Abstract

Evaluation of the response amplitude operator (RAO) function for ship wave frequency motions by means of scale model tests in regular waves is a standard procedure conducted by hydrodynamic model testing institutions. The resulting RAO function allows for evaluating sufficiently reliable seakeeping predictions for low to moderate sea states. However, for standard hull forms, correct prediction of roll motion in irregular wave (and also in regular waves different than these used in the experiment) on the basis of RAO function presents a substantial challenge due to considerable contribution of viscous damping to roll response. In other words, the RAO values depend strongly on the amplitude of the waves used in the experiment, so the final prediction requires careful application of relevant correction of RAO, dependent on the actual significant wave height, for which the prediction is computed. Thus, in order to collect complete data for ship roll prediction, the roll decay test is usually also required. Additional drawback of evaluating the seakeeping prediction on the basis of RAO is the fact that the experiment in regular waves is quite time-consuming, which refers to the experiment itself as well as to the processing. The following paper presents a proposal of the alternative method for experimental evaluation of response amplitude operator of roll motion in beam waves, consisting in exposing the ship model to irregular wave characterized by white noise spectrum, i.e. the spectrum of uniform energy density. In theory, RAO function is equivalent to the square root of the spectrum of the response to white noise wave. The results of experiments in white noise waves were verified on the basis of the results of comprehensive experiments conducted in usual way. Additionally, the effect of non-linearity of viscous damping was widely studied by comparing the calibrated RAO-based predictions with actual response to irregular waves of different heights. As a result, a method for including the non-linear effects in prediction based on white noise was proposed. It was proved that the proposed method is capable of providing equally valuable information in significantly shorter time.

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