Renewed interest in wave impact assessment has risen for various reasons:

• The low airgap of some existing Mobile Units in the North Sea

• The COSL Innovator incident and related to this topic the new DNV-GL guidelines (OTG 13 and OTG 14).

• the installation of many large-diameter monopile foundations for wind turbines in increasingly deep water in the North Sea.

• The installation of many large-diameter wind turbines in increasingly deep water in the North Sea.

• Seabed subsidence (and maybe water level rises due to global warming) and their effect on the decreasing airgap of fixed platforms.

Wave impact assessment has been the subject of many recent studies and research projects, and there has been a strong knowledge and tool development during the last decade, both within model testing and numerical (CFD) analysis (Huang et.al (2017), de Ridder et.al, (2017), Vestbøstad et. al. (2017), Bunnik et.al. (2018)). However, there is still a lack of efficient methods and tools to properly analyze wave impacts and derive the statistical variation of these impacts in the sea states to which these structures are exposed during their lifetime.

To reduce the statistical uncertainties that are naturally arising in estimates of design loads related to extreme waves, sufficient data must be gathered. In order to estimate the design loads it is common practice not to investigate all possible sea states (i.e. long-term analysis) but to investigate a few sea states and assume that the design value occurs at a prescribed probability level in the sea states with the same probability level (i.e. contour line approach). The estimate of the design value at that probability level is then based on results from a limited number of random realizations of these sea states. For linear or weakly nonlinear response types it is possible to estimate design loads accurately with a quite limited number of realizations. For strongly nonlinear problems however this is not possible due to the large statistical variation in the maximum observations, inherent to a random nonlinear process. Estimating accurately the tail of the load distribution requires many more realizations. This approach is restricted by time and costs and eventually one may have to accept an estimated design load with a large statistical uncertainty and account for the uncertainty with a higher safety margin.

In this paper an improved methodology for estimating design loads related to extreme wave impacts will be presented. The methodology is based on screening many 3-hour realizations of the design sea states with simplified, fast but sufficiently accurate methods and to focus only on the potentially critical events with a model containing a more complete description of the physics. This can be either a model test or a non-linear impact simulation (i.e. CFD analysis). By doing this many more rare/critical events can be assessed, reducing the statistical uncertainty in the estimate of the design load. A screening method/wave impact indicator will be presented for a jacket platform and for a fixed offshore wind turbine. Existing model test data is used to show the correlation between indicator and actual impact events and to derive the efficiency of the impact indicators.

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