Sensitivity approach for modeling Stochastic Field of Keulegan-Carpenter and Reynolds numbers through a matrix response surface

International audience The actual challenge for requalification of existing offshore structures through a rational process of reassessment indicates the importance of employing a response surface methodology. At different steps in the quantitative analysis, quite a lot of approximations are performe...

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Bibliographic Details
Published in:Journal of Offshore Mechanics and Arctic Engineering
Main Authors: Schoefs, Franck, Boukinda, Morgan
Other Authors: Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2009
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Online Access:https://hal.science/hal-01007279
https://hal.science/hal-01007279/document
https://hal.science/hal-01007279/file/FSMB.pdf
https://doi.org/10.1115/1.3160386
Description
Summary:International audience The actual challenge for requalification of existing offshore structures through a rational process of reassessment indicates the importance of employing a response surface methodology. At different steps in the quantitative analysis, quite a lot of approximations are performed as a surrogate for the original model in subsequent uncertainty and sensitivity studies. This paper proposes to employ a geometrical description of the nth order Stokes model in the form of a random linear combination of deterministic vectors. These vectors are obtained by rotation transformations of the wave directional vector. This facilitates introduction of an appropriate level of complexity in stochastic modeling of the wave velocity and of the Reynolds and Keulegan-Carpenter numbers for probabilistic mechanics analysis of offshore structures. In situ measurements are used to assess suitable ranges and distributions of basic variables.