The aerospace industry increasingly relies on digital twins to estimate unmeasured quantities through Virtual Sensing techniques. In this context, this work presents the experimental validation of a computational fluid dynamics (CFD) model of an aluminum wing with a NACA 0018 profile. Wind tunnel measurements were collected at various flow conditions and compared against CFD simulations performed in Simcenter STAR-CCM+. The strong agreement, quantified through pressure distribution comparisons and Normalized Root Mean Square Error (NRMSE), confirms the reliability of the numerical model. Crucially, the validated CFD setup provides the basis for future implementation of Virtual Sensing schemes based on the Augmented Kalman Filter (AKF), enabling the estimation of aerodynamic pressure loads using limited sensor data. This validation step is thus essential to ensure the predictive quality of the digital twin in such Virtual Sensing frameworks for structural monitoring and control.

Coupled CFD-Wind Tunnel Assessment of a NACA 0018 Aluminum Wing for Integration in an AKF-Driven Virtual Sensing Framework

Chillemi M.
Writing – Original Draft Preparation
;
Crea R.
Software
;
Cucinotta F.
Supervision
;
Sfravara F.
Validation
;
2026-01-01

Abstract

The aerospace industry increasingly relies on digital twins to estimate unmeasured quantities through Virtual Sensing techniques. In this context, this work presents the experimental validation of a computational fluid dynamics (CFD) model of an aluminum wing with a NACA 0018 profile. Wind tunnel measurements were collected at various flow conditions and compared against CFD simulations performed in Simcenter STAR-CCM+. The strong agreement, quantified through pressure distribution comparisons and Normalized Root Mean Square Error (NRMSE), confirms the reliability of the numerical model. Crucially, the validated CFD setup provides the basis for future implementation of Virtual Sensing schemes based on the Augmented Kalman Filter (AKF), enabling the estimation of aerodynamic pressure loads using limited sensor data. This validation step is thus essential to ensure the predictive quality of the digital twin in such Virtual Sensing frameworks for structural monitoring and control.
2026
9783032149527
9783032149534
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3350295
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