This study investigates methane leakage and diffusion from a buried high-pressure natural gas pipeline (8 MPa, 1000 mm diameter) using CFD simulations with the DES turbulence model. Based on homogeneous and layered soil models, the influences of soil porosity (0.46 to 0.54), particle size (10 μm to 100 μm), and soil stratification on the spatial and temporal characteristics of methane diffusion are systematically explored. The simulation results show that (1) methane diffuses from the leak hole to the surrounding soil in an ellipsoidal pattern, with the fastest diffusion speed along the pipeline’s axial direction. (2) In homogeneous soil, within the range of soil parameter values considered in this study, the absolute changes in risk assessment indices (FDR, GDR) caused by soil particle size were more significant; whereas the relative percentage changes in risk assessment indicators caused by soil porosity were more pronounced. (3) In layered soil, the permeability contrast between adjacent layers creates the permeability discontinuity interface effect. When a fine-grained or low-porosity layer overlies a coarse-grained layer, the upper layer acts as a hydraulic barrier, prolonging FDT from 130 s to 354 s while promoting significant horizontal spread at the interface. Conversely, a coarse-grained or high-porosity upper layer accelerates vertical breakthrough. These findings provide a scientific basis for risk assessment, monitoring site optimization, and emergency response planning, particularly in regions with heterogeneous stratified soils.

Understanding the law of gas diffusion in soil is essential for pipeline risk assessment. A 3D CFD model of natural gas leakage and diffusion from buried pipelines was developed in Ansys Fluent using the Detached Eddy Simulation turbulence model, with soil as an isotropic porous medium. Six parameters—burial depth, pipe diameter, transportation pressure, leak hole diameter, hole shape, and hole orientation—were systematically investigated under an identical numerical framework. First Danger Time (FDT), Farthest Danger Range (FDR), and Ground Danger Range (GDR) were used as unified hazard metrics for quantitative cross-factor comparison. As burial depth increases, FDT and FDR increase monotonically, whereas GDR peaks at 2.0 m. Increasing pipe diameter reduces FDT, FDR, and GDR. Higher pipeline pressure shortens FDT and expands FDR and GDR. Leak hole diameter dominates: increasing from 5 to 200 mm reduces FDT to nearly zero. For equal cross-sectional area, rectangular slits yield the highest mass flow rate and largest danger range, while triangular holes produce the shortest FDT owing to strong corner-induced turbulence. Upward leaks give the shortest FDT but smallest FDR; downward leaks produce the largest FDR; side leaks cause the longest FDT but largest GDR. Factor influence ranking by FDT and FDR change is: leak hole diameter, burial depth, pipeline pressure, hole orientation, pipe diameter, and hole shape. These results provide a quantitative framework for hazard assessment, sensor layout optimization, and emergency-response prioritization in buried natural gas pipelines.

The migration of offshore wind power into deep, remote waters exposes the techno-economic fragility of the traditional "single source–export" development model. Prohibitive capital costs for long-distance transmission infrastructure, coupled with the finite absorption capacity of onshore grids, severely curb the scalability of standalone bases. This paper systematically reviews the emergence of offshore wind integrated energy systems as a comprehensive solution to these bottlenecks. The analysis highlights that coupling wind with photovoltaics, wave energy, marine biomass, and ocean thermal or salinity-gradient energy harnesses spatiotemporal complementarity to dampen power-output fluctuations and ensure supply stability. Crucially, key pathways for in situ utilization—specifically marine ranching integration, seawater electrolysis for hydrogen and ammonia, the electrification of oil and gas platforms, and multi-energy integrated offshore energy islands are reviewed herein. These mechanisms transform surplus electricity into storable chemical energy or support local loads, effectively coping with electricity consumption fluctuations and relieving the pressure on long-distance transmission corridors. By establishing a holistic development framework spanning power generation, multi-energy complementarity, and diversified utilization, this study explores the synergistic logic required to transition from standalone wind farms to integrated offshore energy hubs. The "source-grid-load" integration framework ultimately provides the theoretical and methodological grounding necessary for the efficient planning and exploitation of future deep-sea wind energy resources.

Accurate prediction of natural gas dispersion from buried pipelines is critical for risk assessment and emergency response. However, conventional numerical simulations often simplify soil as a homogeneous isotropic porous medium, which deviates significantly from real-world conditions characterized by stratification, anisotropy, and spatial heterogeneity. This study systematically investigates the effects of these non-ideal soil characteristics on methane diffusion behavior using computational fluid dynamics (CFD). Four distinct soil models—a baseline homogeneous model, a layered model, an anisotropic model, and a spatially heterogeneous model—were constructed and compared under identical leakage scenarios. Key risk indicators, including First Danger Time (FDT), Farthest Danger Range (FDR), Ground Danger Range (GDR), and leakage mass flow rate, were quantitatively evaluated. Results indicate that soil layering enhances vertical migration and expands horizontal hazard ranges, reducing FDT by approximately 8%. Anisotropy introduces a pronounced directional dependence in gas migration, with horizontal-preferred permeability leading to severe underestimation of lateral risk by homogeneous assumptions. The spatially heterogeneous model exhibits reduced hazard ranges compared to the homogeneous case but accelerates early breakthrough. Comprehensive evaluation reveals that the homogeneous model systematically underestimates lateral diffusion distances and delays alarm times. This study provides a quantitative basis for selecting appropriate soil modeling strategies, emphasizing that incorporating soil heterogeneity is essential for reliable safety assessments of buried gas pipelines.

Buried-pipeline leakage poses significant safety risks, yet traditional CFD (Computational Fluid Dynamics) simulations are too slow for real-time diagnosis. This study integrates machine learning with interval sampling to develop a fast and interpretable prediction method. From 1.4 billion CFD-generated data points, 140 million representative samples were extracted via 1:10 interval sampling. Using 17 physical features as inputs, we trained and compared XGBoost, LightGBM, and a Multi-Layer Perceptron (MLP). The MLP model demonstrated exceptional performance (R² (R-squared) = 0.9988, RMSE (Root Mean Square Error) = 0.0153), significantly outperforming the tree-based models (R² ≈ 0.93). Three independent sampling runs confirmed its robustness (R² coefficient of variation~0%). SHAP (Shapley Additive Explanations) analysis identified spatial coordinates and leak aperture as the most critical factors, while also revealing the nonlinear influence of soil particle size. This approach offers a high-precision, interpretable, and efficient surrogate model for buried-pipeline leakage warning systems.