Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states

Eckert-Gallup, Aubrey C.; Sallaberry, Cédric J.; Dallman, Ann R.; Neary, Vincent S.

doi:10.1016/j.oceaneng.2015.12.018

Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states

Journal Article · Wed Jan 06 00:00:00 EST 2016 · Ocean Engineering

DOI:https://doi.org/10.1016/j.oceaneng.2015.12.018· OSTI ID:1237665

Eckert-Gallup, Aubrey C. ^[1]; Sallaberry, Cédric J. ^[1]; Dallman, Ann R. ^[1]; Neary, Vincent S. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (H_s) and either energy period (T_e) or peak period (T_p) values calculated for a given recurrence interval using a set of data based on hindcast simulations or buoy observations over a sufficient period of record. The use of the inverse first-order reliability method (I-FORM) is a standard design practice for generating environmental contours. This paper develops enhanced methodologies for data analysis prior to the application of the I-FORM, including the use of principal component analysis (PCA) to create an uncorrelated representation of the variables under consideration as well as new distribution and parameter fitting techniques. As a result, these modifications better represent the measured data and, therefore, should contribute to the development of more realistic representations of environmental contours of extreme sea states for determining design loads for marine structures.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1237665

Alternate ID(s):: OSTI ID: 1512898
OSTI ID: 1427239
OSTI ID: 1359711

Report Number(s):: SAND--2015-1444J; SAND--2015-5328J; SAND--2015-9228J; PII: S0029801815006721

Journal Information:: Ocean Engineering, Journal Name: Ocean Engineering Journal Issue: C Vol. 112; ISSN 0029-8018

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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