A Digital Twin for Offshore Mooring Chains based on Acoustic Emission Monitoring

Abstract

High energy demand of contemporary society increasingly relies on offshore production. Offshore floating energy production infrastructure, such as floating wind turbines, photovoltaics, and production storage and offloading units, is central to sustaining the energy supply. Mooring chains, which are crucial elements of these floating units, are particularly vulnerable to degradation mechanisms, such as corrosion, fatigue, and their combination. Their frequent inspection is critical to prevent structural failure, production shutdowns, and severe environmental consequences.

A Digital Twin (DT) is a virtual representation of a physical object that uses real-time data to simulate and predict its behaviour. Digital twins have emerged as promising tools, enhancing the monitoring and management of physical assets using real-time data and predictive models. The effectiveness and reliability of a DT for quantitative integrity assessment are contingent on the quality of the input data and the models employed. When paired with predictive models, DTs offer valuable insights for managing the integrity of offshore mooring systems.

The main contribution of this work is to lay the groundwork for enrichment of DTs for mooring chains with quantitative damage assessment. Assessing the structural integrity of submerged offshore mooring chains presents significant challenges due to their difficult accessibility and the complexities involved in subsea inspections. Additionally, the presence of marine growth on the chain links often requires cleaning before detailed inspections can be conducted. The cleaning process is often undesirable from technical, economic, and environmental perspectives. Currently, no in-water inspection techniques for mooring chains have been reported that do not require the removal of marine growth beforehand. Enabling this is another contribution area of this research.

This thesis proposes a novel non-contact acoustic emission (AE) monitoring approach in underwater environments, enhancing DTs of mooring chains without suffering from the limitations of conventional inspection methods. As a passive ultrasound method, AE is an established non-destructive testing (NDT) technique to detect and monitor corrosion and fatigue. Piezoelectric sensors, typically mounted in contact with the material surface, are often employed to capture high-frequency waves generated by damage initiation and propagation. Three key areas have been explored: (i) the feasibility of detecting and monitoring corrosion-fatigue in submerged conditions, both with and without marine growth, using non-contact AE, (ii) the construction of a DT representation based on AE data to identify the location of corrosion-fatigue damage in mooring chain links, and (iii) the integration of AE data with fatigue models to enhance the DT predictive capabilities for damage prognosis.

The feasibility of monitoring corrosion-fatigue damage in submerged conditions using non-contact AE monitoring has been explored using small-scale corrosion-fatigue experiments. The results demonstrated the effectiveness of the proposed approach, with corrosion-fatigue-induced ultrasound signals being detected with a satisfactory signal-to-noise ratio within the frequency range of 50–450 kHz. Cumulative and rate values of AE parameters provided a reliable representation of damage progression, successfully identifying four distinct stages of damage evolution. AE energy, in particular, proved to be the most promising indicator, especially for highlighting crack formation and rapid growth phases. Corrosion-fatigue-induced signals exhibited significantly higher energy levels, approximately an order of magnitude greater than those induced by corrosion alone. In addition, corrosion-induced damage resulted in fewer ultrasound signals than corrosion-fatigue damage. Further experiments demonstrated that simulated crack signals could be measured on steel plates both with and without marine growth, indicating that ultrasound waves in the frequency range of interest can penetrate marine growth and be detected by non-contact AE transducers in submerged conditions. While marine growth caused a noticeable drop in signal amplitude, the findings suggest that non-contact AE monitoring remains feasible in the presence of marine growth.

Large-scale experiments have been used to construct a DT representation based on AE data to identify the location of corrosion-fatigue damage in mooring chain links. Large-scale corrosion and fatigue tests were conducted to assess the feasibility of detecting, localising, and monitoring corrosion and fatigue damage in mooring chains. The results demonstrated the effectiveness of the proposed approach in monitoring growing damage over time. AE measurements were parameterised to track acoustic activity associated with the initiation and progression of corrosion-fatigue damage. A 3D source localisation algorithm was successfully implemented to localise damage-induced ultrasound signal sources, with the localisation results being mapped onto the surfaces of the mooring chain segment. In the fatigue test, the AE-based DT identified three distinct zones of acoustic activity, which aligned well with post-failure inspection results. The remote AE technique accurately detected and localised all damage indications found during post-failure mechanical testing, showcasing its potential for real-time damage detection and localisation in mooring chain links.

Large-scale fatigue test data were subsequently used to assess the feasibility of fatigue crack growth prognosis in submerged mooring chains using remote AE monitoring. A prognosis model, based on the Paris relation and AE energy, was proposed, and its predictive capabilities were evaluated. The results demonstrated the potential of remote AE monitoring to predict fatigue crack growth in submerged mooring chains. AE energy analysis at various test stages revealed distinct phases of crack growth, including initiation, stable propagation, and acceleration toward failure. The prognosis model effectively reflected these stages, as indicated by changes in AE energy rate. A sensitivity analysis on the model parameters showed that reducing the scaling coefficient B led to overestimated crack growth and shorter predicted fatigue life, while increasing it resulted in underestimated crack growth and longer predicted fatigue life. The power-law exponent p further amplified these effects. The fatigue life predictions underscored the importance of accurately determining the initial crack size and selecting the appropriate crack growth models to improve prognosis accuracy.

This research provides a foundation for enhancing DTs of offshore mooring chains through real-time monitoring and predictive analysis. The proposed system shows potential for autonomous inspections of subsea structures, with possible benefits including reduced inspection costs, condition-based maintenance, and improved safety. Its non-intrusive nature may also lessen disturbance to marine ecosystems. Demonstration under offshore conditions will be an important next step to assess and strengthen its practical value for supporting the safety, efficiency, and sustainability of floating energy infrastructure.