Aerodynamic coefficient prediction of bio-inspired camber morphing wings with flexible surfaces using an explainable transformer

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dc.authoridRashid Jafari, Javad/0009-0007-7497-7892
dc.contributor.authorMowla, Md Najmul
dc.contributor.authorDurhasan, Tahir
dc.contributor.authorAsadi, Davood
dc.contributor.authorKesilmi, Zehan
dc.contributor.authorJafari, Javad Rashid
dc.date.accessioned2026-02-27T07:33:34Z
dc.date.available2026-02-27T07:33:34Z
dc.date.issued2026
dc.description.abstractBio-inspired morphing wings with flexible surfaces can enhance aerodynamic efficiency at low Reynolds numbers (Re), yet predicting their fluid-structure interaction remains challenging. We present PhysAero-MHANet, a physics-aware, interpretable deep learning framework coupled with controlled wind tunnel experiments for aerodynamic prediction of camber-morphing finite wings. The campaign yielded 911 samples spanning Re is an element of [3 x 10(4), 1 x 10(5)], camber deflections up to 10(degrees), and angles of attack from-18(degrees )to 18(degrees). Experiments showed up to 34% drag reduction at small angles of attack, a stall delay of approximate to 6(degrees), a maximum lift coefficient C-L,C-max approximate to 1.44, and a peak lift-to-drag ratio C-L/C-D approximate to 8.84. The proposed model is a transformer-based multi-task surrogate with physics-informed attention, hierarchical cross-feature fusion, and shapley additive explanations (SHAP) for interpretability. Against 11 machine-learning, deep-learning, and attention baselines, PhysAero-MHANet achieved R-2 approximate to 0.985 and MAPE < 12% across lift (C-L), drag (C-D), and rolling moment (C-M,C-R) predictions. These results provide new insight into morphing-wing aerodynamics and support real-time control, performance optimization, and integration into unmanned aerial vehicles (UAVs) and micro aerial vehicles (MAVs).
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUEBITAK) [223M340]
dc.description.sponsorshipThis research is supported by the Scientific and Technological Research Council of Turkey (TUEBITAK) under the TUEBIcenter dotTAK 1001 program, with project number 223M340.
dc.identifier.doi10.1016/j.ast.2025.111557
dc.identifier.issn1270-9638
dc.identifier.issn1626-3219
dc.identifier.urihttp://dx.doi.org/10.1016/j.ast.2025.111557
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4643
dc.identifier.volume170
dc.identifier.wosWOS:001658717500001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherElsevier France-Editions Scientifiques Medicales Elsevier
dc.relation.ispartofAerospace Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectPhysics-aware deep learning
dc.subjectCamber-morphing wings
dc.subjectFlexible aerodynamic surfaces
dc.subjectAerodynamic surrogate modeling
dc.subjectExplainable AI
dc.titleAerodynamic coefficient prediction of bio-inspired camber morphing wings with flexible surfaces using an explainable transformer
dc.typeArticle

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