Hull form optimization based on multi-fidelity deep neural network
[Objective]To improve hull optimization design efficiency and obtain better optimization res-ults,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 fu-sion 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 sur-rogate 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 mod-el 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 ac-count both efficiency and accuracy,which can be used for optimization.The optimized hull form obtained by it has a more significant resistance optimization effect.
naval architectureartificial intelligencedrag reductionhull form optimizationmulti-fidelity deep neural networkdata fusiontransfer learning
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