On the Integration of SHM and Digital Twin for the Fatigue Assessment of Naval Surface Ships

Abstract

The integration of structural health monitoring data with multi-scale, multiphysics, probabilistic models can be used to help track the status of assets and aid in decision support. This is the paradigm laid forth by researchers working in the field of digital twin. The scope and complexity of this field is so large and so vast, that the development of the methodology will continuously evolve as the technology and capabilities monitoring devices and numerical models continuously progress. However, the implementation of a cyber-physical system, even with the limitations of current technology, can lead to essential information necessary to implement condition based maintenance for naval ship structures. The objective of this paper is to demonstrate the use of strain gauge data coupled with a global finite element model and a fatigue damage assessment model to provide information pertinent to maintenance and life cycle decisions. An illustrative example is presented and identifies the relative contributions of each step of the digital twin process to the overall uncertainty. This includes both epistemic uncertainties introduced through different modeling techniques, and aleatory uncertainties associated with the natural variability of structural capacity. The benefits of monitoring the ship and utilizing the digital twin framework is discussed, along with the challenges and limitations.