Limit hypersurface state-of-the-art damage assessment approach for a galloping energy harvester, accounting for memory effects

Abstract

Energy harvesters (i.e., EH) constituting nowadays an essential part of renewable energy engineering; hence, apart from numerical modeling, experimental studies are necessary, constituting reliable input for durable design and structural safety assessments. In the current study, galloping EH’s performance was analyzed, utilizing extensive laboratory wind-tunnel tests, conducted under realistic windspeed conditions. The novel structural multivariate risks assessment methodology, presented in the current study is particularly feasible for multi-dimensional nonlinear EH dynamic systems, that have been either directly Monte Carlo (i.e., MC) numerically simulated or physically measured over a representative temporal lapse, providing piecewise ergodic time series. As shown in this analysis, the suggested multivariate methodology accurately enables accurate predictions of the EH dynamic system’s failure/hazard or damage risks, based on laboratory-measured EH system’s dynamics. Furthermore, nonlinear inter-correlations between various systems’ critical components are not always easily handled by classic risk assessment techniques, when dealing with a high-dimensional system’s raw time series. The primary goal of this study was validation and benchmarking the novel risk assessment methodology, which can extract pertinent information, contained within the EH system’s dynamics, based on lab-recorded time histories. In conclusion, the novel hypersurface methodology presented in this study is generic, providing additional capability to accurately, yet efficiently predict damage/failure risks for a variety of nonlinear EH multidimensional systems. Relatively narrow confidence bands have been reported for the forecasted damage and failure levels, indicating both the robustness of the experimental setup, as well as practical design virtues of the advocated Gaidai hypersurface risks assessment methodology. Note that the presented methodology being mathematically exact does not rely on pre-assumptions and yet it is of general purpose.