Liquid carbon storage tanker disaster resilience

One of the most important instruments used in engineering is the statistical prediction of extreme values. Engineering design should envisage structural disaster resilience, in particular to withstand harsh environmental conditions during system operations. For instance, extreme value analysis can forecast the extreme values of the environment's wind and waves as well as engineering responses and moments. Although a variety of statistical techniques are employed to forecast extreme values, it is imperative to improve statistical techniques in order to enable improved forecasting. The innovative deconvolution strategy put forth in this study is one such way. Data on measured wind speeds were utilized to benchmark and evaluate the approach. Additionally, this method is employed to foretell the extreme values of a unique subsea shuttle tanker (SST), a cutting-edge undersea freight tanker that transports CO2 to marginal fields. The novel deconvolution method’s results are validated against the modified Weibull extrapolation method. Time-domain-stimulated 2D planar Simulink model was used to generate representative underlying dynamic system dataset. The proposed methodology provides an accurate response to extreme value prediction utilizing all available data efficiently, which enables modelling and design optimizations of the SST. The proposed deconvolution method’s overall performance indicated that the extreme response prediction results of the dynamic vessel motion numerical simulations are robust and accurate. Significance and importance of this study lie within addressing state-of-art carbon capture and storage subsea system, in particular its safety and reliability design aspects.