Novel Reliability Method Validation for Floating Wind Turbines

Abstract

Wind turbines and associated parts are susceptible to cyclic stresses, including torque, bending, and longitudinal stress, and twisting moments. Therefore, research on the resilience of dynamic systems under such high loads is crucial for design and future risk‐free operations. The method described in this study is beneficial for multidimensional structural responses that have undergone sufficient numerical simulation or measurement. In contrast to established dependability methodologies, the unique technique does not need to restart the numerical simulation each time the system fails. Herein, it is demonstrated that it is also possible to accurately predict the probability of a system failure in the event of a measurable structural reaction. In contrast to well‐established bivariate statistical methods, which are known to predict extreme response levels for 2D systems accurately, this study validates a novel structural reliability method that is particularly suitable for multidimensional structural responses. In contrast to conventional methods, the novel reliability approach does not invoke a multidimensional reliability function in the Monte Carlo numerical simulation case. As demonstrated in this study, it is also possible to accurately anticipate the likelihood of a system failure in the case of a measurable structural reaction.

Deconvolution approach for floating wind turbines

“…The recommended method efficiently uses all the available information to precisely predict extreme values. Based on the overall effectiveness of the suggested strategy, it was found that the novel de‐convolution methodology could include environmental information and provide more accurate forecasts 36‐38,51‐56 …”

“…Based on the overall effectiveness of the suggested strategy, it was found that the novel deconvolution methodology could include environmental information and provide more accurate forecasts. [36][37][38][51][52][53][54][55][56] Figure 3 shows the final un-scaled results of the proposed de-convolution technique in this paper, namely the "shorter" decimal log scale f X PDF tail, extrapolated by de-convolution, along with "longer" data distribution tail and modified Weibull Naess-Gaidai (NG) extrapolation. Equidistant sampling was utilized to reduce the size of the "shorter" data collection by 50 times compared to the entire "longer" simulated data set.…”

Gaidai reliability method for long-term coronavirus modelling

“…Patient numbers from thirteen different Chinese administrative regions were chosen as components thus constituting an example of a thirteen dimensional (13D) dynamic biological system, according to Equation (2) . 39 – 41 …”

“…While in authors similarly used EVT to predict and detect anomalies of influenza epidemics. 41 – 44 China was chosen to test the methodology proposed in this study, because of its COVID-19 origin and extensive health observations and related research available online. 45 – 50 …”