Wave-based numerical methods for damage identification in components and structures

Components and structures accumulate damage during operation, which degrades their load bearing capacity and is prone to causing catastrophic failure. The demand for fuel efficiency and reduction of pollutant emissions has shifted the design of many structures, predominantly aerospace, to incorporate more composite materials. Composite materials are especially susceptible to critical failure due to operation-induced and accidental damage modes, that have adverse impact on the material strength. Timely detection and identification of damage is important in ensuring structural integrity and safety. Continuous and reliable condition monitoring of components is even more important in lightweight structures that have lower loadbearing redundancy. Recent advances in sensors and signal processing, along with the availability of computational power, have rendered model-based monitoring and damage identification solutions attractive. Computational models for wave simulation remain, however, too heavy for conventional use. Robust and efficient modelling of certain damage modes, such as cracks, introduces additional complexities in numerical models for solids. Computational cost for inverse schemes, where multiple solutions for the unknown and sought damage parameters are required, even becomes prohibitive. This work introduces mesh-independent modelling of damage through XFEM, in wave analysis. The behaviour of damage is investigated with the developed method, and validated by established explicit Finite Element models. A signal processing methodology with wavelet transform is also implemented to further investigate the feasibility of wave scattering as means of damage identification, with a view over available wave actuation and measurement methods. The proposed methodology can achieve significant model reduction calculating wave scattering. Furthermore, identification of cracks is feasible, provided multiple wavemodes can be identified and measured

Wave-based numerical methods for damage identification in components and structures

Components and structures accumulate damage during operation, which degrades their load bearing capacity and is prone to causing catastrophic failure. The demand for fuel efficiency and reduction of pollutant emissions has shifted the design of many structures, predominantly aerospace, to incorporate more composite materials. Composite materials are especially susceptible to critical failure due to operation-induced and accidental damage modes, that have adverse impact on the material strength. Timely detection and identification of damage is important in ensuring structural integrity and safety. Continuous and reliable condition monitoring of components is even more important in lightweight structures that have lower loadbearing redundancy. Recent advances in sensors and signal processing, along with the availability of computational power, have rendered model-based monitoring and damage identification solutions attractive. Computational models for wave simulation remain, however, too heavy for conventional use. Robust and efficient modelling of certain damage modes, such as cracks, introduces additional complexities in numerical models for solids. Computational cost for inverse schemes, where multiple solutions for the unknown and sought damage parameters are required, even becomes prohibitive. This work introduces mesh-independent modelling of damage through XFEM, in wave analysis. The behaviour of damage is investigated with the developed method, and validated by established explicit Finite Element models. A signal processing methodology with wavelet transform is also implemented to further investigate the feasibility of wave scattering as means of damage identification, with a view over available wave actuation and measurement methods. The proposed methodology can achieve significant model reduction calculating wave scattering. Furthermore, identification of cracks is feasible, provided multiple wavemodes can be identified and measured