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Rapid Lift-to-Drag Ratio Prediction for NACA Four-Digit Airfoils Using a Deep Neural Network

dc.contributor.authorTekin, Abdurrahman
dc.contributor.authorXiao, Tianhang
dc.contributor.authorYu, Xiongqing
dc.date.accessioned2026-08-20T21:13:37Z
dc.date.available2026-08-20T21:13:37Z
dc.date.copyright2025/09/18
dc.date.issued2026
dc.identifier.otherJAEEEZ.ASENG-6258.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314145
dc.description.abstractAbstractAirfoil optimization is crucial for enhancing aerodynamic performance and efficiency in aerospace engineering because accurate and rapid predictions of the lift-to-drag ratio (CL/CD) directly influence design decisions. However, traditional methods are ...
dc.publisherAmerican Society of Civil Engineers
dc.titleRapid Lift-to-Drag Ratio Prediction for NACA Four-Digit Airfoils Using a Deep Neural Network
dc.typeJournal Article
dc.journal.volume39
dc.journal.issue1
dc.journal.titleJournal of Aerospace Engineering
dc.identifier.doi10.1061/JAEEEZ.ASENG-6258
dc.journal.fristpage04025106-1
dc.journal.lastpage04025106-12
dc.page12
dc.treeJournal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 001
dc.contenttypeFulltext


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