A key limitation of conventional early-stage oil tanker structural design is that the accidental limit state performance is rarely included as an explicit design objective, even though major topology and arrangement decisions are taken before detailed nonlinear analyses become feasible. This paper proposes a crashworthiness-driven structural design methodology for the concept design phase (CDP), in which crashworthiness is introduced as an explicit safety-related performance measure through surrogate modeling and used within a multi-objective optimization framework. Crashworthiness is represented by the internal energy absorption of a double-hull side structure under collision, which is obtained from a limited set of high-fidelity nonlinear simulations and approximated by response surface surrogate models to enable computationally efficient design-space exploration. The optimization framework considers structural weight and crashworthiness while enforcing rule-based adequacy constraints consistent with current classification practice, and it can be extended to additional safety-related measures. Application to an Aframax tanker case study demonstrates that Pareto-optimal solutions can be generated that improve the collision energy dissipation capability without disproportionate increases in structural weight at a stage where topology changes are still practical. The results confirm that crashworthiness-oriented criteria can be embedded within CDP design workflows in a manner compatible with established industrial practice.

The driver of oil fate in nearshore environments: MOPAs-mediated vertical transport and ecotoxicity.

Collapse failure analysis of the single-layer reticulated shell dome for a large-scale slag oil tank

This study proposes a design method based on adjoint shape optimization to enhance the heat transfer efficiency of shell-and-tube heat exchangers in oil and gas transportation systems. The primary focus of this work is the design optimization of shell-and-tube heat exchangers through geometric optimization. By simplifying the complex three-dimensional shell-and-tube heat exchanger into a pseudo-three-dimensional reduced-order model, two-dimensional adjoint shape optimization analyses were conducted under unidirectional symmetry about the x-axis and bidirectional symmetry in the x- and y-axes, respectively. The optimized two-dimensional models exhibited a significant increase in the average outlet temperature. Furthermore, the optimized two-dimensional shapes were extruded and reconstructed into three-dimensional models for validation. The results demonstrate that the average air outlet temperature of the three-dimensional models increased by 5.35 K and 3.07 K compared to the original design. Flow field analysis revealed that the heat transfer was improved, since the optimized pipeline layout enhances flow separation and turbulent mixing, leading to a more uniform temperature distribution. This study validates the effectiveness of the adjoint shape optimization method in improving the performance of shell-and-tube heat exchangers.

Resistance and Propulsion Study of a 17 500-DWT Oil Tanker Using Computational Fluid Dynamics and Slender-Ship Theory: Effects of Energy-Saving Device Fins

Potato harvesters operating in hilly and mountainous areas are often subjected to harsh working conditions such as high temperature, sun exposure, and high torque excavation. Due to the fluid sealing characteristics, closed loop hydraulic systems are prone to high temperatures during long-term continuous operation, resulting in a decrease in fluid viscosity, poor lubrication, severe wear, and power attenuation. This study investigates the hydraulic system of potato harvesters in hilly terrain, systematically analyzing its energy transfer process and identifying key heat-generating components. Based on an optimization strategy that extends the flow path of high-temperature fluid within the tank, four distinct tank designs were proposed. Computational fluid dynamics (CFD) and thermodynamic simulations were conducted to evaluate their heat dissipation performance, followed by full-machine validation testing. Results indicate that the walking and lifting systems are the primary heat sources. The dual pump contributes the highest proportion of heat (52.07%), followed by the walking motor (20.54%). The heat exchanger dissipates 72.91% of the heat, while the hydraulic oil tank accounts for 14.93%. Among the four tank designs, Tank 0 exhibited the fastest temperature rise, reaching a thermal equilibrium of 83.27 °C, whereas Tank 1 had the lowest equilibrium temperature (78.62 °C). Heat dissipation efficiencies for the tanks were 7.8%, 12.9%, 10.1%, and 11.6%, respectively. The residual gas volume fraction decreases significantly as the bubble diameter increases, due to the higher buoyancy and faster rise velocity of larger bubbles, which leads to shorter residence times and more effective precipitation. Tank 1 achieved the lowest equilibrium temperature, indicating the best thermal efficiency. Tank 3 showed the best overall degassing performance, particularly for medium-to-large bubbles. Tank 1 was selected as the optimal final design because it could offer an excellent balance, with very good cooling and competitive degassing (especially for small bubbles). Field tests confirmed a 14.8% reduction in thermal equilibrium temperature for Tank 1 (75.6 °C) compared to Tank 0 (88.7 °C). Simulation and experimental data showed strong agreement, with maximum errors of 9.2% for return fluid temperature, 12.7% for cooling return fluid temperature, 9.7% for pressure, and 8.5% for flow rate. Average errors remained below 8.4% for pressure and 7.6% for flow rate. These results validate the accuracy of the simulation model and the effectiveness of the tank optimization method.

ABSTRACT The extraction and transport of high-viscosity, high-pour-point crude oils present significant technical and cost challenges. While nanoparticle-based additives offer a promising avenue for improving flow properties, their practical application requires robust validation. This work details the synthesis of a novel nanohybrid polymer via oleic acid-functionalized nanosilica copolymerized with vinyl imidazole, icosyl acrylate, and maleic anhydride and its comprehensive evaluation as a flow improver for Indian waxy crude. Experimental analysis confirmed a substantial improvement in rheological behavior, with a 9 °C reduction in pour point and a > 60% decrease in viscosity. To assess the feasibility of field-scale application, the experimental data were further employed in a computational fluid dynamics (CFD) simulation using ANSYS Fluent 2024. The simulation results demonstrated an increase in the liquid fraction of crude oil by more than 60% and a reduction in pressure drop exceeding 78.8%. These findings underscore the efficacy of the synthesized nanohybrid polymer as a promising flow improver additive for enhancing the transportability of Indian waxy crude oils. Despite promising flow improvement, the performance of the nanohybrid additive may vary with crude oil composition and operating conditions. Future work will focus on aging studies, field-scale validation, recyclability and comprehensive environmental and lifecycle assessments.

ABSTRACT Composite defects (crack-stress coupling) in pipelines pose a major risk to oil and gas transportation. Accurately inverting stress distribution at these sites is a critical challenge for pipeline integrity management and precise safety assessment. This study develops a stress inversion model for internally detecting pipeline composite defects, using a data-driven dual-mode excitation collaborative method. This approach enables multi-physics coupling decoupling and fuses weak magnetic signals with coupled signals through data-driven fusion. The model integrates an improved Multiscale Convolutional Neural Network – Long Short-Term Memory Network (MCL-Net) and an enhanced Snow Ablation Optimisation algorithm (ISAO). Parallel multiscale convolutional kernels extract local details and long-range trends from magnetic signals, while a bidirectional LSTM – multi-head attention module captures temporal dependencies and key time-window features. Simulation results show the method achieves an R² of 0.9255, with RMSE and MAE of 0.1371 and 0.0851, respectively. In engineering experiments, it attains an R² of 0.9022, representing a 10.89% accuracy improvement over the BKA-Awarenet algorithm. The method significantly enhances stress inversion accuracy at composite defect sites, establishing a new theoretical foundation and providing effective technical support for precise pipeline safety assessment.

Purpose: Oil, gas, and water transportation is important through pipeline systems which are susceptible to various anomalies such as structural degradation, malfunctions in operations, and leakages. Older physics-based and rule-based methods of monitoring, despite their interpretability, tend to have low sensitivity, flexibility, and scalability. However, the absence of labeled fault data, increasing operational complexity, and non-stationary pipeline conditions create a critical gap in reliable and scalable anomaly detection solutions for real-world deployment. This study addresses this gap by systematically analyzing data-driven unsupervised and semi-supervised learning approaches and their applicability to pipeline monitoring. Materials and Methods: New developments in unsupervised and semi-supervised learning have made data-driven anomaly detection schemas able to learn typical operational behavior and detect anomalies with little assistance of labeled fault data. This review gives a detailed summary of these methods within pipeline monitoring. Among the methods discussed are distance- and density-based, statistical and subspace methods, and neural network-based methods, including autoencoders and self-organizing maps. Semi-supervised algorithms such as one-class classification and hybrid statistical-learning are also discussed. The review includes the issues of data characteristics, practices of evaluation, interpretability, and real-time implementation. Findings: The study identifies and discusses a variety of unsupervised and semi-supervised learning techniques that can effectively address the challenges faced by traditional monitoring methods in pipeline systems. It highlights how these data-driven methods are able to detect anomalies by learning typical operational behavior with minimal reliance on labeled fault data. The study also covers important considerations like data characteristics, evaluation practices, and the challenges of implementing these methods in real-time environments. Unique Contribution to Theory, Practice, and Policy: This review provides a thorough evaluation of emerging data-driven anomaly detection methods, contributing to the theoretical understanding of how unsupervised and semi-supervised learning can be applied in pipeline monitoring. The study's practical contribution lies in its exploration of real-world applicability, offering insight into methods that can enhance the sensitivity and scalability of anomaly detection in pipeline systems. For policy, the research suggests future directions, including enhanced feature learning, concept drift adaptation, and integration with digital twins, which aim to improve the trustworthiness and efficiency of anomaly detection in pipeline operations.

Offshore oil exploration and the volume of imported crude oil shipping have increased steadily, elevating the risk of oil spills. An advanced offshore oil film identification method is proposed to realize the accurate and robust recognition and segmentation of oil films from marine radar images in offshore oil spill detection. This method integrates feature engineering with an improved Beetle Antennae Search (BAS) optimization algorithm, aiming to address the key issues of low discrimination between oil films and complex marine backgrounds and insufficient spill boundary localization accuracy in radar image analysis. First, the raw radar image was transformed into the Cartesian coordinate system, and a filtering procedure was applied to attenuate interference. Subsequently, the gray distribution and local contrast of the denoised image was further improved. Afterwards, the complexity of the grayscale distribution within each feature map was quantified using Shannon entropy. The Top-K feature maps with the highest entropy values were subsequently used to construct an information-rich subset. The subset was then processed through a pixel-wise averaging strategy to generate a coupled feature image. Then, Otsu threshold was used to refine ocean wave regions. Finally, the oil films were segmented with an improved BAS optimization algorithm. The fitness function of the improved BAS algorithm was augmented through the integration of edge fitting accuracy, and a target-proximity penalization scheme. Through an adaptive step-length modulation paradigm and Perceptual Mechanism, it can achieve a marked improvement in search accuracy and achieving precise segmentation of oil slicks. The detection accuracy of the proposed method is significantly enhanced relative to the traditional BAS algorithm and existing marine radar oil spill detection methods. The IOU, Dice, recall and F1-score reached 81.2%, 89.6%, 85.2%, and 90.1% respectively. This method not only advances the methodological rigor of spill detection but also provides critical data support for the development of more effective control and remediation practices.

Numerous tasks of field practice are largely dependent on the physical properties of hydrocarbons, which include oil. The presence of paraffin, asphaltene and resins in oil has a significant impact on the movement mode, and, consequently, on the design of technological processes for drilling, well operation, field development and pipeline transport of oil. In many fields around the world, the process of pressure recovery when determining reservoir pressure lasts a very long time, in some cases several weeks or months, which is explained by the manifestation of nonequilibrium effects, i.e. elastic properties of liquid. Laboratory experiments have proven that if paraffins, asphaltenes and resins exist in the oil, then they give such oils viscoelastic properties, i.e., along with viscous properties, they also have elastic properties. These oils are considered from the perspective of nonequilibrium liquids, i.e., the rate of change in their internal structure is significantly lower than the rate of change in external conditions. To describe the behavior of visco-plastic-elastic properties, we propose a model taking into account the relaxing properties of the system. As our studies have shown, these properties cause a decrease in time of the filtration process, i.e., a decrease in the productivity of wells for hydrocarbons in a porous medium. This paper provides a model representation of the movement of such media in a pipeline and porous media. During the filtration process, the liquid appears as a suspension of small particles with a long relaxation time (elastic properties) and viscoelastic properties. It is assumed that the sizes of particles moving along with the liquid through a pipeline in a porous medium are subject to deformation, as if they harden and are fixed in the pipeline in a porous medium. The visco-elastic properties of oils can be effectively used during drilling. In a particular case, it can be suggested that when drilling intervals with anomalous oils, the hydrodynamic pressure in the well is significantly lower than the formation pressure. Keywords: visco-plasticity, elasticity, flow rate, volumetric flow rate, pressure, filtration process.

ABSTRACT The import/export industry is vital to global trade and economies, but maritime business contributes to environmental issues like carbon emissions, marine pollution, and resource consumption. With growing pressure to minimize these impacts, green technologies and sustainable practices in shipping have become crucial. This paper discusses green innovation in the maritime sector, emphasizing the need for strict regulatory standards. It examines low-carbon fuels, energy-efficient systems, intelligent transport, and autonomous ships for their role in reducing emissions and improving efficiency. Additionally, it explores measures such as cross-border coordination, policy clarity, and incentives to promote green technologies. Challenges include high acquisition costs, design inconsistencies, and technological constraints. However, increased research, infrastructure investment, and international rule harmonization can mitigate these barriers. Key stakeholders, including global and national organizations, play a role in shaping environmental standards for oil tankers. The future of sustainable maritime trade depends on governments, businesses, and technology providers advancing eco-friendly and efficient shipping. This study calls for exploring relevant theories and formulating effective policies to support maritime trade’s sustainable growth.

Robust and Sprayable Superhydrophilic/Air-Superoleophobic Fluoroethylene-(hydroxyalkyl) Vinyl Ether Composite Coating for Efficient Antiwaxing Performance in Crude Oil Transportation

To address the growing demand for advanced oil-control materials in cosmetics, this study developed novel flower-like mesoporous silica nanoparticles (FLS) with topology-enhanced oil–adsorption properties. Using a biphasic microemulsion synthesis strategy, FLS with petal-like surface topology, radial pore channels, and excellent colloidal stability were successfully prepared. Compared with conventional mesoporous silica nanoparticles (MSN) with small mesopores (2–3 nm) synthesized via the classical Stöber method, FLS exhibited significantly superior oil-absorption capacity across a wide range of oils, with maximum uptake nearly twice that of MSN. Notably, FLS showed exceptional adsorption efficiency for large-molecular-weight oils, demonstrating an approximately 226.3% increase over MSN in adsorbing PDMS-15000 w. This remarkable enhancement is attributed to the unique flower-like topology, which provides large open concave structures for instantaneous oil wetting and straight, radially aligned mesochannels for rapid oil transport and maximized pore utilization. In vivo human skin tests further confirmed the cosmetic efficacy of FLS. Collectively, these findings position FLS as a next-generation oil-control material and highlight topology-enhanced oil adsorption as a novel design strategy for advanced adsorbents.

“Our oil tank is nearly empty”: Energy Scarcity, Precarious Lives and the Quest for Sustainable Future in Suzanne Weyn’s Empty

Polymers with high molecular weights can significantly reduce the frictional drag in turbulent flow. However, under the strong hydrodynamic force of turbulent flow, chains of the polymer's macromolecules may undergo significant mechanical degradation, leading to a loss of drag reduction effectiveness. This paper proposes using oil-soluble surfactants for turbulent drag reduction in oil transport. By disk rheometer experiments and molecular dynamics simulations, four oil-soluble surfactants-heptadecenyl hydroxyethyl imidazoline (HEHI), sorbitan monolaurate (Span20), didecyl phosphate (AP10), and ditridecyl hydrogen phosphate (DHP) were selected to elucidate the coupling mechanism between the dynamic micellar network structures and rheological properties. Our study establishes quantitative links between surfactant microstructure and macroscopic drag reduction. Molecular dynamics simulations show that the optimal mean square displacement for HEHI surfactant is 83 Å. Exceeding the critical micelle concentration of 250–500 ppm for HEHI negatively impacts its drag reduction efficiency. Disk rheometer experiments measured a maximum drag reduction (DR) of 17.8% at 250 ppm for HEHI, while DHP achieved 10.9% DR at 500 ppm. HEHI and Span20 buffer shear stress via hydrophobic chains. DHP and AP10 use charge repulsion to limit aggregation and sustain stable performance under high shear. All four surfactants exhibit strong shear resistance. This work demonstrates the critical role of surfactant type and concentration in oil drag reduction, revealing excellent shear resistance to guide industrial transport optimization.

Assessing the risk of coastal oil strandings in the Brazilian equatorial margin: A numerical modeling approach.

Transportation of Heavy and Extra-Heavy Crude Oil by Pipelines. An Updated Review

Probabilistic risk assessment for inert gas system on oil tanker ships using system theoretic accident model and process (STAMP) and Bayesian belief network (BBN)

A Fine-Grained Behavior Classification Method for Oil Tankers Based on Bidirectional Long Short-Term Memory Network