Path planning for Autonomous Underwater Vehicles (AUVs) in complex 3D environments faces significant challenges regarding computational efficiency and safety. This paper proposes APF-Informed RRT*, a hybrid algorithm designed to overcome the inherent "blindness" of traditional sampling-based planners. The method introduces three key innovations: (1) A hybrid sampling strategy combining goal bias and ellipsoidal constraints to accelerate convergence, particularly in narrow passages; (2) An asymmetric cost mechanism that leverages Artificial Potential Fields (APF) for safe tree extension while strictly applying Euclidean distance during rewiring to ensure geometric optimality; and (3) A retrospective parent selection technique that performs online topological pruning to "straighten" paths dynamically. Simulation results demonstrate that the proposed algorithm significantly outperforms state-of-the-art planners (RRT*, Q-RRT*, F-RRT*) in terms of initial solution time and path cost, providing a robust solution for energy-efficient and safe AUV navigation.

Unbonded flexible risers (UFRs) used in deep-sea service are prone to collapse failure under high external pressure. This study proposes a generative adversarial network (GAN)-based surrogate model for rapid UFR cross-section displacement and stress field prediction under collapse. First, a theoretical model based on helical stiffness projection is introduced, and the numerical model is verified through theoretical comparison. A dataset is generated from the validated numerical model, and symmetry transformation is applied for data augmentation. A preprocessing strategy based on fixed partitioning and overlap-weighted fusion is developed for efficient segmentation and reconstruction of high-resolution mechanical field images. To improve prediction accuracy in mechanically complex regions, a dynamically weighted masked loss function is introduced to enhance feature learning and suppress background interference. Within this framework, the generator adopts a U-Net architecture with skip connections, and the discriminator employs PatchGAN for local feature extraction. Compared with four commonly used prediction models, the proposed framework achieves the best overall performance. The results show that the developed GAN model achieves a mean relative error (MRE) of 2.66% for displacement prediction and 3.40% for stress prediction. Compared with the finite element method (FEM), the proposed model significantly reduces prediction time while maintaining accuracy.

The transport mechanism of non-spherical particles in complex pipelines, such as right-angle elbows, remains insufficiently understood, posing challenges to the efficiency optimization of industrial systems like deep-sea mining. This study investigates the fundamental mechanisms governing the upward transport of 1-15 mm non-spherical particles in a 100 mm right-angle bend by integrating Particle Image Velocimetry (PIV) experiments with coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations. We systematically quantify the effects of key factors-flow velocity, particle size distribution, and shape factor (ranging from 0.4 to 1)-on flow asymmetry, particle dynamics, and transport efficiency. The results reveal a pronounced flow asymmetry, where the outer-side peak velocity is approximately twice that of the inner side, accompanied by a persistent separation vortex. Crucially, transport efficiency is governed by particle interactions: wide-grading blends achieve up to 12% higher conveying speed than narrow fractions at high flow rates. While spherical particles (shape factor, SF = 1) attain the highest axial velocity, particles with SF >= 0.8 are identified as optimal, maintaining moderate rotation, concentrating in the central high-speed zone, and thereby combining high transport velocity with minimal wall contact. These findings elucidate the underlying particle-fluid interactions in bends and provide a quantitative basis for optimizing particle morphology in industrial hydraulic transport systems.

Geological storage of carbon dioxide (CO2) in deep-sea formations represents a pivotal strategy for mitigating atmospheric CO2 levels, where storage security and efficacy are fundamentally governed by the pore-scale seepage behavior of CO2. However, the microscopic displacement mechanisms of CO2–water two-phase flow under the characteristic high-pressure, low-temperature conditions of the deep sea remain inadequately understood. This study employed a self-developed high-pressure microfluidic experimental platform (0–30 MPa, 4–50 °C) to systematically investigate the CO2 displacement process in porous media. The effects of injection rate (0.001–5 mL/min) and system pressure (1, 5, and 10 MPa) on displacement patterns, front stability, and final saturation were quantified. The results demonstrate that injection rate is the primary controller of displacement stability: high rates (≥0.1 mL/min) induce viscous fingering and lower final saturation, whereas low rates (≤0.05 mL/min) promote stable, piston-like displacement. Crucially, elevated pressure exerts a profound stabilizing effect, effectively suppressing fingering instabilities and enhancing final gas saturation (up to 0.544 at 10 MPa). This work elucidates the synergistic regulatory mechanism between injection rate and confining pressure, providing essential pore-scale experimental evidence for optimizing injection parameters to achieve efficient and secure CO2 storage in deep-sea reservoirs.

Accurately identifying gas-liquid two-phase flow patterns within the riser is crucial in deep-sea pipeline gas-liquid transportation operations. This research utilizes Distributed Acoustic Sensing (DAS) technology to capture vibration signals during the pneumatic lifting experimental process within the riser. Through integrating feature extraction techniques across the time, frequency, and time-frequency domains, a Support Vector Machine (SVM)-based model for identifying gas-liquid two-phase flow patterns in the riser is established. The findings demonstrate that the model achieves a high accuracy rate of 97% in flow pattern identification, with specific accuracies of 95% for slug flow and 96.67% for churn-annular flow recognition. This investigation offers valuable insights into gas-liquid two-phase flow pattern identification in risers and addresses the underexplored area of fiber optic sensing technology application in flow pattern recognition.

To address tracking accuracy degradation and frequent control updates of unmanned underwater vehicles (UUVs) under disturbances and uncertainties, this paper proposes an enhanced event-triggered strategy combining a grey wolf optimizer-based nonlinear disturbance observer (GWO-NDO) with an adaptive sliding mode controller (AGSMC). A four-degree-of-freedom dynamic model is established based on the BlueROV2 platform. The GWO-NDO performs offline global optimization of observer gains, overcoming the limitations of heuristic tuning in existing integrated schemes. The adaptive-gain sliding mode controller automatically adjusts gains according to tracking error to balance convergence and chattering. An adaptive event-triggered mechanism dynamically adjusts the triggering threshold to reduce control updates. Simulations on a 3D helical trajectory demonstrate that the proposed method outperforms conventional approaches in integrated error metrics, reducing control updates by 87.83% while maintaining tracking accuracy, thus achieving a superior balance compared to existing integrated schemes.

Anaerobic fermentation is a common agricultural waste treatment method. However, maintaining a constant digestion temperature is challenging because a reliable heat supply is often unavailable across regions and seasons.To address this issue, we propose a thermal energy supply concept based on a thermal flow-reversal reactor (TFRR) with a compact thermal storage medium. A small fraction of the methane produced by the digester is diverted and oxidized at low concentration and low temperature; the reactor alternates between storing and releasing heat, enabling continuous heat output to keep the digester at a constant temperature.For a TFRR built from multi-channel alumina ceramic plates, we develop a two-dimensional numerical model that couples heat and mass transfer and accounts for species conservation, energy conservation, and reaction heat release.A constant-temperature digester fed with a mixed substrate of banana pseudostem and pig manure is used as a case study. Simulations focus on the inlet methane concentration and the flow-switching interval, and we evaluate performance under different ambient air temperatures.The results show that, with an appropriate combination of inlet methane concentration and switching interval, the TFRR can sustain stable low-temperature oxidation and continuous heat release when the inlet methane volume fraction is 0.5%-1.0%. This allows the digester to maintain a constant operating temperature without external energy input.Further analysis indicates that this stable operating range can be shifted by tuning the operating parameters as ambient temperature changes, demonstrating strong climate adaptability and controllable operation.This provides new ideas and technical support for the construction of biomass biogas supply centers.

The extraction of polymetallic nodules from the deep sea offers a promising source of critical metals, but the hydrodynamic process of in-situ ore pre-concentration remains a significant challenge. This study proposes an integrated pre-separation system that combines a hydrocyclone with a buffer station to achieve efficient mineral enrichment underwater through multiphase flow separation. Using an orthogonal experimental design and computational fluid dynamics (CFD), we systematically investigate the influence of key geometric parameters—cone angle, vortex finder length, and cylindrical section length—on the internal flow field and separation performance. Results demonstrate that a cone angle of 20° effectively minimizes fine particle entrainment by stabilizing the vortex core and suppressing short-circuit flow. An optimized configuration (80 mm overflow depth, 90 mm cylinder length) achieves a separation efficiency of 85.1

High-precision underwater object detection is a crucial component for improving the efficiency of marine resource development. Traditional detection methods face challenges due to underwater environmental factors such as low brightness and high blurriness. To address these issues, this paper proposes an underwater object detection algorithm named SMV-YOLOv11. Firstly, the Swin Transformer is adopted to replace the backbone network, and a novel Multi-scale Spatial and Channel Attention module (MSCA) is designed and integrated into the backbone to enhance its feature extraction capability. Subsequently, the VoVGSCSP module is introduced to replace the C3K2 module, effectively leveraging contextual information to improve global detection performance. Furthermore, the WIoUv3 loss function replaces the CIoU loss function to enhance the model’s robustness against spatial transformations and scale variations. Comparative experiments and ablation studies conducted on publicly available datasets validate the superiority and generalization ability of the improved model. SMV-YOLOv11 achieves an 87.6 % mean Average Precision (mAP), representing a 1.4 % improvement over the original YOLOv11. It also demonstrates significant gains of 2.7 %, 2.8 %, and 1.9 % in Precision, Recall, and F1 Score, respectively. The experimental results demonstrate that SMV-YOLOv11 possesses significant advantages in underwater object detection, effectively mitigating interference from background noise, low-light conditions, and blur. This provides a new high-precision and high-speed solution for the field.

Marine risers are susceptible to vortex-induced vibrations (VIV) in complex ocean current environments, posing significant risks to structural safety and fatigue life. This study, conducted on the Ansys Workbench platform, establishes a three-dimensional numerical model using bidirectional fluid–structure interaction (FSI) methods. Wet modal analysis is employed to extract the riser’s natural frequencies, followed by a systematic comparison of vibration responses under uniform flow and linear shear flow conditions. The findings indicate that as the vortex shedding frequency approaches the structural natural frequency, the system exhibits pronounced frequency lock-in. Spectral analysis confirms that VIV dominates the dynamic response. Notably, under initial conditions (uniform flow velocity = 0.5 m/s; shear flow velocity = 0.05 m/s, Gradient = 0.025), shear flow induces larger vibration amplitudes. However, as flow velocity increases, uniform flow surpasses shear flow in both amplitude (maximum 0.03 D) and frequency (maximum 0.02 D). Modal analysis demonstrates that uniform flow excites the fourth-order mode, whereas shear flow confines the system in the second-order mode. Additional controlled simulations highlight the critical influence of the shear flow’s initial velocity on vibration modes, providing a theoretical basis for VIV suppression.

With the over-exploitation of mineral resources on land, mineral resources are scarce, so people have set their sights on the deep sea, which is rich in mineral resources. This paper establishes a two-dimensional model for the transportation process of granular ore in the pipeline during the lifting process of deep-sea mining pipelines. Using the method of electrical resistance tomography, the empirical formula of the solid phase content of granular ore in solid-liquid two-phase flow is obtained by analyzing the characteristic voltage. Different models are constructed under this working condition, and their results are compared with those obtained by the empirical formula, which proves the feasibility of this method and provides a technical reference for pipeline lifting in deep-sea mining.

Biomass cellulose evaporators have gained significant attention due to their environmental friendliness and sustainability. However, during the drying process, excessive aggregation of intermolecular hydrogen bonds in cellulose forms dense crystalline domains, severely hindering water transport. In this study, we regulated the selfassembly behavior of cellulose molecular chains through the specific ion effect. The "salt-in" anions occupied the active hydroxyl sites, disrupting the dense hydrogen bond network and exposing more free hydroxyls, thereby reducing the evaporation enthalpy. The optimized aerogel exhibited enlarged porous structures, increasing the evaporation rate by 32.3 % to 2.17 kg m- 2h- 1, while also demonstrating excellent salt resistance. This molecular-level regulation strategy provides a new idea for the design of high-performance evaporation membranes and advances the development of sustainable water purification technologies.

Thermal energy storage is an efficient way for thermal control of near-earth and deep space detectors, but the melting rate is restricted by low heat transfer performance of phase change material (PCM) and disappearance or suppression of natural convection under micro/low gravity. To accelerate melting of PCM under micro/low gravity, graphene nanoplatelet (GNP)-enhanced PCM with fins are proposed. The effects of fin shape, GNP concentration and gravity level on dynamic melting characteristics considering thermocapillary convection are investigated. The results show that the improvement effect of rectangular fins on melting rate is higher than that of triangular fins under microgravity; with the decrease of gravity level, the melting rate is reduced. The presence of GNP significantly promotes the melting under micro/low gravity. At GNP concentration of 0.03 vol

High-precision underwater object detection is a crucial component for improving the efficiency of marine resource development. Traditional detection methods face challenges due to underwater environmental factors such as low brightness and high blurriness. To address these issues, this paper proposes an underwater object detection algorithm named SMV-YOLOv11. Firstly, the Swin Transformer is adopted to replace the backbone network, and a novel Multi-scale Spatial and Channel Attention module (MSCA) is designed and integrated into the backbone to enhance its feature extraction capability. Subsequently, the VoVGSCSP module is introduced to replace the C3K2 module, effectively leveraging contextual information to improve global detection performance. Furthermore, the WIoUv3 loss function replaces the CIoU loss function to enhance the model's robustness against spatial transformations and scale variations. Comparative experiments and ablation studies conducted on publicly available datasets validate the superiority and generalization ability of the improved model. SMV-YOLOv11 achieves an 87.6 % mean Average Precision (mAP), representing a 1.4 % improvement over the original YOLOv11. It also demonstrates significant gains of 2.7 %, 2.8 %, and 1.9 % in Precision, Recall, and F1 Score, respectively. The experimental results demonstrate that SMV-YOLOv11 possesses significant advantages in underwater object detection, effectively mitigating interference from background noise, low-light conditions, and blur. This offers a new solution for the field.

Unbonded flexible risers (UFRs) used in the intermediate transportation in seabed mining are prone to corrosion in seawater during long-term operation. In this study, 8-layer UFRs were equivalent to 5-layer UFRs, and the constitutive equations for the 5-layer UFRs were derived using the principle of stiffness equivalence. The length and width of the outer tensile armour layer and the effect of composite corrosion on the stiffness of the UFRs at different corrosion depths were investigated separately. Additionally, the displacement of the helical steel strip when buckling occurred and when it reached the yield limit under axial pressure owing to composite corrosion are discussed herein. The results indicated that when the corrosion depth was 1 mm, the stiffness reductions caused by the corrosion length were 3.92 %, 6.26 %, and 4.2 % for tensile, clockwise torsion, and counterclockwise torsion, respectively. The reductions caused by the corrosion width were 4.04 %, 5.41 %, and 3.29 %, with composite corrosion stiffness reductions of 48.21 %, 5.99 %, and 33.73 %, respectively. The buckling analysis of the UFRs under axial compression demonstrated that buckling failure occurred earlier in the corroded regions. When the corrosion depth reached 1 mm, the spiral steel strip exhibited signs of folding.

The concept of deep-sea mining has been proposed for over half a century, yet the answer to whether it can solve the global mineral resource crisis has not been determined. The main issue remains environmental concerns. One ongoing research topic that runs through this issue is environmental baseline surveys and monitoring. This article uses a literature review method to compare and summarize previous related research. Firstly, it explains the importance of baseline survey and monitoring in the deep-sea environment. Then, it selects key content such as water quality and sediment analysis for detailed analysis, involving the technologies, methods, and frameworks used in the research. The article also focuses on analyzing several world-renowned deep-sea mining environmental pollution sea trials, summarizing their limitations. The article concludes by analyzing the indispensable importance of baseline survey and monitoring research topics in Environmental Impact Assessment (EIA) work for deep-sea mining. The significance of this research is to promote stakeholders' investment in baseline survey and monitoring work for deep-sea mining, rather than focusing solely on technological research and development. By drawing on past experiences and lessons, one can find a suitable framework for deep-sea environmental research. This effort is crucial for sustainable development in deep-sea mineral extraction.

In this study, sludge retting was used as a pretreatment method for extracting banana pseudostem fibers. A Box–Behnken response surface design was employed to optimize the retting conditions. Three variables—Bacillus subtilis concentration, treatment time, and pH—were selected for analysis. Their effects on the mechanical properties of the fibers were systematically evaluated. Experimental data were analyzed using ANOVA in Design-Expert 13, and a regression model was established for parameter optimization. The optimal conditions were determined to be a Bacillus subtilis concentration of 1.18%, a treatment time of 20 days, and a pH of 7. Under these conditions, the tensile strength, elastic modulus, and elongation at break of the fibers reached 1161.63 MPa, 50.68 GPa, and 2.32%, respectively—representing improvements of 46.23%, 42.48%, and 34.1% compared to untreated samples. In addition, the fibers were analyzed using SEM, TGA-DTG, FTIR, and XRD to investigate changes in surface topography, thermal behavior, chemical bonding, and crystalline structure. Results showed that sludge retting effectively removed non-cellulosic components, enhanced thermal stability and crystallinity, and significantly improved the mechanical performance of the fibers. This study demonstrates that sludge retting is a green and sustainable pretreatment technique with strong potential for banana pseudostem fiber processing.

This study examines the stability and vibration characteristics of a pipe on an elastic foundation equipped with two lateral and rotational springs. The equations governing the dynamic motion of the pipe are derived using Hamilton's principle and are then solved using differential quadrature to ascertain the vibration characteristics of the pipe. The study further investigates the effects of flow velocity, elastic stiffness, and the two-parameter foundation on the pipe's vibration frequency and critical velocity. A comparison with literature results substantiates the validity of the findings presented herein. The results indicate that the elastic stiffness at both ends significantly influences the pipe's vibration frequency and critical velocity, revealing a notable distinction between symmetric and asymmetric elastic stiffness. Moreover, the two-parameter foundation is shown to enhance both the vibration frequency and critical velocity of the pipe, thereby contributing to improved stability.