Flexible electronic films need to be applied in different ambient temperatures. The porous substrate of the composite film enhances air permeability. The lifespan of these composite films is significantly affected by variations in temperature and substrate porosity. To explore the impact of temperature and porosity on the performance of composite films, we developed a 3D deformation detection system utilizing the advanced three-dimensional digital image correlation (3D-DIC) method. This system enabled us to observe and analyze the 3D deformation behaviors of porous polydimethylsiloxane (PDMS) flexible composite films when they are subjected to uniaxial stretching at different temperatures. We proposed employing two parameters, namely the strain fluctuation coefficient (M) and off-plane displacement (w), to characterize the 3D deformation of the films. This holistic characterization of deformation through the combined utilization of parameters M and w held greater significance for composite films compared to the conventional practice of solely measuring mechanical properties like the elastic modulus. Through experimental analysis, we discovered that as the temperature increased, the M value of the film decreased while the w value increased for the same stretching distance. Furthermore, the porosity of the composite film depended on the doping mass ratio of PDMS to deionized water during the fabrication process. Specifically, when the ratio was set at 6:1, the composite film exhibited the smallest M value and w value, and the highest air permeability. Additionally, the 3D deformation behavior remained stable across different temperatures for this specific ratio. Moreover, our findings unveiled a remarkable association between the parameter w and the resistance value of the device. These findings provide valuable insights for optimizing the fabrication process of porous PDMS flexible electronic composite films.

Digital twin (DT) research spans autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), unmanned surface vessels (USVs), underwater gliders, and emerging marine robotic platforms; however, reported systems differ widely in terms of their physical–virtual coupling and validation maturity. A structured Web of Science search retrieved 116 records, of which 107 were retained for analysis. This review synthesizes architectures, enabling technologies, applications, coupling modes, validation settings, and deployment barriers across the perception–decision–control chain. The reported workflows address model construction, multiphysics simulation, virtual testing, monitoring, control validation, and decision support. Within the purposively selected 11-case full-text audit, two cases were classified as DT-enabling methods or components, five as digital models or virtual prototypes, three as digital shadows, and one as a bidirectional DT prototype; none documented sustained operational deployment. Four constraints recurred across platforms: the trade-off between model fidelity and computation; uncertain marine environments; communication and synchronization limitations; and fragmented interfaces and standards. Physics-informed learning, multi-fidelity modeling, edge computing, and interoperable reference architectures provide candidate development paths, but the evidence suggests that the central gap lies less in producing sophisticated virtual models than in establishing sustained, interoperable, and bidirectional physical–virtual operation.

Underwater vehicles (UVs) are pivotal for ocean exploration, yet their effectiveness is fundamentally constrained by acoustic performance in noisy and dynamic seas. Self-noise, non-stationary interference, and extreme conditions not only degrade sensing, navigation, and stealth but also cascade into losses in propulsion efficiency, actuation reliability, and control precision. This review provides a system-performance-oriented synthesis of advances across four key areas: bioinspired and intelligent noise reduction materials/structures, active noise control and adaptive signal processing, noise-robust navigation and collaborative localization, and deep learning-enhanced acoustic perception. Key findings indicate that bioinspired surfaces reduce flow noise by ≈5 dB, adaptive filtering improves $\mathrm{SNR}$ by up to 20 dB, and distributed robust filtering ensures multi-AUV consistency under uncertainty. These developments collectively establish acoustic performance not as a parallel metric, but as a fundamental enabler and critical bottleneck for the integrated propulsion-actuation-control stack of next-generation UVs. Consequently, this review outlines viable pathways toward high-performance acoustic–mechanical integration.