Improving extreme offshore wind speed prediction by using deconvolution.

Area of Science:

• Ocean Engineering
• Environmental Science
• Statistical Modeling

Background:

• Offshore engineering faces challenges in predicting extreme environmental loads from wind and waves.
• Current extreme value prediction methods often rely on predefined statistical distribution classes, limiting their applicability.
• Structural reliability in offshore environments is critically dependent on accurate extreme value assessments.

Purpose of the Study:

• To propose an innovative method for predicting extreme values in offshore engineering.
• To develop a technique that does not assume specific extrapolation functional classes.
• To enhance the accuracy of forecasting environmental loads and assessing structural reliability.

Main Methods:

• Developed a novel extreme value prediction method based on intrinsic data qualities.
• Applied the method to two distinct wind speed datasets: simulated and real-world offshore measurements.
• Compared the forecast accuracy against the established Naess-Gaidai extrapolation method.

Main Results:

• The proposed method demonstrated effective prediction of extreme values.
• Forecast accuracy was evaluated against a traditional extrapolation technique.
• The new approach shows promise for improving reliability assessments in offshore structures.

Conclusions:

• The novel method offers a data-driven alternative to traditional statistical approaches for extreme value prediction.
• This technique enhances the understanding and prediction of extreme environmental loads in offshore engineering.
• Accurate extreme value prediction is crucial for ensuring the safety and reliability of offshore infrastructure.