Objective To improve hull optimization design efficiency and obtain better optimization results, different fidelity data is organically integrated and a multi-fidelity deep neural network is applied.
Methods A multi-fidelity deep neural network is constructed based on the idea of multi-source data fusion and transfer learning. By fusing a large amount of low-fidelity data with a small amount of high-fidelity data, the linear and nonlinear terms between the high-fidelity data are constructed to obtain a high-fidelity surrogate model. Based on this method, the optimization design of the resistance of a DTMB 5415 ship is carried out. The potential flow and viscous flow are used to evaluate the resistance of the sample points respectively. The potential flow calculation results are used as low-fidelity data, while the viscous flow calculation results are used as high-fidelity data. A multi-fidelity deep neural network surrogate model is then constructed. The optimal solution is obtained by genetic algorithm and compared with the optimal solution of the Kriging model constructed by high-fidelity data.
Results Based on the multi-fidelity deep neural network method, the resistance of DTMB 5415 is reduced by 6.73%. Based on the Kriging model, the resistance of DTMB 5415 is reduced by 5.59%.
Conclusions The multi-fidelity deep neural network surrogate model can take into account both efficiency and accuracy, which can be used for optimization. The optimized hull form obtained by it has a more significant resistance optimization effect.
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