Optical sensors network for structural health monitoring

The main goal of this thesis is to develop a methodology to collect and analyze large number of data coming from CFD and FEM simulations, in order to build an optical sensors network whose purpose is to monitor structural loads for future prognostic applications and for real-time monitoring. In this case study, the method will be applied to a solar panel powered UAV prototype developed by the ICARUS Team, which is very suitable for experimental purposes and could greatly benefit from this methodology, not only for structural health monitoring but also for indirect monitoring on other systems. Smart structures provide many advantages in maintenance, both in terms of inspection time needed and costs. Fiber Bragg Grating (FBG) sensors will be used for this application, produced in collaboration with Photonext Team. Aerodynamics CFD simulations are made using the software Siemens Star CCM+, allowing the computation of aerodynamic loads in different flight conditions. The loads are then applied to the FEM structural model and the analysis will be conducted using Altair Hyperworks. Data post-processing and optimization is done with MATLAB. A feedforward neural network is then trained using strains measured by the sensors placed inside the wing structure. In order to achieve a well trained network, several simulations will be carried out to build a significant dataset.