Towards an Efficient Digital Twin Framework for Fiber Composite Structures Using Optimal Sensor Placement Techniques

Abstract

Structural health monitoring (SHM) is a critical component in the safe and efficient operation of aerospace structures. Preliminary studies were carried out to investigate the suitability of sparse sensor measurements to estimate the response of fiber composite structures and predict their performance. The displacement response was approximated using a linear combination of a set of basis vectors, where the basis vectors were extracted from a snapshot matrix of the structure’s response under various loads. The coefficients of the basis vectors were assessed in real time using the sensor measurements at sparse locations chosen based on the pivot locations of the QR algorithm. This displacement map is then leveraged to compute critical SHM parameters such as the buckling load. The proposed method was applied to a fiber composite plate subjected to in-plane buckling loads. It was shown that the displacement vectors were mapped with L2-norm errors of mean 0.003% using measurements at just six locations, leading to stress distribution and buckling load estimations with very small errors.