A framework for offshore load environment modeling
In the present paper, we propose a novel decision analytical framework for systems modeling in the context of risk informed integrity management of offshore facilities. Our focus concerns the development of system models representing environmental loads associated with storm events. Appreciating tha...
| Published in: | Volume 7B: Ocean Engineering |
|---|---|
| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
American Society of Mechanical Engineers
2018
|
| Subjects: | |
| Online Access: | https://vbn.aau.dk/da/publications/15f9f6df-ca55-4cdd-bfea-e35f9e4e65ce https://doi.org/10.1115/OMAE2018-77674 http://proceedings.asmedigitalcollection.asme.org/volume.aspx?conferenceid=4034&volumeid=18608#tocHeading_66017 http://www.scopus.com/inward/record.url?scp=85055540433&partnerID=8YFLogxK |
| Summary: | In the present paper, we propose a novel decision analytical framework for systems modeling in the context of risk informed integrity management of offshore facilities. Our focus concerns the development of system models representing environmental loads associated with storm events. Appreciating that system models in general serve to facilitate the optimal ranking of decision alternatives, we formulate the problem of systems modeling as an optimization problem to be solved jointly with the ranking of decision alternatives. Taking offset in recent developments in structure learning and Bayesian regression techniques, a generic approach for the modeling of environmental loads is established, which accommodates for a joint utilization of phenomenological understanding and knowledge contained in databases of observations. In this manner, we provide a framework and corresponding techniques supporting the combination of bottom-up and topdown modeling. Moreover, since phenomenological understanding as well as analysis of databases may lead to the identification of several competing system models, we include these in the formulation of the optimization problem. The proposed framework and utilized techniques are illustrated on a principal example. The example considers systems modeling and decision optimization in the context of possible evacuation of an offshore facility in the face of an emerging storm event. |
|---|