Physics-based Data-informed Prediction of Vertical, Catenary, and Stepped Riser Vortex-induced Vibrations

Abstract

Semi-empirical models serve as current state-of-the-art prediction technologies for vortex induced vibrations (VIV). Accurate prediction of the flexible body’s structural response relies heavily on the accuracy of the acquired hydrodynamic coe ffi cient database. The construction of systematic databases from rigid cylinder forced vibration experiments requires not only an extensive amount of time and resources but is also a virtually impossible task given the wide multi-dimensional space the databases span. In this work we improve the flexible cylinder VIV prediction by machine-learning the hydrodynamic databases using measurements along the structure; such a methodology has been proven e ff ective for vertical flexible risers in uniform and sheared flows using vibration amplitude and frequency data. This work demonstrates the e ff ectiveness of the framework on flexible vertical risers in a stepped current and flexible catenary risers (with the catenary plane parallel or at an oblique angle with respect to the incoming flow). Moreover the framework is applied to stepped (two-diameter) risers undergoing dual-frequency vibrations. Last but not least, the framework is extended to using only sparse strain sensing. The predicted VIV responses using the learned hydrodynamic coe ffi cient databases are compared with experimental observations.