Online damage detection for laminated composite shells partially filled with fluid

A general approach for the online damage detection of laminated composite shells partially filled with fluid (LCSFF) is proposed in this paper. Based on advanced composite damage mechanics and the interaction between the fluid and the composite shell, a finite element (FE) model, is first established to simulate the structural response of LCSFF with damage. Piezoelectric patches are used as sensors and actuators to realize automatic damage detections in this FE model. The FE model is validated using structural natural frequencies obtained from experiments. The change in the energy spectrum of the decomposed wavelet signals of structural dynamic responses between the intact and damaged structures is used as the damage index due to its high sensitivity to the structural damage status. The non-linear mapping relationships between the structural damage index and various damage status of the LCSFF are established using an artificial neural network (ANN) trained with numerical structural dynamic response data. Results show that the general approach proposed in this paper can successfully identify the damage status of LCSFF with satisfactory accuracy.