10.5890/JEAM.2023.09.006

Pan Pan$^1$, Ming Xue$^1$, Ying Zhang$^1$, Zhangsong Ni$^1$, Zixu Wang$^2$

$^1$ Chengdu Fluid Dynamics Innovation Center, Chengdu 610072, China

$^2$ China Aerodynamics Research and Development Center, Mianyang 621000, China

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Abstract

In this paper, \replaced[id=Reviewer1,comment=5]{a new aircraft icing prediction scheme is proposed to obtain the aircraft icing shape from common meteorological parameter.}{we propose a new aircraft icing prediction scheme to obtain the aircraft icing shape from common meteorological parameter.} Machine learning modeling is used to establish the mapping between meteorological parameters and in-cloud microphysical parameters based on a random forest algorithm. The outputs of machine learning model, median volume diameter (MVD) and liquid water content (LWC), are utilized as input parameters to simulate ice accretion for a specific airfoil, and the final icing shape is determined. The present work \replaced{shows}{showes} that in-cloud microphysical parameters might have some relationship with common meteorological parameters, and random forest \replaced{shows}{show} better performance in prediction of in-cloud microphysical parameters. The research work has brought about a quantitative prediction scheme of aircraft icing that shows high engineering practical value in route planning, aviation meteorological warning and airworthiness certification, etc.

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