Dynamic sampling method for ship resistance performance optimisation based on approximated model

Abstract

ABSTRACT Sampling method is an effective tool for building approximated models, which can improve the efficiency of hull form optimisation. A dynamic sampling method (DSM) is introduced herein to improve the accuracy and efficiency of the approximated model by considering the influences of sample quality measures in both input and output parameter spaces. The DSM uses leave-one-out to obtain an estimate of the cross-validation errors, which are maximised to determine the sample points in the design space. The variation is used for the calculation of continuous and multimodal regions. The proposed method is compared, using both numerical examples and hull form optimisation, with traditional sampling methods from the literature. The stem profile’s optimisation for KCS based on DSM and static sampling method is performed. And the optimised hull form with less ship resistance is obtained. The comparison results show that DSM performs better than the static sampling method. Abbreviations: AM: approximated models; ACE: accumulative error; AMDSM: approximated models based on dynamic sampling method; AMUD: approximated models based on uniform design; CAMM: continuous and multimodal; CF: covariance function; CFD: computational fluid dynamics; CV: cross validation; CVV: cross validation variance; DSM: dynamic sampling method; eLOO: leave-one-out error; LOO: leave-one-out; LHS: Latin Hypercube Sampling; MAPE: mean absolute predictive error; MCS: Monte Carlo Sampling; NN: neural network; RAAE: relative average absolute error; RMSE: root mean square error; SM: sampling methods; SP: sample points; SSM: static sampling method; TS: test samples; UDL: uniform design; VF: variation function.