Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Catastrophic failures of partially or fully submerged structures, e.g., offshore platforms, hydrokinetic turbine blades, bridge decks, etc., due to the dynamic impact of free surface flows such as waves or floods have revealed the need to evaluate their reliability. In this respect, an accurate estimation of hydrodynamic forces and their relationsh...
Contexts in source publication
Context 1
... Var() defines the estimated variance of the output variable. Table 2 shows the cross-validation error of the tested surrogate models as combinations of different trend and observation functions. It can be observed that the estimated LOO errors vary with different combinations and outcomes of interest. ...Context 2
... can be observed that the estimated LOO errors vary with different combinations and outcomes of interest. In most of the cases, the combination of 3rd degree polynomial function and Matérn 3/2 correlation function results in the best performance with a minimum mean error of the prediction for the outcomes (highlighted in bold in Table 2). Also of note is that combinations of 2nd degree polynomialMatérn 3/2 and 3rd degree polynomial-Gaussian also exhibit a good prediction. ...Context 3
... of optimal surrogate models for the drag coefficient outcomes, i.e., mean and oscillation amplitude quantities, are shown in Figure 6, where the red dots represent the DOE. The estimated parameters of all six models for six quantities of interest are summarized in Table A2 (Appendix B). Once a surrogate model is built with its estimated parameters, the hydrodynamic pressure coefficients of an arbitrary design of and can be rapidly predicted without the need for an attempt at re-simulation. ...