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Pattern transformation in a periodic porous structure has inspired multifarious mechanical metamaterials/metastructures due to the induced unusual negative Poisson's ratio behavior of macroscopic materials. Recently, it has been leveraged to architect a variety of designable and multifunctional structural members. Inspired by this design methodology, a novel porous cylindrical shell, which is perforated by a large number of staggered openings, is constructed and investigated meticulously. A stable, anti‐disturbed, and controllable waisted deformation of the architected cylindrical shell will be triggered under an axial compression. A stoma‐shaped biomimetic hole and graded distribution of initial openings are proposed to ensure that the holes distributed throughout the shell can be closed up concurrently while the closed states of holes can be flexibly programmed. To explore the applications of such shells, a handy cylindrical vessel is elaborately designed and its multiple functions including reagent release, underwater sampling, and flow control are exhibited by experiments. The results reflect that the designed vessel can be facilitated with many advantages such as uniform release, quick action, easy actuation, and repeated usage. Moreover, it also may open a new avenue for metamaterials in the fields of biomedical engineering, underwater detection, fluid machinery, etc.

Floating Production Storage and Offloading (FPSO) is essential offshore equipment for developing offshore oil and gas. Due to the complex sea conditions, FPSOs will be subjected to long-term alternate loads under some circumstances. Thus, it is inevitable that small cracks occur in the upper part of the module pier. Those cracks may influence the structure’s safety evaluation. Therefore, this paper proposes a method for the FPSO module to support crack identification based on the PSPNet model. The main idea is to introduce an attention mechanism into the model with Mobilenetv2 as the backbone of the PSPNet, which can fuse multiple feature maps and increase context information. The detail feature loss caused by multiple convolutions and compressions in the original model was solved by applying the proposed method. Moreover, the attention mechanism is introduced to enhance the extraction of adequate information and suppress invalid information. The mPA value and MIoU value of the improved model increased by 2.4% and 1.8%, respectively, through verification on FPSO datasets.

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