UKERC Energy Data Centre: Projects

Projects: Projects for Investigator
UKERC Home >> UKERC Energy Data Centre >> Projects >> Choose Investigator >> All Projects involving >> EP/S001565/1
 
Reference Number EP/S001565/1
Title Predictive Modelling in Complex Uncertain Environments: Optimised exploitation of physics and data
Status Completed
Energy Categories FOSSIL FUELS: OIL, GAS and COAL(Oil and Gas, Other oil and gas) 35%;
RENEWABLE ENERGY SOURCES(Wind Energy) 35%;
NOT ENERGY RELATED 30%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr E Cross
No email address given
Mechanical Engineering
University of Sheffield
Award Type Standard
Funding Source EPSRC
Start Date 29 June 2018
End Date 28 January 2022
Duration 43 months
Total Grant Value £579,374
Industrial Sectors Construction
Region Yorkshire & Humberside
Programme ISCF - Skills
 
Investigators Principal Investigator Dr E Cross , Mechanical Engineering, University of Sheffield (100.000%)
  Industrial Collaborator Project Contact , DSTL - Defence Science and Technology Laboratory (0.000%)
Project Contact , Los Alamos National Laboratory, USA (0.000%)
Project Contact , University of California, USA (0.000%)
Project Contact , University of California, San Diego, USA (0.000%)
Project Contact , École polytechnique fédérale de Lausanne (EPFL), Switzerland (0.000%)
Project Contact , Siemens AG, Germany (0.000%)
Project Contact , Prowler.io (0.000%)
Project Contact , Safran Landing Systems UK Ltd (0.000%)
Project Contact , Ramboll Group A/S, Denmark (0.000%)
Web Site
Objectives
Abstract 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.
Publications (none)
Final Report (none)
Added to Database 07/02/19