Search Results (84)

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Welding and cutting behaviour may affect the mechanical properties of titanium alloy welded structures, which may have some impact on the safety assessment of the structure. This study analyses changes in residual stress in Ti80 butt-welded thick plates before and after wire-cut electric discharge machining, using numerical simulations based on thermo-elastoplastic theory and the element birth and death method, validated by X-ray non-destructive testing. The transverse residual tensile stress near the weld exhibits an asymmetric bimodal distribution, while the longitudinal stress is significantly higher than the transverse stress. Wire-cut electric discharge machining had minimal influence on the transverse residual stress distribution but led to partial relief of the longitudinal residual tensile stress. The maximum reductions in transverse and longitudinal welding residual tensile stresses are approximately 60% and 36%, respectively. The findings indicate that wire-cut electric discharge machining can alter surface residual stresses in Ti alloy butt-welded thick plates. This study also establishes a numerical simulation methodology for analysing welding residual stresses and their evolution due to wire-cut electric discharge machining. The results provide a theoretical basis for analysing the structural strength and safety of Ti-alloy-based deep-sea submersibles. Full article

Electric submersible progressive-cavity pumps (ESPCPs) deliver high lifting efficiency but are prone to failure in the high-temperature, high-pressure, and multiphase down-hole environment, leading to production losses and elevated maintenance costs. To achieve reliable condition recognition under these noisy and highly imbalanced data constraints, we fuse deep residual feature learning, ensemble decision-making, and generative augmentation into a unified diagnosis pipeline. A class-aware TimeGAN first synthesizes realistic minority-fault sequences, enlarging the training pool derived from 360 field records. The augmented data are then fed to a CNN backbone equipped with ResNet blocks, and its deep features are classified by a Random-Forest head (CNN-ResNet-RF). Across five benchmark architectures—including plain CNN, CNN-ResNet, GRU-based, and hybrid baselines—the proposed model attains the highest overall validation accuracy (≈97%) and the best Macro-F1, while the confusion-matrix diagonal confirms marked reductions in the previously dominant misclassification between tubing-leakage and low-parameter states. These results demonstrate that residual encoding, ensemble voting, and realistic data augmentation are complementary in coping with sparse, noisy, and class-imbalanced ESPCP signals. The approach therefore offers a practical and robust solution for the real-time down-hole monitoring and preventive maintenance of ESPCP systems. Full article

The application fields of high-speed submersible permanent magnet synchronous motors (PMSM) are constantly expanding. Especially in high-risk and complex environments such as oil exploration, offshore oil exploitation, and deep well operation, the reliability, stability, and efficiency of motor drive systems are more and more prominent. The submersible motor is greatly affected by load disturbance, pressure change, and external oil flow, and the traditional method may not perform well in complex disturbance problems. Therefore, a three-order adaptive nonlinear extended state observer is proposed to collect the input and output information of the system in real time, and estimate the motor speed, position, and total disturbance. A linear feedback control law is designed to eliminate the disturbance. The superiority of the proposed algorithm under complex operating conditions is verified by the Simulink model and experiments, which provide a theoretical basis for the control of submersible motors. Full article

Deep-sea biological detection is a pivotal technology for the exploration and conservation of marine resources. Nonetheless, the inherent complexities of the deep-sea environment, the scarcity of available deep-sea organism samples, and the significant refraction and scattering effects of underwater light collectively impose formidable challenges on the current detection algorithms. To address these issues, we propose an advanced deep-sea biometric identification framework based on an enhanced YOLOv8n architecture, termed PSVG-YOLOv8n. Specifically, our model integrates a highly efficient Partial Spatial Attention module immediately preceding the SPPF layer in the backbone, thereby facilitating the refined, localized feature extraction of deep-sea organisms. In the neck network, a Slim-Neck module (GSconv + VoVGSCSP) is incorporated to reduce the parameter count and model size while simultaneously augmenting the detection performance. Moreover, the introduction of a squeeze–excitation residual module (C2f_SENetV2), which leverages a multi-branch fully connected layer, further bolsters the network’s global representational capacity. Finally, an improved detection head synergistically fuses all the modules, yielding substantial enhancements in the overall accuracy. Experiments conducted on a dataset of deep-sea images acquired by the Jiaolong manned submersible indicate that the proposed PSVG-YOLOv8n model achieved a precision of 79.9%, an mAP50 of 67.2%, and an mAP50-95 of 50.9%. These performance metrics represent improvements of 1.2%, 2.3%, and 1.1%, respectively, over the baseline YOLOv8n model. The observed enhancements underscore the effectiveness of the proposed modifications in addressing the challenges associated with deep-sea organism detection, thereby providing a robust framework for accurate deep-sea biological identification. Full article

Electric submersible pumps (ESPs) are crucial equipment in offshore oilfield production. Due to their complex structure and the variable geological environments in which they work, ESPs are prone to a wide range of complex faults. Existing fault diagnosis models for ESP wells face several issues, including high subjective dependence, large sample data requirements, and poor adaptability to different geological environments. These issues lead to relatively low accuracy in ESP well fault diagnosis. To address these challenges, this paper integrates the mechanistic knowledge of ESP wells with their working parameters to construct a fault symptom inference model for ESP wells. A fault diagnosis model for ESP wells is formed by combining deep learning with an expert rule-based fault diagnosis method. The two models are connected in series to construct a mechanism knowledge-integrated ESP fault diagnosis model (MK-ESPFDM), achieving real-time and accurate diagnosis of faults in ESP wells. A series of experiments demonstrate that the proposed algorithm strategy can effectively improve the diagnostic accuracy of the model. It also reduces human subjectivity and enhances the model’s adaptability to different faults and geological environments. The research presented in this paper has reached a high level in the field of ESP well fault diagnosis. Full article

Pressure hulls are the primary pressure-bearing structures in submersibles and deep-sea space stations, which are essential for marine scientific research. Due to repeated dive cycles and extended operational periods, these hulls undergo creep deformation over time, posing risks to their structural integrity. This paper provides a comprehensive review of research on the creep behavior of pressure hulls, focusing on three key aspects: creep testing, creep constitutive models, and numerical simulation techniques. Initially, various creep testing methodologies are presented, with the experimental data serving as a foundational basis for subsequent analyses. Experimental data from creep tests form the foundation for constructing and validating constitutive models, which are critical for predicting long-term deformation. The review also explores advanced numerical simulation techniques, such as user subroutines and multiscale modeling, to analyze creep in complex pressure hull structures. Finally, based on the insights from the reviewed studies, the paper proposed potential directions for future research to address current challenges and enhance the design and maintenance of pressure hulls. Full article

Due to the limited availability of land resources, offshore wind turbines have become a crucial technology for the development of deep-water renewable energy. The multi-floating body platform, characterized by its shallow draft and main body located near the sea surface, is prone to significant motion in marine environments. The proper chamfering of the heave plate can effectively enhance its resistance during wave action, thereby improving the stability of the floating platform. The optimal chamfer angle is 35°. Considering the complexity of the floating body’s motion response, this study focuses on the damping characteristics of the heave plate with 35° chamfered perforations. Using the NREL 5 MW three-column semi-submersible floating wind turbine platform as the research model, the hydrodynamic characteristics of the floating body with a perforated heave plate are systematically studied through theoretical analysis, numerical simulation, and physical tests. The amplitude of vertical force under various working conditions is measured. Through theoretical analysis, the additional mass coefficient and additional damping coefficient for different working conditions and models are determined. The study confirms that the heave plate with 35° chamfered perforations significantly reduces heave in the multi-floating body. Full article

The semi-submersible platform is a widely used structure for supporting floating offshore wind turbines (FOWTs) in deep-sea environments where waves and currents interact. Understanding the impact of wave–current interaction (WCI) on hydrodynamic loading and the resulting platform response is essential for effective platform design. However, many existing ocean engineering software packages assume that wave and current loadings can be linearly superimposed. In this study, computational fluid dynamics (CFD) numerical simulations were performed to examine the dynamic response of a newly proposed triangle semi-submersible platform under various wave–current cases. The research underscores the significant influence of WCI on platform motion and loads, introducing nonlinearities that substantially affect both dynamic response and structural stability. Furthermore, the study reveals that WCI can mitigate vortex-induced motion (VIM), thereby enhancing platform stability by altering the force frequency, which no longer aligns with the platform’s natural frequency, thus preventing resonance. Additionally, the presence of current can intensify wave dynamics, leading to increased wave forces acting on the platform. These findings highlight the necessity of integrating WCI considerations into the design and optimization of floating wind turbine platforms to enhance their structural stability and operational performance. Full article

In special industrial fields such as electric submersible pump (ESP) wells, named entity recognition (NER) often suffers from low accuracy and incomplete entity recognition due to the scarcity of high-quality corpora and the prevalence of rare words and nested entities. To address these issues, this study introduces a character-level convolutional neural network (char-CNN) into the Flat-Lattice Transformer (FLAT) model and constructs nested entity matching rules for the ESP well domain, forming the char-CNN-FLAT-CRF model. This model achieves NER in the low-resource context of ESP wells. Through multiple experiments, the char-CNN-FLAT-CRF model demonstrates superior performance in this NER task compared to mainstream models and shows good recognition capabilities for rare words and nested entities. This research provides a methodological and conceptual reference for NER in other industrial fields that lack sufficient high-quality corpora. Full article

Electric Submersible Pumps (ESPs) play a pivotal role in the petroleum industry, but their performance is significantly affected by factors such as oil viscosity, gas–liquid ratios, and solid content. Traditional performance prediction methods, including polynomial fitting and mechanistic modeling, often lack adaptability and efficiency, requiring extensive empirical testing. This study leverages experimental data from the viscous and gas–liquid flow tests reported in the literature to benchmark various prediction methods. This research provides a comparative analysis of traditional curve-fitting methods, mechanistic modeling, and seven machine learning approaches. A key innovation of this study is an in-depth sensitivity analysis of different machine learning methods, especially focused on neural network parameters, such as activation functions and training configurations, to assess their impact on prediction accuracy and identify optimal network designs. Furthermore, a pump testing methodology is introduced to significantly reduce testing costs while maintaining a high prediction accuracy. The findings demonstrate the advantages of machine learning over traditional methods, including an enhanced prediction accuracy, practical guidelines for efficient parameter tuning, and the ability to address incomplete pump curve data. These contributions not only highlight the value of integrating machine learning into ESP modeling and operational workflows but also pave the way for future advancements in universal modeling frameworks for diverse ESP applications. Full article

The electrical submersible pump (ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, which is utilized to examine the relationship between production parameters and energy usage. This process aids in identifying the input parameters of the model. Following this, an energy consumption prediction model is developed using the dual-stage attention-based recurrent neural network (DA-RNN) algorithm. To evaluate the accuracy of the DA-RNN model, a comparison of its errors is carried out in comparison to three other deep learning algorithms: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transform. Lastly, an orthogonal experiment is executed using the chosen model to pinpoint the most energy-efficient operating regime. Analysis of 325 ESP wells in the Bohai PL oil field indicated that ten parameters, including choke diameter, casing pressure, pump inlet pressure, pump outlet pressure, motor temperature, frequency, oil production, gas production, water production, and GOR significantly impact the energy consumption of the ESP well system. Consequently, these parameters were selected as input variables for the deep learning model. Due to the attention mechanisms employed in the encoding and decoding stages, the DA-RNN algorithm achieved the best performance during model evaluation and was chosen for constructing the energy consumption prediction model. Furthermore, the DA-RNN algorithm demonstrates better model generalization capabilities compared to the other three algorithms. Based on the energy consumption prediction model, the operating regime of the ESP system was optimized to save up to 12% of the maximum energy. The energy consumption of the ESP well system is affected by numerous parameters, and it is difficult to comprehensively evaluate and predict quantitatively. Thus, this work proposes a data-driven model based on the DA-RNN algorithm, which has a dual-stage attention mechanism to rapidly and accurately predict the energy consumption of the ESP well system. Optimization of production parameters using this model can effectively reduce energy consumption. Full article

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. To this end, the concept of wave buoy analogy (WBA), which utilizes ships as sailing buoys, has captured practitioners’ attention due to its cost-effectiveness and extensive coverage. Despite extensive research, real-time sea-state estimation (SSE) has remained challenging due to the large observation window needed for statistical inferences. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram. For that, an adaptive ship response predictor and deep learning model were incorporated to classify seaway while minimizing network complexity through feature engineering. The system’s performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the approach for a fully automated SSE system. For further comparison of features of low- and high-fidelity modeling, the deficits with the feature transformation of the existing SSE models are discussed. This study provides a foundation for improving online SSE and promoting the seaway acquisition for stationary marine units. Full article

New conceptual designs for floating offshore wind platforms (FOWPs) are crucial for deep-sea wind power generation, increasing power output, lowering construction costs, and minimizing the risk of damage. While there have been various conceptual designs, tailored solutions for the South China Sea are limited due to the relatively harsh environment. This study proposes a novel 10 MW FOWP—“SparFloat”, which combines the advantages of a semi-submersible platform and Spar platform to cater for the sea conditions of the South China Sea. By systematically adjusting the distance between columns and the diameters of the side column and heave plate, the impact of geometrical changes in the platform on its dynamic response is investigated for the purpose of design optimization. The results highlight that roll/pitch natural periods are predominantly governed by restoring stiffness, whereas heave motion exhibits a higher sensitivity to added mass and radiation damping. Increasing the inter-column distance and side column diameter enhances stability and reduces roll/pitch natural periods, while enlarging the heave plate diameter extends the heave natural period. Time-domain simulations using a coupled FAST-AQWA framework confirm that the optimized design meets rule requirements, verifying the robustness and suitability of the SparFloat concept for the challenging environment of the South China Sea. Full article

The first record of bramble sharks (Echinorhinus brucus) in the United Arab Emirates is presented. In situ observations of multiple bramble sharks were made at depths between 460 and 720 m from two piloted submersibles and a remotely operated vessel, representing the first known observations of this species in its native deep-water habitat in Arabia and the Indian Ocean. Notably, this research expands on the documented regional distribution of E. brucus for the Gulf of Oman/Arabian Sea and extends this species’ regional records to deeper mesophotic zones. These findings underscore the need for further research to understand the ecology and distribution of this cryptic shark species, particularly given its global endangered status and the limited knowledge of its regional population dynamics. Full article