March 23, 2018
Lachlan Astfalck is one of the PhD students in the Project 5: Floating Facility Data Analytics for Condition/Longevity Monitoring project stream. Lachlan’s work concentrates on applying recent advances in the probabilistic characterisation of computer models to vessel motion models currently used on the North West Shelf of Australia.
As Lachlan explains, “It is exciting to work between ocean engineers and statisticians in characterising uncertainties in offshore engineering models. It is hoped that the insights achieved from my research will make a difference to our understanding of ocean engineering operations.”
Bayesian statistics provide a natural framework to combine these sources of uncertainty, creating probabilistic predictions that incorporate our physical knowledge of the system, at the same time laying a foundation for formal decision making under uncertainty. The framework also allows researchers to reduce uncertainty in predictions by identifying which inputs are most influential on the model output and calibrating the model to actual observations of the process it attempts to mimic.
The reduction of uncertainty in predictions will ultimately lead to better forecasting, which in turn will allow for more efficient operations and production of offshore structures.