Accelerated strategies for impact identification of composite laminate using sparse sensor networks and model-based virtual time reversal

Abstract

Barely visible impact damages are notorious threats to the health of composite structures. Detection of the impact is critical to promise the performance of composite structures. Recently, the authors proposed an impact identification method based on sparse sensor networks and virtual time reversal, which is verified on composite laminates. However, the computational demands of virtual time reversal simulations pose a notable limitation to this approach. To improve the efficiency of the method, several acceleration measures are developed in this article. First, the re-emitted wavefield analysis is accelerated using a frequency-domain method rather than the time-domain simulation, which largely reduces the computational costs. Subsequently, the GPU parallel acceleration algorithm is configured to further improve the efficiency. The proposed acceleration strategy is experimentally validated by various impact events on a composite laminate. The results render that the proposed acceleration strategy can promise the accuracy of the results of impact identification while significantly promoting computational efficiency, in which the time consumption plummets to only 1.94 s. Moreover, factors affecting the efficiency of the proposed method are discussed. The time consumption is positively correlated with the number of sensors involved in the identification. In addition, as the number of candidate points increases, the GPU encounters difficulty in handling all complex matrix operations simultaneously, leading to a substantial rise in computation time. Furthermore, the effect of signal lengths on the efficiency is minimal and largely negligible.