期刊
OCEAN ENGINEERING
卷 245, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2021.110374
关键词
Long-term extreme response; Environmental contour; Response based analysis; Statistical model; Serial correlation
资金
- EPSRC, United Kingdom Supergen Offshore Renewable Energy Hub [EP/S000747/1]
- EPSRC [EP/S000747/1] Funding Source: UKRI
This study compares different methods for estimating the long-term extreme response of marine structures and finds that most methods have large errors compared to response-based analysis. The fitted statistical models are identified as a significant source of error, and most contour methods do not consider serial correlation in the data. The choice of contour method introduces significant errors into long-term response estimates, with contours approximating the failure region as having a linear boundary showing relatively small errors for unimodal response types.
Environmental contours are a pragmatic and widespread method to estimate the long-term extreme response of marine structures. Over the years, a range of approaches have been proposed. A benchmarking study was recently conducted to compare the various methods using a common set of data. The current work extends this benchmark study by providing a quantitative assessment of the contours submitted to the exercise. The estimates of long-term responses from the contours were compared against a response-based analysis (RBA) for a wide range of responses. While some contour methods agreed well with estimates from the RBA (relative errors less than 10%), most methods were found to give large errors relative to the RBA. For the 1-year responses most methods showed a large positive bias, whilst both positive and negative biases were found for the 20-year responses. The reasons for the differences between the contours and RBA were explored. It was shown that the fitted statistical models accounted for a large portion of the error in some approaches, with both positive and negative biases of the order of 50% for some contributions, depending on the response type. Whilst for other methods, the statistical model gave accurate predictions for most responses, no models were able to capture all response behaviours for all locations. Secondly, most contour methods do not account for serial correlation in the data. It is shown that this introduces a significant positive bias into long-term response estimates, especially for lower return periods. The level of error introduced by the type of contour method is dependent on the assumption made about the shape of the failure region in the contour definition. For the predominantly unimodal response types considered, contours which approximate the failure region as having a linear boundary (IFORM and direct sampling contours), introduce relatively little error for most responses. However, for some responses, the direct sampling contours were found to introduce errors in the range 20%-40%, depending on the variable space in which they are constructed. The ISORM and highest density contours were found to have a significant over-conservatism bias, which would be expected for the response types considered.
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