Parallel spectral element method for guided wave based structural health monitoring

Abstract

Parallel implementation of the spectral element method is developed in which flat shell spectral elements are utilized for spatial domain representation. The implementation is realised by using Matlab Parallel Computing Toolbox and optimized for Graphics Processing Unit (GPU) computation. In this way, considerable computation speed-up can be achieved in comparison to computation on conventional processors. The implementation includes an interpolation of wavefield on a uniform grid. The method was tested on experimental data set available on the Open Guided Waves platform. The qualitative comparison was performed on full wavefield data, whereas quantitative comparison was made directly on signals of propagating Lamb waves registered by piezoelectric transducers. In both cases, good agreement between numerical and experimental results was achieved. The proposed method is particularly useful for structural health monitoring algorithms in which signal parameters are required such as wave velocity dependence on the angle of propagation. Moreover, it enables model–assisted damage size estimation in which the damage influence curve is estimated based on large numerical data sets and sparse experimental data. Due to relatively short computation times, large data sets can be generated and used for machine learning or other soft computing methods opening up new possibilities in health monitoring of metallic and composite structures.