Search Results (131)

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

With the transition to deep coal mining, the transparent detection of hidden geological hazards in the floor strata is fundamental for production safety. In mine seismic exploration, gliding waves—inhomogeneous plane waves propagating along the coal–rock interface—offer a unique advantage for penetrating high-velocity floors via the skin effect, overcoming the total reflection limitations of conventional in-seam waves. This study investigates the propagation laws and anomaly response characteristics of floor gliding waves using super-critical incidence theory and high-order staggered-grid finite difference simulations. The results demonstrate that the apparent velocities of gliding P and S-waves are bounded by those of the coal and host rock, exhibiting minimal dispersion. Quantitative analysis using a penetration depth model reveals that while penetration depth is frequency-dependent—with lower frequencies providing deeper reach—high-frequency components remain essential for high-resolution imaging. Crucially, the proposed method was validated through a field Case Study at the 11123 working face. By utilizing a specialized deep-hole excitation strategy to ensure super-critical incidence, the inversion successfully identified a hidden fault extending up to 60 m below the floor, which was subsequently confirmed by rock roadway excavation. These findings establish a robust physical basis for designing underground floor-detection systems and provide a significant theoretical reference for addressing detection blind spots in deep mining environments. Full article

Compact finite difference schemes approximate spatial derivatives through implicit relations between neighboring grid points. Despite using compact stencils and relatively simple algebraic structures, these schemes achieve high-order accuracy and spectral-like resolution, reducing dispersion errors while maintaining low numerical dissipation. These properties make them particularly attractive for problems requiring accurate spatial derivatives and computational efficiency, such as wave propagation, aeroacoustics, and turbulent flow simulations. This review presents the main ideas behind compact finite difference schemes, including their derivation from Taylor expansions and Padé approximations, their accuracy properties, and their resolution characteristics through modified wavenumber analysis. The manuscript is intended as a review and practical synthesis, rather than as the proposal of a new numerical scheme, and aims to connect the theoretical construction of compact schemes with their numerical behavior, practical implementation, and representative applications. To support reproducibility, we provide a fully documented open-source Python 3.11 notebook with a reference implementation of the schemes discussed in the paper. The examples include first- and second-order derivative calculations and representative one- and two-dimensional boundary-value problems, including Helmholtz-type equations. Finally, we survey applications across computational fluid dynamics, acoustics, geophysical flows, structural mechanics, biology, electromagnetism, and quantitative finance. Full article

Ambient air pollution remains a major public health concern, contributing to millions of premature deaths worldwide according to the World Health Organization. Regional air quality assessments are commonly performed using chemical-transport models that require substantial computational resources due to their detailed representation of atmospheric processes. This study explores the feasibility of applying the second-generation dispersion model URBAIR^® as a computationally efficient alternative for long-term regional air quality simulations. URBAIR^® was implemented for three European case studies within the DISTENDER project to simulate particulate matter (PM₁₀) and nitrogen dioxide (NO₂) concentrations for 2018 under different spatial and temporal resolutions. Model performance was assessed against background monitoring stations and compared across grid configurations. The results show that the model successfully reproduces annual mean concentration patterns, particularly in urban areas, with R² values ranging mostly between 0.2–0.6, RMSE between 16–36 µg.m⁻³, and mean bias from −8 to 5 µg.m⁻³, indicating overall acceptable statistical performance. Within the specific configurations evaluated in this study, increasing spatial resolution was not consistently associated with improved model performance. However, because spatial resolution covaried with other factors including meteorological temporal resolution, domain characteristics, and monitoring station density, the present analysis does not allow the independent effect of spatial resolution to be isolated. Moreover, a key limitation of the modeling approach is the absence of chemical transformation processes, which may affect the representation of secondary pollutants. Overall, the dispersion-based modeling framework substantially reduces computational demand and input complexity, proving suitable for long-term exposure and climate-related applications when annual average concentrations are the primary objective. In future studies, the modeling approach should be applied to other case studies to consolidate the findings of this exploratory work so that it may contribute to sustainability-oriented decision making by facilitating regional assessments of air quality and potential health impacts related to climate change. Full article

Modeling over-dispersed count data is a common challenge in applied statistics, especially in engineering applications where repeated events, system faults, and clustered observations often produce variability beyond that allowed by the classical Poisson model. In this paper, we introduce and study the Poisson–QGamma distribution, a new compound discrete model obtained by mixing the Poisson distribution with the QGamma distribution. The proposed distribution is analytically tractable and flexible enough to capture over-dispersion, skewness, and excess kurtosis, which are frequently observed in real count data. Several statistical properties of the distribution are derived, including the probability mass function, cumulative distribution function, survival and hazard rate functions, moments, dispersion index, skewness, kurtosis, entropy, and generating functions. Parameter estimation is considered using maximum likelihood, method of moments, least squares, and weighted least squares methods. The finite-sample behavior of these estimators is examined through Monte Carlo simulation. A regression model based on the Poisson–QGamma distribution is also developed for count responses with covariates. The proposed model is compared with classical and competing count models using simulation and real-data applications. Three engineering-related datasets, involving power grid failure counts, environmental sensor event counts, and packet loss counts in communication networks, are analyzed to illustrate the practical value of the model. The results show that the Poisson–QGamma model provides a better fit than several standard alternatives, including the Poisson, negative binomial, Poisson–Lindley, generalized Poisson, and COM–Poisson models, particularly in the presence of over-dispersion and heavy-tailed behavior. Overall, the proposed distribution offers a parsimonious and effective tool for modeling over-dispersed count data, while also contributing to the broader class of compound discrete distributions. Full article

As industrial parks face increasing pressure to balance economic development with environmental sustainability, optimizing emission strategies becomes critical for achieving sustainable development goals. In this study, a pollutant dispersion module is coupled with the WRF-FDDA-LES (Weather Research and Forecasting four-dimensional data assimilation and large-eddy simulation) to establish a multiscale air quality model for the Pengzhou Industrial Park, Sichuan, China, hereafter referred to as PZ-LESTD. Using PZ-LESTD, the study conducts refined large-eddy simulations of pollutant dispersion from elevated sources in the industrial park on 23 August 2022. The capability of the model in simulating large-scale weather conditions and pollutant transport, together with its performance in refined-grid LES of elevated emission dispersion, is evaluated. Sensitivity experiments with different pollutant emission heights are also carried out. The results demonstrate that the model can satisfactorily reproduce large-scale meteorological variables and pollutant distributions over China and achieve high accuracy in the refined LES simulations. Analysis of the simulated dispersion processes of elevated sources indicates that the current elevated emission strategy in the Pengzhou Industrial Park is effective in mitigating the impact of industrial exhaust on surface air quality in the park and surrounding areas. Sensitivity tests of emission heights reveal that source heights of 20 m to 50 m can significantly reduce impacts on nearby ambient air quality, whereas increasing the source height from 50 m to 160 m results in only minor differences in surface-level pollution, although higher emission sources lead to greater horizontal transport of pollutants. This study provides scientific evidence for sustainable industrial planning and emission management strategies, supporting the transition towards environmentally sustainable industrial parks. The findings contribute to evidence-based policymaking for air pollution prevention and control, facilitating the achievement of sustainable development goals through optimized industrial emission layouts and green industrial transformation. Full article

Grid-level population projection is essential for spatial planning under demographic decline, particularly for ensuring that population allocation accounts for grid extinction risk. This study develops a two-stage machine learning framework to predict residential grid transitions across South Korea’s 1 km grid system and assess how spatial policies shape depopulation outcomes through 2050. Stage 1 employs Random Forest classification to predict grid state transitions (macro-averaged F1 score = 0.694), while Stage 2 applies LightGBM regression for population prediction (coefficient of determination = 0.950). The extinction probability map from Stage 1 is incorporated into scenario simulations to adjust population allocation based on predicted residential viability. Feature importance analysis reveals that baseline population, household count, and demographic composition are key determinants of grid-level residential transitions. Five spatial development scenarios simulated through 2050 reveal substantial policy sensitivity. Cumulative extinction rates range from 3.1% under extreme dispersion to 24.5% under extreme concentration, representing a 25 percentage point divergence attributable to spatial allocation policy. Provincial heterogeneity is pronounced, with rural provinces facing extinction rates up to 39.9% while metropolitan areas remain largely unaffected. Comparing scenario outcomes enables pre-identification of policy-sensitive grids (19.5%) where allocation choices determine residential survival. These grids are predominantly located in areas with high forest cover and greater spatial isolation compared to stable grids, but differ in demographic profiles. Aging-Vulnerable grids (14.0%) exhibit high aging ratios with limited economic base, while Moderate-Vulnerability grids (5.5%) show younger demographics with relatively higher economic activity. These differential characteristics provide a spatially explicit basis for differentiated policy responses. Beyond depopulation planning, the spatial outputs of this framework can inform related planning domains such as land use transition planning, carbon management, and infrastructure prioritization under demographic decline. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

The diffusion process of heavy gas during instantaneous leakage is significantly influenced by wind speed. Accurately characterizing the coupling relationship between wind speed and heavy gas diffusion is crucial for accident risk assessment and emergency response. Based on the Thorney Island 008 test, this study employs computational fluid dynamics (CFD) numerical simulation to construct a gas leakage diffusion model. Through grid independence verification and comparison with measured data, the optimal simulation scheme is determined. Design five wind speed conditions of 0.5 m/s, 1 m/s, 3 m/s, 6 m/s, and 10 m/s to investigate the division of heavy gas dispersion phases, the cloud radius modification model, and the spatiotemporal distribution characteristics of downwind concentrations. The study clearly identifies that heavy gas leakage dispersion can be divided into three stages: gravity diffusion, density stratification, and passive diffusion. By introducing a dimensionless wind speed correction term to improve the cloud plume radius prediction model, the validation results show that the calculated values from the modified model align with the trend observed in CFD simulations. Under all wind speed conditions, the maximum relative error remains within 10%. Downwind gas concentration distribution characteristics reveal that in the near-source areas (25 m, 100 m), higher wind speeds correlate with higher peak gas concentrations and shorter peak arrival times. Conversely, in the mid- and far-field zones (200–500 m), lower wind speeds are associated with higher peak gas concentrations and longer peak arrival times. The cloud radius modification model proposed in this study enables the prediction of heavy gas cloud radii under varying wind speeds within specific conditions. The revealed characteristics of the diffusion phase and the spatiotemporal distribution patterns of gas concentrations provide scientific basis for risk zoning and emergency response planning in heavy gas leakage incidents. Full article

Efficient on-site connection of power cable conductors is critical for ensuring the safe operation of the power grid. Traditional thermite welding methods pose significant safety risks, including open flames and fumes. Meanwhile, induction heating, when applied to cable conductors, faces challenges of severe magnetic field dispersion, low heating efficiency, and a high risk of damaging adjacent insulation layers. This paper proposes a novel magnetic flux control system based on gradient permeability ceramics to address these issues. The core of this system is the synergistic utilization of a gradient permeability composite ceramic mold and a high-permeability shielding shell. A 2D axisymmetric multiphysics coupled model was established to compare the performance of the optimized system with a conventional case and single control components. Simulation results demonstrate that the optimized system increases the magnetic flux density at the weld seam to 3.7 times that of the conventional setup (0.263 T). Consequently, the weld seam of the 240 mm² copper conductor is rapidly heated to the melting point of copper (1083 °C) within 7.78 s. Due to the high heating rate, upon completion of the welding process, the temperatures of the inner shielding and insulation layers are only 48.8 °C and 24.3 °C, respectively, well below the materials’ safety thresholds. These findings suggest that the proposed magnetic flux control strategy achieves rapid and precise heating, offering a theoretical foundation for the development of high-performance on-site equipment for fabricating cable joints. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

This study presents a quantitative simulation analysis of hydrogen-enriched methane (HENG) storage with nitrogen as the cushion-gas in a depleted gas reservoir by varying three key operational parameters: the injection/withdrawal period, hydrogen blending ratio (5–20%), and injection depth. Ten injection–withdrawal cycles were modeled for each scenario, and performance was evaluated using cycle-averaged and cumulative hydrogen purity, recovery factors, and the mixing zone size. Extending the injection period increased hydrogen purity to 20.00–20.26% and reduced nitrogen to 0.001–0.003%, but recovery decreased from 65.63 to 53.83–41.09% due to enhanced dispersion and residual trapping. The blending ratio was the dominant control: 20% blending yielded 19.9–20.0% purity with nitrogen as low as 0.00–0.03%, whereas 5–10% blending produced lower purity but minimized nitrogen production to 0.97–1.08%. Injection depth affected nitrogen recovery more than purity, increasing from 0.72–1.20% (upper) to 1.46–1.61% (lower), along with thicker mixing zones. Final mixing zone size ranged from 3176 to 5546 blocks, with smaller zones consistently linked to higher purity and lower nitrogen breakthrough. The shut-in period further reduced nitrogen recovery from 6.49 to 1.33% and stabilized mixing behavior. Overall, minimizing late-cycle mixing zone thickness is essential for maintaining HENG storage performance. Although this study provides quantitative insights into HENG operational strategies, the use of a homogeneous grid and simplified fluid properties limits representation of geological heterogeneity and reactive processes. Future work will incorporate heterogeneity and reaction modeling into field-scale simulations to validate and refine these operating strategies for practical deployment. Full article

A quantitative understanding of methane leakage has become essential for safety design as eco-friendly fuel systems expand in modern ship applications. To address this need, controlled methane-release experiments were conducted in a large indoor chamber (30 × 16 × 20 m) to evaluate how sensor-network resolution (1 m vs. 0.5 m spacing) influences dispersion measurement and 5% Lower Explosive Limit (LEL)-based risk assessment. Initial tests with a 1 m grid showed that most sensors detected only low concentrations except for near the release nozzle, demonstrating that coarse spatial resolution cannot capture the primary dispersion pathway or transient peaks. This limitation motivated the use of a 0.5 m high-density sensor network, which enabled clear identification of the dispersion centerline, concentration-gradient development, early detection behavior, and the evolution of diluted regions, particularly under buoyancy-driven plume rise. Experimental results were compared with CFD simulations using the RNG k–ε and k–ω GEKO turbulence models. Strong agreement was obtained in peak concentration, concentration-rise rates during the accumulation phase, and LEL-based dispersion distances. These findings confirm the suitability of the selected turbulence models for predicting methane behavior in large enclosed spaces and highlight the sensitivity of model–experiment agreement to measurement resolution. The results provide an experimentally grounded reference for sensor layout design and verification of gas-detection strategies in ship compartments, fuel-gas preparation rooms, and modular supply units. Overall, the study establishes a methodological framework that integrates high-resolution experiments with CFD modeling to support safer design and operation of methane-fueled vessels. Full article

Numerical modelling of airflow in underground mines is gaining importance in modern ventilation system design and safety assessment. Computational Fluid Dynamics (CFD) simulations enable detailed analyses of air movement, contaminant dispersion, and heat transfer, yet their reliability depends strongly on the accuracy of the geometric representation of excavations. Raw point cloud data obtained from laser scanning of underground workings are typically irregular, noisy, and contain discontinuities that must be processed before being used for CFD meshing. This study presents a methodology for automatic segmentation and regularization of large-scale point cloud data of underground excavation systems. The proposed approach is based on skeleton extraction and trajectory analysis, which enable the separation of excavation networks into individual tunnel segments and crossings. The workflow includes outlier removal, alpha-shape generation, voxelization, medial-axis skeletonization, and topology-based segmentation using neighbor relationships within the voxel grid. A proximity-based correction step is introduced to handle doubled crossings produced by the skeletonization process. The segmented sections are subsequently regularized through radial analysis and surface reconstruction to produce uniform and watertight models suitable for mesh generation in CFD software (Ansys 2024 R1). The methodology was tested on both synthetic datasets and real-world laser scans acquired in underground mine conditions. The results demonstrate that the proposed segmentation approach effectively isolates single-line drifts and crossings, ensuring continuous and smooth geometry while preserving the overall excavation topology. The developed method provides a robust preprocessing framework that bridges the gap between point cloud acquisition and numerical modelling, enabling automated transformation of raw data into CFD-ready geometric models for ventilation and safety analysis of complex underground excavation systems. Full article

The development of electric quarry transport puts a significant strain on local power grids, leading to sharp peaks in consumption and degradation of power quality. Existing methods of peak smoothing, such as generation control, virtual power plants, or intelligent load management, have limited efficiency under the conditions of stochastic and high-power load profiles of industrial charging stations. A new strategy for direct charge and discharge management of a system for integrated battery energy storage (IBES) is based on dynamic iterative adjustment of load boundaries. The mathematical apparatus of the method includes the formalization of an optimization problem with constraints, which is solved using a nonlinear iterative filter with feedback. The key elements are adaptive algorithms that minimize the network power dispersion functionality (i.e., the variance of $P_{grid}\left(t\right)$ over the considered time interval) while respecting the constraints on the state of charge (SOC) and battery power. Numerical simulations and experimental studies demonstrate a 15 to 30% reduction in power dispersion compared to traditional constant power control methods. The results confirm the effectiveness of the proposed approach for optimizing energy consumption and increasing the stability of local power grids of quarry enterprises. Full article