Predictive Modelling in Complex Uncertain Environments: Optimised exploitation of physics and data

This project aims to improve the current techniques used to assess the condition and safety of offshore and aerospace structures.

The platforms used by the Oil and Gas industry in the North Sea were designed to operate for around 25 years in total. Over 600 of these platforms have now reached the end of their design life and the decision must be taken as to whether they can continue to be used safely or whether they should be decommissioned. For new offshore wind turbines, it is critical to have a good understanding of current structural condition so that maintenance can be planned optimally - unscheduled maintenance and downtime is extremely costly, owing to the difficulty of accessing these structures. Equally, in the aerospace industry, the ability to follow a condition-based maintenance strategy will save much time and money in avoiding unscheduled/emergency repair work.

This project brings together researchers from the University of Sheffield, who are experts in Structural Health Monitoring and nonlinear system modelling, with industry experts who are leading the way in the monitoring and assessment of offshore and aerospace structures. The aim of this collaboration is to develop the most accurate means possible of assessing structural condition using monitoring data.

The approach that will be taken here will combine the latest developments in artificial intelligence with more traditional methods that exploit understanding of the physics at work. Predictive models based on well-understood physics can often fall short of being able to explain complex behaviour, such as the loading an offshore structure will experience in a changing sea-state. This is where learning from measured data can be used to augment the model and improve prediction at times when the physics doesn't explain the behaviour captured by the sensors.

The combination of physics and data-based models will be used to improve the prediction of the forcing a structure experiences from a changing environment. An accurate quantification of this enables one to calculate the stresses a structure has undergone, which leads to a prediction of its current condition. A similar modelling approach will be used to help make predictions about the structure itself.

Finally, as well as improving the accuracy of the methods used to assess structural condition, the project aims to quantify the amount of uncertainty inherent in the models and algorithms that will be implemented. This approach acknowledges the fact that it is not always possible to make an accurate prediction of structural condition at a given time, but allows a confidence level to be assigned to each assessment made. To make responsible and optimal decisions concerning the repair or decommission of a structure, understanding the level of confidence one has in an assessment of structural condition is absolutely key.

High-fidelity models of structures, informed by measurement data and expert domain knowledge, of the type proposed here, will ultimately allow operators to characterise performance, diagnose faults and deterioration and finally to predict how long the structures may continue to operate as designed, given current operating conditions. Although the application focus here is on offshore and aerospace structures, a new and general methodology is proposed here that will find applications across many industrial sectors, including chemical and process, ground transport and civil infrastructure.

Direct beneficiaries of the research here will be the industrial partners. The partners from the offshore sector are Ramboll (oil and gas extraction) and Siemens Gamesa (offshore wind). Safran Landing Systems (landing gear) and Dstl (military aircraft) are the partners from the aerospace sector.

In both of these sectors, enhanced predictive modelling capability is crucial for an efficient maintenance strategy. Reliable predictions of the dynamic response of structures enable a drive towards optimised condition-based maintenance. This is critical, particularly offshore, where structures operate in harsh and inaccessible environments; scheduled maintenance is consequentially costly and any unplanned maintenance extremely so (and can lead to large amounts of downtime). In the offshore wind context savings in operational costs can translate directly into reductions in the Levelised Cost of Electricity.

For offshore oil and gas, enabling a switch to condition-based maintenance is essential for lifetime extension. In the North Sea alone, over 600 platforms have exceeded their design lives. Where structures are believed to be undamaged and oil/gas resources persist, continued use is clearly desirable. A recent study estimates that reinforcing just 30 of these rigs in Denmark would cost 30 bn Danish Krone (DKK: approximately £3bn), while decommissioning would cost 15 bn DKK. Given that the UK has 360 rigs in the same situation in the North Sea, it is evident that extending the remaining life of the rigs is an issue of UK National Importance. The modelling capability developed here will inform and strengthen the decision-making process determining necessary actions towards the end of a structure's design life.

New, improved, wave force models will allow better control of uncertainty in load prediction, leading to more accurate estimation of fatigue accrual in structures. In the longer term and with wider applicability, any enhanced estimation of wave loading will inform the design of new offshore structures. This will be a major boon to offshore industry, as end users will be able to avoid the component overdesign that leads to economic and structural inefficiency.
The impact of the operational environment is equally difficult to quantify for aircraft; here enhanced modelling of structural response and, therefore, life assessment, is of equal importance. Access to this information allows extension of component use, therefore cost saving, and, importantly, the ability to schedule maintenance effectively. It has been estimated that predictive maintenance will increase aircraft availability by up to 35%, which would have major implications on the cost of air travel.

This fellowship paves the way to a new approach for predictive modelling. Beneficiaries in academia will be numerous, stretching from those in the SHM, structural nonlinearity and verification and validation research communities out to researchers working more generally in the modelling of dynamic processes. Dissemination will be achieved through conference presentations and publications, as well as at special-focus workshops hosted at the LVV. The data collected from the experimental campaigns will be made freely available, with wider dissemination impact achieved by developing YouTube videos of the large-scale tests.