  The Vortex-induced vibration (VIV) prediction of long flexible cylindrical
structures relies on the accuracy of the hydrodynamic database constructed via
rigid cylinder forced vibration experiments. However, to create a comprehensive
hydrodynamic database with tens of input parameters including vibration
amplitudes and frequencies and Reynolds number, surface roughness and so forth
is technically challenging and virtually impossible due to the large number of
experiments required. The current work presents an alternative approach to
approximate the crossflow (CF) hydrodynamic coefficient database in a carefully
chosen parameterized form. The learning of the parameters is posed as a
constraint optimization, where the objective function is constructed based on
the error between the experimental response and theoretical prediction assuming
energy balance between fluid and structure. Such a method yields the optimal
estimation of the CF parametric hydrodynamic database and produces the VIV
response prediction based on the updated hydrodynamic database. The method then
was tested on several experiments, including freely-mounted rigid cylinder in
large Reynolds number with combined crossflow and inline vibrations and
large-scale flexible cylinder test in the Norwegian Deepwater Program, and the
result is shown to robustly and significantly reduce the error in predicting
cylinder VIVs.
