calibration of empirical coefficients in wake-oscillator model for vortex-induced vibrations using genetic algorithms

Vortex-induced vibrations (viv) occur when fluid flows around an object and generates oscillatory motion. in particular, fluid flow around a cylinder forms a symmetric vortex street pattern leading to viv. this study employs a mathematical wake-oscillator model to simulate the viv phenomenon. to refine the model, the genetic algorithm is utilized to determine the empirical coefficients of the mathematical model. an optimization approach is utilized for improved calibration of the model's prediction with experimental data, which integrates the root mean square (rms) objective function with the weighted contribution of the points. the results reveal that the empirical coefficients defined through the genetic algorithm maintain the quality of the super-upper branch prediction and present a better prediction for experimental observations. however, further steps are required to properly select empirical coefficients in wake oscillator models. the utilization of hybrid functions by combining multiple metrics can be one of the suitable choices.