| contributor author | Xu, Zhao-Ke | |
| contributor author | Dai, Hao | |
| contributor author | Zhang, Hai-Jun | |
| date accessioned | 2026-08-20T21:13:18Z | |
| date available | 2026-08-20T21:13:18Z | |
| date copyright | 2025/08/05 | |
| date issued | 2025 | |
| identifier other | JAEEEZ.ASENG-6214.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314137 | |
| 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | Deep Learning–Based Method for Efficient Airfoil Rime Icing Prediction | |
| type | Journal Article | |
| journal volume | 38 | |
| journal issue | 6 | |
| journal title | Journal of Aerospace Engineering | |
| identifier doi | 10.1061/JAEEEZ.ASENG-6214 | |
| journal fristpage | 04025081-1 | |
| journal lastpage | 04025081-11 | |
| page | 11 | |
| tree | Journal of Aerospace Engineering:;2025:;Volume ( 038 ):;issue: 006 | |
| contenttype | Fulltext | |
