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contributor authorTeng, Jian
contributor authorGao, Ziyi
date accessioned2026-08-20T21:15:01Z
date available2026-08-20T21:15:01Z
date copyright2025/11/19
date issued2026
identifier otherJAEEEZ.ASENG-6535.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314180
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 ...
publisherAmerican Society of Civil Engineers
titleDeep Learning–Assisted Evaluation of Total Pressure Distortion in S-Duct Inlets from Sparse Sensor Data
typeJournal Article
journal volume39
journal issue2
journal titleJournal of Aerospace Engineering
identifier doi10.1061/JAEEEZ.ASENG-6535
journal fristpage04025126-1
journal lastpage04025126-13
page13
treeJournal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 002
contenttypeFulltext


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