Mesoscale eddies play an important role in regulating the global ocean ecosystem and climate variability. However, few studies have been found to focus on the survey of the underwater gliders (UGs) motion performance inside mesoscale eddies. The dynamic model of an UG considering the eddy density is established to predict its motion performance inside an eddy. Ignoring the effect of vertical velocity inside the eddy on the motion of UG, the simulation results and experimental data are compared to verify the derived model. From the analysis of the motion performance, the vertical velocity is larger at 400∼940 m depth than that at a depth of 0∼400 m in the ascent. Considering the vertical structures of parameters within eddies, the climbing profiles are chosen as the available samples to capture an eddy better. The larger error caused by the eddy density mainly occurs near the depth of the thermocline. Moreover, there is a stronger influence of eddy density on the motion performance of the UG in the ascent than that in the descent. The results show the differences in the effect of the mesoscale eddy density on the motion performance of “Petrel II” UG in the descent and ascent, and they provide a sampling suggestion for the application of UGs in the mesoscale eddy observation.

Combination prediction models have gained great development in the area of information science, and are widely applied in engineering fields. The underwater glider (UG) is a new type of unmanned vehicle used in ocean observation for the advantages of long endurance, low noise, etc. However, due to its lower speed relative to the ocean current, the surfacing positioning point (SPP) of an UG often drifts greatly away from the preset waypoint. Therefore, this paper proposes a new combination model for predicting the SPP at different time scales. First, the kinematic model and working flow of the Petrel-L glider is analyzed. Then, this paper introduces the principles of a newly proposed combination model which integrates single prediction models with optimal weight. Afterwards, to make an accurate prediction, ocean current data are interpolated and averaged according to the diving depth of UGs as an external influencing factor. Meanwhile, with sea trial data collected in the northern South China Sea by Petrel-L, which had a total range of 4230.5 km, SPPs are predicted using single prediction models at different time scales, and the combination weights are derived with a novel simulated annealing optimized Frank–Wolfe method. Finally, the evaluated results demonstrate that the MAE and MSE are 966 m and 969 m, which proves that the single models achieved good performance under specified situations, and the combination model performed better at full scale because it integrates the advantages of the single models. Furthermore, the predicted SPPs will be helpful in the dead reckoning of the UG, and the proposed new combination method could extend into other fields for prediction.

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.

The physical and chemical stability of perovskite films has always been a key issue for their industrialization, which has been extensively studied in terms of materials, environment, and encapsulation. Spin coating is one of the most commonly used methods for the preparation of perovskite films in research. However, little attention has been paid to the deformation state of the substrate when it is fixed by means of adsorption and its impact. In this work, the three-dimensional digital image correlation (3D-DIC) method and hyperspectral technology are used to acquire and analyze the adsorption deformation characteristics of the substrate during spin coating, as well as the resulting inhomogeneity. Plastic and four different thicknesses of float glass (0.2, 0.5, 0.7, 1.1 mm) were selected as substrates, and they were tested separately on two suction cups with different structures. The results show that the plastic and 0.2 mm specimens exhibit obvious strain localization behavior. The distribution and magnitude of the strain are affected by the size of the sucker structure, especially the width of the groove. For glass specimens, this effect shows a nonlinear decrease with increasing substrate thickness. Compared to the strain value, the irregularity of local deformation has a greater impact on the non-uniform distribution of materials. Finally, inhomogeneities in the perovskite films were observed through optical lens and hyperspectral data. Obviously, the deformation of the substrate caused by adsorption should attract the attention of researchers, especially for flexible or rigid substrates with low thickness. This may affect the centrifugal diffusion path of the precursor, causing microstructure inhomogeneity and residual stress, etc.

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.