Deep Learning–Based Method for Efficient Airfoil Rime Icing Prediction

dc.contributor.author	Xu, Zhao-Ke
dc.contributor.author	Dai, Hao
dc.contributor.author	Zhang, Hai-Jun
dc.date.accessioned	2026-08-20T21:13:18Z
dc.date.available	2026-08-20T21:13:18Z
dc.date.copyright	2025/08/05
dc.date.issued	2025
dc.identifier.other	JAEEEZ.ASENG-6214.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314137
dc.description.abstract	AbstractThe input feature parameters of current machine learning and deep learning–based wing icing prediction models primarily consist of flight states and atmospheric conditions, and the output data are ice shapes. This dependency necessitates many ...
dc.publisher	American Society of Civil Engineers
dc.title	Deep Learning–Based Method for Efficient Airfoil Rime Icing Prediction
dc.type	Journal Article
dc.journal.volume	38
dc.journal.issue	6
dc.journal.title	Journal of Aerospace Engineering
dc.identifier.doi	10.1061/JAEEEZ.ASENG-6214
dc.journal.fristpage	04025081-1
dc.journal.lastpage	04025081-11
dc.page	11
dc.tree	Journal of Aerospace Engineering:;2025:;Volume ( 038 ):;issue: 006
dc.contenttype	Fulltext