Experimental-based Gaidai multidimensional reliability assessment approach for wind energy harvesters

Abstract

Dynamic Energy Harvesters (EH) playing nowadays significant role within green/renewable energy engineering, thus, in addition to numerical modelling, thorough lab/experimental research, as well as multimodal structural design and reliability approaches being required for operational longevity and safety. Performance of a particular EH device had been examined in this investigation, utilizing extensive lab wind tunnel tests, provided realistic range of windspeeds. Presented study offers state-of-the-art multidimensional structural risk assessment methodology, particularly suitable for multimodal nonlinear dynamic EH systems. Multidimensional dynamic system reliability can be analyzed via direct Monte Carlo Simulations (MCS) or via physical measurements, conducted across a representative period, resulting in jointly quasi-ergodic timeseries, representing EH multidimensional system's dynamics. Presented study demonstrated that the proposed multimodal risk assessment methodology was able to accurately forecast EH system's damage and failure risks, based on lab measured dynamics. High dimensionality along with complex inters-correlations between structural EH system's components may present challenge for existing reliability assessment methodologies, as those are mostly limited to univariate or at most bivariate reliability analyses. Presented study's main objective was to establish a novel multidimensional structural reliability assessment methodology, enabling relevant excessive dynamics information to be extracted from experimentally recorded/measured time-histories. Advocated multimodal, multidimensional reliability methodology enables efficient, yet accurate prognostics of structural damage (failure) risks for a variety of nonlinear dynamic systems.

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