Topology optimization analysis of VLCC transverse web based on UNet deep learning

This paper proposes a hull transverse web topology optimization method based on UNet for application in the optimization design of complex ship structures.

Taking the transverse web of a very large crude carrier (VLCC) as the research object, a UNet topology optimization surrogate model is first created according to optimization mathematical principles. The finite element grid physical quantity is then mapped to the tensor to obtain the dataset for model training. Finally, the intersection over union (IoU) method is used to evaluate the training results, and the method is compared with the solid isotropic material with penalization (SIMP) method in terms of topology configuration.

The results show that this method can quickly output the material layout of the design domain, and compared with SIMP topology optimization, it can obtain the topology configuration more efficiently.

The proposed topology optimization method can provide a new design method for ship transverse web structures.