Experimental shape sensing of a wing structure using SSB-iFEM: Static assessment and dynamic wind tunnel test

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Authors: Marco Esposito, Matteo Sorrenti, Marco Gherlone
Cited by: 7
References: 42

Abstract

Reconstructing the displacement field from discrete strain measurements, commonly known as shape sensing, plays a crucial role in the development of advanced Structural Health Monitoring (SHM) frameworks. Monitoring displacements throughout a structure’s operational life provides valuable data for predictive maintenance strategies and supports the implementation of digital twin technologies. Among the various shape-sensing techniques, the inverse Finite Element Method (iFEM) has emerged as a prominent solution. However, despite its demonstrated effectiveness, the practical application of iFEM remains limited by the requirement for back-to-back strain sensor configurations, i.e., sensors installed on both surfaces of a thin-walled structure. To overcome this limitation, a new variant called Single Sensor Based iFEM (SSB-iFEM) has recently been proposed. In this work, SSB-iFEM is employed to perform, for the first time, shape sensing on an entire aerospace structure: the half-wing of a commercial hotliner. The test setup reflects the complexity and constraints of real industrial conditions, as only limited structural information is available due to the commercial nature of the test article. Furthermore, the structure is instrumented exclusively on the accessible external surface and tested under simulated operating conditions in a wind tunnel. The experimental results demonstrate the high versatility and accuracy of SSB-iFEM, even when using a reduced set of strain sensors. This study proves that the proposed formulation successfully overcomes the main limitations of standard iFEM and significantly extends the applicability of shape sensing approaches to real-world aerospace structures.

Fields of Study & Subjects

Aeroelasticity and Vibration Control Structural Health Monitoring Techniques Model Reduction and Neural Networks

Publication details

Journal / source
Measurement
Publisher
Elsevier BV
Volume
265
Pages
120354–120354
Publication date
8 January 2026
Language
EN