Technologies based on optical fibers provide the possibility of installing relatively dense networks of sensors that can perform effective strain sensing functions during the operational life of structures. A contemporary trend is the increasing adoption of composite materials in aerospace constructions, which leads to structural architectures made of large monolithic elements. The paper is aimed at showing the feasibility of a detailed reconstruction of the strain field in a composite spar, which is based on the development of reference finite element models and the identification of load modes, consisting of a parameterized set of forces. The procedure is described and assessed in ideal conditions. Thereafter, a surrogate model is used to obtain realistic representation of the data acquired by the strain sensing system, so that the developed procedure is evaluated considering local effects due to the introduction of loads, significant modelling discrepancy in the development of the reference model and the presence of measurement noise. Results show that the method can obtain a robust and quite detailed reconstruction of strain fields, even at the level of local distributions, of the internal forces in the spars and of the displacements, by identifying an equivalent set of load parameters. Finally, the trade-off between the number of sensor and the accuracy, and the optimal position of the sensors for a given maximum number of sensors is evaluated by performing a multi-objective optimization, thus showing that even a relative dense network of externally applied sensors can be used to achieve good quality results.
A. Airoldi, L. Marelli, P. Bettini, G. Sala, and A. Apicella, "Strain field reconstruction on composite spars based on the identification of equivalent load conditions," Proc. SPIE 10168, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2017, 101680V (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 27, 2017; Published: 12 April 2017); https://doi.org/10.1117/12.2260161.
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