Prediction of extreme cargo ship panel stresses by using deconvolution - Gaidai, Oleg; Yan, Ping; Xing, Yihan (Frontiers in Mechanical Engineering, 2022)
Extreme value predictions typically originate from certain statistical distribution functional classes to fit the data and are subsequently extrapolated. This paper intends to highlight an alternative way of extrapolation based on intrinsic properties of the data set itself and does not pre-assume any extrapolation functional class. The proposed novel extrapolation method can be utilised in engineering design. To illustrate this, the paper uses two examples to showcase the advantages of the proposed method. First, synthetic data from a non-linear Duffing oscillator was chosen as an example to illustrate the new method. The second example was an actual container ship sailing between Europe and America and experiencing huge deck panel stresses in severe weather. In this example, actual onboard measured data was obtained and used for the study. Further, this example represents a real and physical case which is challenging to model due to the non-stationary and highly non-linear natures of the wave-ship load responses. This is especially so in the case of extreme responses, where the role of second and higher-order responses tend to more prominent and contribute more. The prediction accuracy of the proposed method is also validated versus the Naess-Gaidai extrapolation method. Moreover, this paper discusses new methods of generic smoothing of the distribution tail irregularities due to the underlying data set scarcity.