Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety, Given Manufacturing Imperfections

Energy harvesting is a component of contemporary offshore and onshore green energy engineering. Rigorous experimental studies, as well as safety and reliability research, being essential for modern green energy design and engineering. In order to evaluate dynamic performance of galloping energy harvesters, this study utilized extensive wind-tunnel tests, performed under realistic in situ windspeed conditions. State of art Gaidai structural reliability approach has been presented, that is particularly well suitable for non-stationary imperfect or damaged multi-dimensional energy harvesting systems. This approach utilizes analog observations made during representative timelapse, producing quasi-ergodic system dynamic record. As shown in the current study, the recommended technique may be utilized to evaluate the risk of damage or failure in dynamic systems. Additionally, high-dimensionality, deterioration, and nonlinear cross-correlations between dynamic system's key components are challenging to handle for standard reliability approaches, dealing with nonstationary, multidimensional systems. The goal of this study was to benchmark novel Gaidai multivariate reliability approach that allows for effective processing of pertinent statistical data even from limited, multivariate non-stationary underlying dataset. Gaidai multivariate reliability approach attempts to assist designers in evaluating risks of failure and hazards for nonlinear multidimensional dynamic energy harvesting systems, when initial manufacturing imperfections being present.