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dc.contributor.authorTeng, Jian
dc.contributor.authorGao, Ziyi
dc.date.accessioned2026-08-20T21:15:01Z
dc.date.available2026-08-20T21:15:01Z
dc.date.copyright2025/11/19
dc.date.issued2026
dc.identifier.otherJAEEEZ.ASENG-6535.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314180
dc.description.abstractAbstractTraditional inlet total pressure distortion evaluation methods, which rely on dense sensor arrays, are costly and complex in industrial applications. This paper presents a deep learning approach using convolutional neural networks to predict total ...Practical ApplicationsThis work describes a deep learning workflow for estimating total pressure distortion in S-duct inlets from sparse sensor data. Traditional total pressure distortion assessments employ dense sensor arrays that are relatively ...
dc.publisherAmerican Society of Civil Engineers
dc.titleDeep Learning–Assisted Evaluation of Total Pressure Distortion in S-Duct Inlets from Sparse Sensor Data
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue2
dc.journal.titleJournal of Aerospace Engineering
dc.identifier.doi10.1061/JAEEEZ.ASENG-6535
dc.journal.fristpage04025126-1
dc.journal.lastpage04025126-13
dc.page13
dc.treeJournal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 002
dc.contenttypeFulltext


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