Fast prediction of mechanical impedance of an underwater foundation based on surrogate models

  Objectives  The mechanical impedance of foundations can directly affect the stealth performance of underwater structures, which is commonly obtained by finite element simulation. However, this method is usually very time-consuming and inefficient when applied directly to optimal design. In order to enhance the efficiency, surrogate model technology is used to take the place of computer simulation.

  Methods  The mechanical impedance of a typical underwater foundation is studied. The comparison among support vector regression(SVR)method, back propagation neural network(BPNN) method, radial basis function(RBF)method and Kriging model is conducted for their fitting accuracy. Besides, a data preprocessing method is put forward by combining the practical engineering requirement and characteristics of mechanical impedance. The effect of the data preprocessing method on the fitting accuracy of the surrogate model is studied.

  Results  According to the calculated results, different surrogate models have different accuracy in fitting the mechanical impedance of the foundation. And the kriging model achieves the best results among four surrogate models. Compared with the initial model, the data preprocessing method has increased the normalized root mean square error and the normalized maximum absolute error by 9.80% and 15.43% respectively to the modified model.

  Conclusions  The kriging model has the best applicability on fitting the parameter input-impedance output mapping. And the proposed data preprocessing method can be used to improve the accuracy of the surrogate model.