Abstract
A new equation structure is proposed as an alternative to the Morison equation for the prediction of wave forces. Initially, non-linear parametric continuous time differential equation models were estimated from wave force data for a variety of flow situations by adopting a new approach which avoids direct differentiation of the input-output data. The method consists of two stages. The first stage involves the estimation of a discrete time model (polynomial NARMAX) from sampled input-output data and computation of the linear and higher-order frequency response functions. The second stage involves identifying continuous time models by curve fitting to the complex frequency response data using a weighted complex orthogonal estimator. The orthogonal property of the estimator helps in identifying the correct model structure or which terms to include in the model, and the weighting property provides an additional degree of freedom to control the properties of the estimator with respect to the selection of the frequency range and number of data points. Morison equation models were fitted initially to the data but were shown to curve fit to the data without capturing the underlying dynamics. The frequency domain characteristics of the Morison equation models were also analysed and shown to be structurally deficient in representing certain dynamic features of the force. However, it is shown that the new equation structure is capable of emulating all the relevant features of the wave force mechanics. Extensive simulations on a variety of experimental data show that models based on the new structure perform remarkably well compared with the Morison equation. For each flow situation, in addition to the drag and inertia coefficients of the Morison equation, there are two non-dimensional coefficients defining history effects which show some consistency between widely different flow situations. © 1998 Academic Press Limited.
Original language | English |
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Pages (from-to) | 449-485 |
Number of pages | 36 |
Journal | Mechanical Systems and Signal Processing |
Volume | 12 |
Issue number | 3 |
Publication status | Published - May 1998 |