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This paper presents the fatigue reliability prediction for welded planar tubular joints of offshore fixed jacket support structures. A coupled model is employed to calculate the dynamic response of the jacket support structure under wind and wave loads. Stress concentration factors (SCFs) are evaluated using Efthymiou parametric equations, and the hot-spot stresses at eight locations on the chord and brace of the tubular joints are derived by summing the stress components from axial, in-plane, and out-of-plane actions. The stress distribution is determined using the rainflow counting method, fatigue damage from an S–N curve, and Miner's rule. Uncertainties associated with the fatigue lifetime evaluation procedure are identified, quantified, and modeled. The focus is specifically given to modeling the uncertainty related to the SCFs, and a framework is proposed to evaluate the fatigue reliability of the joint by incorporating individual uncertainties for each degree of freedom of the SCFs (i.e., axial load crown, axial load saddle, in-plane bending moment, and out-of-plane bending moment). The results reveal that assuming the same uncertainty level (Coefficient of Variation of 0.2) as recommended by DNV-RP-C210 for each degree of freedom of the SCFs and including the uncertainty after obtaining the stress distribution from rainflow counting does not consistently yield conservative reliability index values, as it depends on the critical hot spot. It is essential to note that the simplified assumptions derived from the oil and gas sector may not directly translate to the offshore wind industry due to differences in loading conditions. A reduction of up to approximately 24% in the reliability index is observed for one critical hot spot, while an increase of up to 20% was observed for another. Overall, this paper provides valuable insights into the complex interplay of factors affecting the reliability of joints in offshore fixed jacket support structures, shedding light on the need for tailored approaches to address uncertainties in the specific context of the offshore wind industry. • Fatigue reliability of welded tubular joints in offshore wind turbine (OWT) jacket. • The stress distribution is determined using the rainflow counting method. • Fatigue damage is assessed using an S–N curve and Miner's rule. • Incorporates individual uncertainties for each degree of freedom (DOF) of SCFs. • SCFs uncertainty most impacts hot spot reliability index in OWT jacket joints. • The same uncertainty in each DOF of SCFs does not ensure conservative reliability. [ABSTRACT FROM AUTHOR]

Fatigue reassessment within the decision process of lifetime extension might be uneconomical when individually performed for each turbine of a large offshore wind farm. This paper analyses the possibilities to extrapolate results from fatigue reassessment of wind turbines at selected design positions to other wind turbines of the wind farm. Five monopile-based turbines placed in a generic offshore wind farm were assessed with integrated aero-hydro-elastic simulations. A fatigue assessment was performed for each out of the five selected turbines using site specific environmental data commonly available during the design process. The results were compared to a fatigue reassessment where environmental data were modified in order to account for changes in environmental conditions during the service lifetime of the wind turbines. Results indicate that an extrapolation is feasible for selected parameters when changes in environmental conditions are small. This is an important step towards an effective and efficient assessment methodology for lifetime extension of offshore wind turbines. [ABSTRACT FROM AUTHOR]

In harsh sea conditions, it is possible for offshore wind turbines (OWTs) to be exposed to slamming loads due to breaking waves, especially plunging breaking waves. These slamming loads lead to significant structural responses and can affect the ultimate limit state (ULS) design and the fatigue limit state (FLS) design of OWTs. However, detailed consideration of slamming loads is not a common practice in the design of primary structures in offshore wind industry. Studies on integrated dynamic analysis of OWTs with consideration of slamming loads are very limited. When applying slamming loads on OWTs, several aspects should be considered, such as the detection of breaking waves, the calculation of slamming loads, and the approaches to integrate the slamming loads in fully coupled analysis, etc. This paper provides an extensive review of key issues concerning these aspects, which can benefit the application of slamming loads on OWTs. [ABSTRACT FROM AUTHOR]

Abstract: Among several possible support structure types for offshore wind turbines, a full-height lattice tower is one design option. Advantages of this design are the smaller amount of steel used for the structure compared to other concepts, and the possibility to install the whole structure in one operation. Based on the complexity of dynamic loadings on the support structure by wind and wave as well as operational loads, an initial lattice tower design with constant member dimensions over the tower height shows a large optimization potential and can be optimized section by section. This paper presents basic considerations for an iterative optimization approach and identifies sensitivities for the optimization process of a full-height lattice tower. It was found that an analysis with constant member dimensions over the tower height gives an indication about the required dimensions for an optimized design. [Copyright &y& Elsevier]

Lifetime extension needs low-cost assessments that can identify the remaining useful life of offshore wind monopiles. A novel concept for load monitoring was developed that only needs strain gauges installed at one level of the support structure. Damage equivalent loads were calculated from strain measurements and extrapolated along a monopile using a regression algorithm. In this paper, the assumptions behind the load extrapolation algorithm were verified with two consecutive months of measurement data from an offshore wind park. The verification was performed separately for two offshore wind turbines. Both turbines had strain gauges installed at a distance of approximately 15 m and 25 m. Results show that monthly damage equivalent loads can be predicted with errors smaller than 4% based on measurement data only. Prediction using linear regression resulted in similar results for the total fatigue damage as a nonlinear k-nearest neighbor approach, but individual 10-min damage equivalent loads showed larger differences than for the more robust k-nearest neighbor algorithm, especially for small loads. These results are very promising and should motivate further research. • Strain gauges installed at one level of the transition piece can be used to monitor loads of the monopile also. • Measured loads can be extrapolated along a monopiles using aero-hydro-elastic simulations and a regression algorithm. • Verification with data from two offshore wind turbines confirms good performance of the load extrapolation algorithm. • Results are similar for two offshore wind turbines located in the same wind farm. • Results are insensitive to the distance between measurement and extrapolation location. [ABSTRACT FROM AUTHOR]

In large offshore wind farms fatigue loads on support structures can vary significantly due to differences and uncertainties in site conditions, making it necessary to optimize design clustering. An efficient probabilistic fatigue load estimation method for monopile foundations was implemented using Monte-Carlo simulations. Verification of frequency domain analysis for wave loads and scaling approaches for wind loads with time domain aero-elastic simulations lead to 95% accuracy on equivalent bending moments at mudline and tower bottom. The computational speed is in the order of 100 times faster than typical time domain tools. The model is applied to calculate location specific fatigue loads that can be used in deterministic and probabilistic design clustering. Results for an example wind farm with 150 turbines in 30–40 m water depth show a maximum load difference of 25%. Smart clustering using discrete optimization algorithms leads to a design load reduction of up to 13% compared to designs based on only the highest loaded turbine position. The proposed tool improves industry-standard clustering and provides a basis for design optimization and uncertainty analysis in large wind farms. [ABSTRACT FROM AUTHOR]