| dc.contributor.author | Teng, Jian | |
| dc.contributor.author | Gao, Ziyi | |
| dc.date.accessioned | 2026-08-20T21:15:01Z | |
| dc.date.available | 2026-08-20T21:15:01Z | |
| dc.date.copyright | 2025/11/19 | |
| dc.date.issued | 2026 | |
| dc.identifier.other | JAEEEZ.ASENG-6535.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314180 | |
| dc.description.abstract | AbstractTraditional 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.publisher | American Society of Civil Engineers | |
| dc.title | Deep Learning–Assisted Evaluation of Total Pressure Distortion in S-Duct Inlets from Sparse Sensor Data | |
| dc.type | Journal Article | |
| dc.journal.volume | 39 | |
| dc.journal.issue | 2 | |
| dc.journal.title | Journal of Aerospace Engineering | |
| dc.identifier.doi | 10.1061/JAEEEZ.ASENG-6535 | |
| dc.journal.fristpage | 04025126-1 | |
| dc.journal.lastpage | 04025126-13 | |
| dc.page | 13 | |
| dc.tree | Journal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 002 | |
| dc.contenttype | Fulltext | |