Search Results (10,055)

Gas blending with hydrogen represents a core research direction for present and future energy transport systems. The throttling of natural gas and hydrogen mixtures through pressure-regulating valves inevitably induces thermodynamic temperature variations. Theoretical analyses and simulated thermal profiles demonstrate that hydrogen blending effectively counteracts the extreme expansion temperature drop post-throttling. This thermodynamic shift alleviates the localized microclimatic thermal conditions favorable to ice-plugging, validating the feasibility of hydrogen injection as a systematic thermal mitigation strategy for high-pressure pipeline networks. This study utilizes computational fluid dynamics software to model the flow field variations in pure hydrogen and gas–hydrogen mixtures under the influence of pressure-regulating valves. Employing a real gas equation of state across varying operational temperatures and pressure conditions, this research calculates and analyzes the flow field variations driven by the Joule–Thomson effect for pure hydrogen and mixtures with varying hydrogen blending ratios. The objective is to inform temperature regulation strategies for long-distance hydrogen–natural gas pipeline networks and to establish an empirical temperature fitting relationship for pure hydrogen. The numerical evaluation indicates a maximum relative error of 6.02% and a maximum absolute error of 0.06877 K. Furthermore, guided by the localized temperature variation patterns, the temperature rise results from 75 pure hydrogen simulation cases were extracted. A Multilayer Perceptron artificial intelligence algorithm was utilized to perform inverse calculation iterations on the thermal data and regulation results. Through the stochastic selection of initial parameters and repeated training iterations referencing the fitting formula, an optimized regulation sequence was obtained. This process drives the fluid temperature to approach the practical regulation target. Following the network training phase, the maximum absolute error between the calculated temperature regulation result and the target regulation temperature is recorded at 0.0556 K, providing a methodological reference for subsequent high-pressure hydrogen applications. Full article

To clarify the oil–gas multiphase flow behavior of natural gas/CO₂ composite flooding in the dual-medium system of the BZ26-6 fractured reservoir, systematic oil–gas relative permeability experiments were conducted under reservoir temperature and pressure conditions. Using the steady-state method, the effects of core type, gas composition, and reservoir pressure on relative permeability behavior were investigated. The results show that the relative permeability curves are characterized by relatively high oil-phase permeability and low gas-phase permeability. Increasing the CO₂ fraction generally enhances oil mobilization and displacement efficiency, whereas the two-phase co-flow zone may reach an optimum at an intermediate CO₂ fraction, depending on the core structure. Specifically, with increasing CO₂ fraction, displacement efficiency increased from 37.05% to 43.70% in fractured metamorphic cores and from 60.74% to 64.63% in fractured carbonate cores. In contrast, decreasing reservoir pressure may induce stress-sensitive fracture compression, narrow the co-flow zone, and reduce flow capacity. Oil–gas two-phase flow behavior is strongly controlled by reservoir structure, with fractured carbonate cores exhibiting higher displacement efficiency and a wider co-flow region than fractured metamorphic cores. Within the scope of this study, a CO₂ fraction of 40% appears to be a comparatively favorable composite-gas composition when both displacement performance and gas-source economics are considered. Full article

To address the lack of systematic quantitative characterization of oil bank dynamic evolution and unclear dominant controlling factors in polymer flooding, this study combines reservoir numerical simulation with Python-based quantitative analysis and a machine learning framework (random forest + SHAP). We established 1D and 2D reservoir models: the 1D model develops a precise quantitative characterization method for oil bank width (defined by front/rear edge saturation offsets Pf < 1.0% and Pb < 1.0%, fitted with a cubic polynomial, R² > 0.95) and height (derived from optimal oil saturation difference time curves and integral calculation); the 2D model investigates the regulatory mechanism of reservoir heterogeneity. Based on 15,000 sets of physically consistent simulation data, the random forest model achieves high prediction accuracy (R² = 0.98). Sensitivity analysis reveals that main flow direction permeability, reservoir temperature, and water-phase exponent (nw) of the Corey model are the dominant controlling parameters, exhibiting substantially higher sensitivity than polymer adsorption capacity and residual resistance coefficient. The oil bank height shows a negative correlation with the first two parameters, while it displays a peak-type variation with the water-phase exponent. Under heterogeneous conditions, permeability anisotropy amplifies the regulatory effect of relative permeability exponents, leading to unbalanced oil bank migration (quantified by front ratio R). This study breaks through the limitations of traditional qualitative characterization, elucidates the spatiotemporal evolution laws and heterogeneous regulatory mechanisms of the oil bank, and provides reliable theoretical and dataset support for optimizing polymer flooding schemes. Full article

Internal combustion engines are a well-established, efficient and dispatchable solution for distributed power generation and they are widely used in various sectors including grid balancing, data centers and combined heat and power systems. Current research efforts focus on further increasing efficiency, enabling decarbonization through renewable fuels and improving responsiveness to electricity demand in the presence of variable renewable energy sources. In this context, the free-piston linear generator (FPLG) stands out as a highly promising technology, as it directly converts piston motion into electricity, offering high efficiency, reduced mechanical complexity and seamless grid integration. Initially explored for its high-efficiency potential with homogeneous charge compression ignition combustion at extreme compression ratios, opposed-piston FPLGs are now commercially available for distributed power generation, delivering global efficiencies exceeding 45%, near-zero emissions and multi-fuel capability. Building on the detailed studies conducted by Svrcek and co-authors, this work investigates the power-generation potential of low-temperature homogeneous combustion using CFD simulations with detailed chemical kinetics. First, rapid compression machine (RCM) experiments with methane were reproduced in simulations to validate the proposed methodology and to consolidate experimental findings on the maximum achievable efficiency. Subsequently, an extensive RCM simulation campaign supported the identification of optimal operating conditions in terms of air–fuel ratio using methane as fuel. The RCM results enabled the definition of a preliminary methane-fueled opposed-piston FPLG configuration. Full-cycle simulations including gas exchange, mixing and combustion demonstrated an indicated efficiency of 58% at an equivalence ratio $\varphi =0.5$ and a compression ratio of 50. The key novelties of this study are the development of a novel RCM-2 configuration that more closely reproduces the dynamic behavior of an opposed-piston FPLG including air-spring effects and the introduction of a divided intake port strategy to simultaneously reduce fuel slip and mitigate knocking behaviour through charge stratification. The simulation results for the proposed configuration confirm the potential of opposed-piston FPLGs for high-efficiency power generation and highlight key parameters affecting performance and emissions formation. Full article

The pearlitic microstructure, comprising alternating lamellae of ferrite and cementite, provides a favorable combination of strength, toughness, and wear resistance. Consequently, pearlitic steels have been widely utilized in pipeline systems due to their advantageous mechanical properties and cost-effectiveness. These characteristics also render pearlitic steel pipelines promising candidates for hydrogen transport infrastructure, particularly in the context of repurposing existing natural gas networks. However, interactions between hydrogen and the pearlitic microstructure raise significant concerns regarding hydrogen embrittlement, a phenomenon that can substantially degrade mechanical performance and compromise long-term structural integrity. Experimental observations indicate that pearlitic microstructures are particularly susceptible to hydrogen embrittlement, largely due to the high density of ferrite–cementite interfaces, which act as effective hydrogen trapping sites. These detrimental effects motivate the present study, which aims to develop a deeper understanding of nanoscale mechanisms of hydrogen-assisted crack initiation and propagation in pearlitic microstructures. In this work, molecular dynamics simulations are employed to investigate the initiation and propagation of hydrogen-affected cracks in pearlitic microstructures, considering lamellar orientations both parallel and perpendicular to the applied tensile loading direction. The analysis focuses on the synergistic interaction between hydrogen-enhanced decohesion (HEDE), which promotes interfacial separation due to hydrogen segregation, and hydrogen-enhanced localized plasticity (HELP). Full article

Support structures in liquid hydrogen tanks act as localized thermal bridges between the ambient temperature outer vessel and the cryogenic inner vessel. However, the difference between support thermal bridge-induced localized heat input and equivalent uniform heat input remains insufficiently clarified, especially regarding their effects on local thermal behavior and support position-dependent thermodynamic response. In this study, a gas–liquid two-phase CFD model was developed for a 37.4 m³ liquid hydrogen tank at a 50% filling ratio. Localized heat flux regions were used to represent support thermal bridges, and an equivalent uniform heat input case with the same total heat input was introduced for comparison. The results show that localized support heat input concentrates the high-temperature region near the support-corresponding wall area and induces stronger local natural convection with a maximum velocity of approximately 0.27 m/s, compared to approximately 0.14 m/s in the uniform heat input case. The uniform heat input case produces a slightly higher overall gas-phase pressure, but it cannot capture the local heat accumulation and flow field reconstruction caused by support thermal bridges. Circumferential support position variation mainly affects the relative position between the localized heat source, gas region, liquid region, and gas–liquid interface. Upper support position variation has a more pronounced influence on local peak temperature and flow intensity than lower support variation. Axial support position variation mainly shifts the local high-temperature and high-velocity regions along the tank length, while its influence on overall pressure response is limited. These results indicate that equivalent uniform heat input can approximate the overall pressurization trend, but localized support heat input boundaries should be retained when local temperature fields, flow structures, and support layout effects are of concern. Full article

This study was conducted to explore how varying the concentration of nano-La₂O₃ particles in the plating bath influences the morphology, constitution, and corrosion resistance of Ni-B composite coatings deposited on N80 carbon steel via electroless plating. The novelty of this work lies in the systematic investigation on the co-deposition behavior and grain refinement mechanism of nano-La₂O₃ in electroless Ni-B system, which has been rarely reported in previous studies. The microstructure and chemical composition of the coatings were characterized through a combination of SEM, EDS, XPS and XRD analyses. SEM confirmed that a dense Ni-B/La₂O₃ composite coating was formed, with a uniform thickness of approximately 10 μm, and the nano-La₂O₃ particles were evenly distributed. XPS analysis verified the presence of B, C, O, Ni and La, while XRD analysis revealed a refinement in crystalline size due to the addition of the nanoparticles. The corrosion resistance enhancement mechanism is attributed to the triple synergistic effect: nano-La₂O₃ pins grain boundaries and refines Ni-B grains to the minimum average size of 12.943 nm at the optimal concentration of 8 g·L⁻¹; the refined grain structure promotes the formation of a continuous and dense Ni(OH)₂ passive film; the uniformly dispersed nanoparticles act as physical barriers to block the penetration of corrosive media. Electrochemical measurements demonstrated that this coating exhibited outstanding anti-corrosion performance, as confirmed by a remarkably positive corrosion potential (E_corr = −0.37189 V) and a minimal corrosion current density (I_corr = 3.7524 μA/cm²). The results conclusively show that nano-La₂O₃ reinforcement effectively enhances the corrosion protection performance of electroless Ni-B alloy coatings. Full article

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

To address hydrogen separation from hydrogen-blended natural gas, this work develops a mathematical model for a novel thermal-transpiration-effect-based circulating-flow gas separator according to the Navier–Stokes equations, following the joint modification with velocity-slip and temperature-jump boundary conditions, and a binary gas diffusion model derived from the Maxwell–Stefan equations. The model is then used to investigate the component transport and flow of a CH₄-H₂ mixture at the slip flow regime. The average hydrogen mole fraction in the component enrichment zone increases monotonically as the temperature difference increases, reaching 0.429 at a hot channel temperature of 400 K. An optimum inlet gas velocity of 0.93 m/s is identified to achieve the maximum average hydrogen mole fraction in the enrichment zone. In addition, decreasing the microchannel diameter enhances the hydrogen enrichment performance, with the average hydrogen mole fraction reaching 0.578 at a microchannel diameter of 1 μm whereas increasing the microchannel diameter improves the product gas flow rate, indicating a trade-off between separation performance and processing capacity. These insights provide guidance for understanding the component transport mechanism and for the preliminary design of this type of gas separator for hydrogen separation applications. Full article

Lubricating oil monitoring provides continuous health information for early fault warning and maintenance decision-making in industrial gas turbines. However, real-world multi-sensor monitoring streams exhibit pronounced nonstationary thermodynamic drifts that often obscure subtle high-frequency residuals containing critical incipient degradation signatures. Prevailing data-driven monitoring models typically struggle to separate these macroscopic trends from stochastic wear-related fluctuations, and their restrictive distributional assumptions are often inadequate for the heteroscedastic and heavy-tailed nature of industrial residuals. To address these challenges, this study proposes ResAD-Net, a framework for early fault warning in nonstationary multi-sensor oil monitoring that combines trend–residual decoupling, trend-conditioned residual modeling, and residual-domain dependency learning. Specifically, a signal trend–residual decoupling strategy is adopted to separate slowly varying operational trends from stochastic residual fluctuations captured by the sensors, thereby exposing residual information that is more sensitive to incipient degradation. On this basis, a trend-conditioned diffusion model is introduced to characterize state-dependent, skewed residual distributions and generate residual sample ensembles for nonstationary monitoring. Meanwhile, a graph-based variational autoencoder is employed to learn latent intersensor dependency structures from the residual domain, providing diagnostic cues for temporal risk evolution analysis and sensor-level inspection. Experiments on a real-world industrial oil-monitoring record show that the proposed framework achieves an average F1-score of 0.985 with no observed false positives in the predefined pre-alarm reference interval of the finite test set. In addition to accurate anomaly detection, ResAD-Net captures early residual distributional shifts before clear macroscopic deviations emerge and provides diagnostic association cues for interpreting oil-monitoring changes around the system-level alarm. Full article

The Annual Delivery Program (ADP) for Liquefied Natural Gas (LNG) represents a complex maritime inventory-routing problem that requires the precise synchronization of production and distribution. This study introduces a novel Mixed Integer Linear Programming (MILP) model designed to optimize vessel routing and scheduling over a one-year horizon under a direct-shipment assumption. The model minimizes total logistics costs, encompassing both fixed annual fleet costs and daily operating costs. The novelty of the model can be summarized in two aspects. First, it simultaneously optimizes several decisions: the assignment of frequency of deliveries to customers, the assignment of vessels to customers, cargo load sizes, and vessel routing and scheduling. The key distinction is that, unlike existing formulations that take the frequency of deliveries to customers as a fixed parameter, this frequency is itself a decision variable selected from a customer-specific discrete set; the selected frequency partitions the planning horizon into uniform windows and sets each delivery’s cargo load size to the exact demand accumulated over its window from daily demand data. Second, it incorporates several relaxations of selected variables and valid inequalities that enable us to solve the complex model for moderate size problems within a reasonable computational time using the exact optimization approach. Using this novel model, we carried out extensive numerical analysis based on cost and operational parameter scenarios and developed important insights for the characteristics of a solution to the problem. Full article

This study presents the results from a comparison of measurement quantification methods of methane emissions from two onshore liquefied natural gas (LNG) export terminals, comparing site-level measurements, made using an in situ airborne technique, and estimates based on emission factors (EFs) derived from measurements using a remote sensing, ground-based, differential absorption LIDAR (DIAL) technique. The methane emissions from each site were quantified at an approximately one-year interval for each of the two techniques. DIAL was used to measure emissions at the sub-site, functional element (FE) level and calculate EFs for each FE using the specific FE activity data (AD). The total site methane emissions during the airborne measurements were estimated for each site using these EFs and the AD at the time. The results show the estimated methane emissions and the airborne measurements are close to agreement when considering the average of all the flight curtains (down to a 7% difference between uncertainty limits), whilst individual curtains were potentially significantly different. These results highlight the importance of fully characterising the methodology and uncertainty of both approaches. Using up-to-date, site-specific EFs or comparing over a statistically large sample size should improve agreement by reducing unknown emission uncertainties associated with site changes affecting the emission profile. Understanding each FE emission profile across a range of AD is critical to address potential differences due to non-linearity. It is important that accurate, specific and up-to-date AD is obtained to give a reliable estimate of emissions. The potential of the concept to estimate methane emissions from the FE EFs is demonstrated. Full article

Crackers and cookies have become the most widely consumed snacks due to their low production costs, long shelf life, and ability to deliver essential nutrients. Increasing consumer health consciousness has shifted preferences toward foods perceived as natural and beneficial. This shift elevates demand for cracker formulations with novel, health-promoting natural ingredients. This study examined the effects of incorporating freeze-dried olive leaf powder (FDOLP) into crackers on their physicochemical properties, phenolic and volatile compound profiles, antioxidant capacity, and sensory acceptability. Total polyphenol content of crackers was determined using the Folin–Ciocalteu method, while antioxidant capacity was evaluated by FRAP and DPPH assays. The UHPLC-ESI-HRMS analysis evaluated olive-derived compounds, including tyrosol, oleuropein derivatives, and pinoresinol 4-O-glucoside, present in olive leaf-enriched crackers. The characterisation of volatile compounds in crackers was performed using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). A darker colour was observed in the enriched crackers compared to the control samples. Results demonstrated that increasing the proportion of FDOLP led to enhanced phenolic composition and antioxidant activity, as well as changes in the volatile profile of the crackers. Sensory analyses indicated that crackers enriched with moderate levels of FDOLP maintained acceptable overall sensory scores, suggesting a potential for the development of functional snacks. These findings demonstrate that olive leaves can be effectively utilised as a natural functional ingredient in cracker formulations to enhance their nutritional value and bioactive properties. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

Northern Colombian Creole-Antillean avocado constitutes a promising agroindustrial resource because of its lipid-rich composition and regional availability. Despite this potential, the industrial exploitation of this biomass remains limited, particularly regarding the technical assessment of large-scale oil production systems. In this study, an avocado oil production process was evaluated through computer-aided simulation combined with the Water–Energy–Product (WEP) methodology to assess operational behavior, resource utilization, and process efficiency from an integrated technical perspective. The evaluated system achieved an overall production yield of 9.43%, mainly affected by the elevated raw material requirements associated with oil generation. Nevertheless, the extraction stage exhibited favorable technical performance, reaching an oil recovery efficiency of 81.42%. Concerning water management, the process required 26.85 m³/t of freshwater and generated wastewater equivalent to 96.05% of the total water consumed, revealing important limitations related to water integration and recirculation within the process configuration. From an energy perspective, the system presented a specific energy intensity of 19,929 MJ/t, with natural gas representing the predominant energy source throughout the operation. Overall, the obtained results demonstrate that the proposed process is technically viable for avocado oil production while also identifying critical opportunities for improving resource utilization, decreasing water demand, and enhancing the operational sustainability of the system. Full article