Search Results (93)

In order to solve the problems of low calorific value and pipeline corrosion caused by high concentration of CO₂ in oil-associated gas, and promote the resource utilization of associated gas, this study used validated grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulation to investigate the adsorption characteristics of 11 different topological structures (straight-channel MFI/BEA, cage-channel LTA/FAU/CHA) and cation types (Ca²⁺, Na⁺, H⁺) of commercial zeolites for CO₂ and alkanes (CH₄, C₂H₆, C₃H₈) at 0%~90% RH. The results showed that the CO₂ adsorption capacity of all zeolites decreased with increasing humidity, but straight-channel zeolites (ZSM5-300, BETA-25) had excellent moisture resistance, with only a 20.8% and 30.6% decrease in capacity at 90% RH, respectively. The performance of cage-channel zeolite drops sharply under high humidity. Topology structure and cation synergistically regulate separation efficiency, maintaining stable diffusion order in straight channels. Ca²⁺ enhances dry state capacity but is prone to hydrophilic failure. The adsorption heat of CO₂ on straight-channel zeolite is 25–38 kJ/mol, resulting in lower regeneration energy consumption. ZSM5-300 is preferred for PSA (CH₄/CO₂ kinetic separation coefficient of 809.52 at 90% RH), and NaFAU is preferred for TSA (CO₂ adsorption capacity of 3.6 mmol/g and selectivity of 502.6 at 90% RH). This study clarifies the core structure-activity relationship and provides key theoretical support for the decarbonization of oil-associated gas. Full article

Accurate quantification of methane (CH₄) emissions from individual point sources is essential for understanding localized greenhouse gas dynamics and supporting mitigation strategies. This study employs satellite-based point-source emission rate data from the Carbon Mapper initiative, combined with ERA5 meteorological reanalysis, to simulate near-surface CH₄ dispersion using a Gaussian plume model coupled with Monte Carlo simulations. This approach captures local dispersion characteristics around each emission source. Simulations driven by these emission inputs reveal a highly skewed, heavy-tailed concentration distribution (consistent with log-normal characteristics), where the 95th percentile (1292.1 ppm) significantly exceeds the mean (475.9 ppm), indicating the dominant influence of a small number of super-emitters. Sectoral analysis shows that coal mining contributes the most high-emission sites, while the solid waste and oil & gas sectors present higher per-source intensities, averaging 1931.1 ppm and 1647.6 ppm, respectively. Spatially, emissions are concentrated in North and Northwest China, particularly Shanxi Province, which hosts 62 high-emission sites with an average maximum of 1583.9 ppm. Sensitivity analysis reveals that emission rate perturbations produce nearly linear responses in concentration, whereas wind speed variations induce an inverse and asymmetric nonlinear response, with sensitivity amplified under low wind speed conditions (a ±30% change in wind speed results in more than ±25% variation in concentration). Under stable atmospheric conditions (Class E), concentrations are approximately 1.3 times higher than those under weakly unstable conditions (Class C). Monte Carlo simulations further indicate that output uncertainty peaks within 150–300 m downwind of emission sources. These results provide a quantitative basis for improving uncertainty characterization in satellite-based methane inversion and for prioritizing risk-based monitoring strategies. Full article

The multi-energy flow coordination optimization of the self-sufficient hydrogen energy park is becoming a research focus. However, without explicit consideration of tank car, the optimization remains incomplete, thereby undermining practical applicability. In this paper, a Dynamic Adaptive Grey Wolf Optimization (DA-GWO) algorithm is proposed for self-consistent multi-energy flow coordination optimization, considering hydrogen energy-based tank cars in hydrogen railway energy parks. First, a foundational model of the hydrogen-based railway energy system was constructed that integrates green non-dispatchable units such as wind power and photovoltaics, as well as dispatchable units such as fuel cells, gas boilers, and cogeneration units. Given the diversity and complexity of in-service hydrogen railway tank cars, a probabilistic model of daily charging behaviour was constructed using a Monte Carlo method to simulate real-world operating conditions of tank cars, thereby enhancing the reliability of the hydrogen-powered railway model. Considering the diverse and complex units in the self-consistent hydrogen energy park for hydrogen-powered railways, a DA-GWO algorithm was constructed for the multi-energy flow optimization. Through a self-adaptive parameter adjustment, the algorithm’s global optimization performance is improved. Finally, the model parameters were further adjusted with data from a coastal Chinese city, and the optimization experimental tests were conducted to validate the proposed method. From the results, the proposed method can save at least 6.7% cost compared with the grey wolf optimization method and the PSO (Particle Swarm Optimization) optimization method. Full article

Accurate conjugate heat transfer (CHT) analysis is critical to the thermal management of hypersonic vehicles operating in rarefied environments, where non-equilibrium gas dynamics dominate. While numerous sophisticated CHT solvers exist for continuum flows, they are physically invalidated by rarefaction effects. This paper presents a novel partitioned coupling framework that bridges this methodological gap by utilizing the preCICE library to non-intrusively integrate the Direct Simulation Monte Carlo (DSMC) method, implemented in SPARTA, with the finite element method (FEM) via FEniCS for high-fidelity simulations of rarefied hypersonic CHT. The robustness and accuracy of this approach are validated through three test cases: a quasi-1D flat plate benchmark confirms the fundamental coupling mechanism against a reference finite difference solution; a 2D flat-nosed cylinder demonstrates the capability of the framework to handle highly non-uniform heat flux distributions and resolve the ensuing transient thermal response within the solid; finally, a standard cylinder case confirms the compatibility with curved geometries and its stability and accuracy in long-duration simulations. This work establishes a validated and accessible pathway for high-fidelity aerothermal analysis in rarefied gas dynamics, effectively decoupling the complexities of multi-physics implementation from the focus on fundamental physics. Full article

To enhance the accuracy and efficiency of reliability analyses for an aero-engine vectoring exhaust nozzle (VEN), a dual-stochastic extreme response surface method based on the genetic algorithm (DSERSM-GA) is developed by integrating the genetic algorithm, the random extremum response surface method, and the dual response surface method in the paper. In the proposed method, a limited set of Monte Carlo samples is strategically utilized to construct and optimize a population-based response surface model, forming a robust mathematical framework for reliability prediction. The uncertainty sources considered include aerodynamic loads acting on the vector nozzle, material densities of the expansion plate and triangular link, as well as the elastic moduli of these components. Stress and deformation responses of both the expansion plate and triangular link are employed as the performance metrics. The proposed DSERSM-GA methodology is validated through dynamic reliability simulations applied to a vector nozzle system, yielding distributions and corresponding reliability indices of critical responses. Comparative analyses against traditional Monte Carlo Simulation (MCS) and conventional Extreme Response Surface Methods (ERSM) demonstrate that the DSERSM-GA significantly reduces computational costs while preserving high predictive accuracy. Full article

Understanding the microscopic occurrence states of shale oil—particularly the distribution between adsorbed and free phases—is essential for optimizing the development of unconventional reservoirs. In this study, we propose an integrated methodology that combines experimental techniques with molecular dynamics simulations to investigate shale oil behavior within kerogen nanopores. Specifically, pyrolysis–gas chromatography–mass spectrometry (PY-GC-MS), solid-state ¹³C nuclear magnetic resonance (¹³C NMR), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) were performed to construct a representative kerogen molecular model based on shale samples from the Lianggaoshan Formation in the Sichuan Basin. Grand Canonical Monte Carlo (GCMC) simulations and a theoretical occurrence model were applied to quantify the adsorption characteristics of n-dodecane under varying pore sizes, temperatures, and pressure. The results show that temperature exerts a stronger influence than pore diameter on adsorption capacity, with adsorption decreasing by over 50% at higher temperatures, and pressure has a limited effect on the adsorption amount of dodecane molecules. This study offers a robust workflow for evaluating shale oil occurrence states in complex pore systems and provides guidance for thermal stimulation strategies in tight oil reservoirs. Full article

Islanded microgrids face significant frequency stability challenges due to limited system capacity, low inertia levels, and the strong variability in renewable energy sources. Traditional reliability assessment methods, often based on static power balance, struggle to comprehensively reflect frequency dynamic characteristics and their impact on power supply reliability. To address this issue, this paper proposes a sequential Monte Carlo reliability assessment method integrated with a system frequency response model. First, an SFR model for the isolated microgrid, incorporating diesel generators, gas turbines, energy storage, and wind turbines, is established. For synchronous units, a frequency deviation-based failure rate correction mechanism is introduced to characterize the impact of frequency fluctuations on equipment reliability. State transitions are achieved by integrating failure and repair rates to reach threshold values. Second, sequential Monte Carlo simulation is employed to conduct time-series simulations of annual operation. Random sampling of unit failure and repair times is used to calculate reliability metrics. MATLAB/Simulink simulation results demonstrate that system frequency fluctuations caused by power imbalance worsen unit failure rates, leading to microgrid reliability values lower than static calculations. This provides reference for planning, design, and operational scheduling of isolated microgrids. Full article

Fault slips induced by hydraulic fracturing are the primary mechanism of casing de-formation during deep shale gas development in Sichuan’s Luzhou Block, where de-formation rates reach 51% and severely compromise productivity. To address a critical gap in existing research on quantitative risk assessment systems, we developed a probabilistic model integrating pore pressure evolution dynamics with Monte Carlo simulations to quantify slip risks. The model incorporates key operational parameters (pumping pressure, rate, and duration) and geological factors (fault friction coefficient, strike/dip angles, and horizontal stress difference) validated through field data, showing >90% slip probability in 60% of deformed well intervals. The results demonstrate that prolonged high-intensity fracturing increases slip probability by 32% under 80–100 MPa pressure surges. Meanwhile, an increase in the friction coefficient from 0.40 to 0.80 reduces slip probability by 6.4% through elevated critical pore pressure. Fault geometry exhibits coupling effects: the risk of low-dip faults reaches its peak when strike parallels the maximum horizontal stress, whereas high-dip faults show a bimodal high-risk distribution at strike angles of 60–120°; here, the horizontal stress difference is directly proportional to the slip probability. We propose optimizing fracturing parameters, controlling operation duration, and avoiding high-risk fault geometries as mitigation strategies, providing a scientific foundation for enhancing the safety and efficiency of shale gas development. Full article

The Maxwell–Boltzmann (MB) distribution is important because it provides the statistical foundation for connecting microscopic particle motion to macroscopic gas properties by statistically describing molecular speeds and energies, making it essential for understanding and predicting the behavior of classical ideal gases. This study advances the statistical modeling of lifetime distributions by developing a comprehensive reliability analysis of the MB distribution under an improved adaptive progressive censoring framework. The proposed scheme strategically enhances experimental flexibility by dynamically adjusting censoring protocols, thereby preserving more information from test samples compared to conventional designs. Maximum likelihood estimation, interval estimation, and Bayesian inference are rigorously derived for the MB parameters, with asymptotic properties established to ensure methodological soundness. To address computational challenges, Markov chain Monte Carlo algorithms are employed for efficient Bayesian implementation. A detailed exploration of reliability measures—including hazard rate, mean residual life, and stress–strength models—demonstrates the MB distribution’s suitability for complex reliability settings. Extensive Monte Carlo simulations validate the efficiency and precision of the proposed inferential procedures, highlighting significant gains over traditional censoring approaches. Finally, the utility of the methodology is showcased through real-world applications to physics and engineering datasets, where the MB distribution coupled with such censoring yields superior predictive performance. This genuine examination is conducted through two datasets (including the failure times of aircraft windshields, capturing degradation under extreme environmental and operational stress, and mechanical component failure times) that represent recurrent challenges in industrial systems. This work contributes a unified statistical framework that broadens the applicability of the Maxwell–Boltzmann model in reliability contexts and provides practitioners with a powerful tool for decision making under censored data environments. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

This study presents a hybrid sensor placement methodology that combines criterion-based candidate selection with advanced optimization algorithms. Four established selection criteria—modal kinetic energy (MKE), modal strain energy (MSE), modal assurance criterion (MAC) sensitivity, and mutual information (MI)—are used to evaluate DOF sensitivity and generate candidate pools. These are followed by one of four optimization algorithms—greedy, genetic algorithm (GA), particle swarm optimization (PSO), or simulated annealing (SA)—to identify the optimal subset of sensor locations. A key feature of the proposed approach is the incorporation of constraint dynamics using the Udwadia–Kalaba (U–K) generalized inverse formulation, which enables the accurate expansion of structural responses from sparse sensor data. The framework assumes a noise-free environment during the initial sensor design phase, but robustness is verified through extensive Monte Carlo simulations under multiple noise levels in a numerical experiment. This combined methodology offers an effective and flexible solution for data-driven sensor deployment in structural health monitoring. To clarify the rationale for using the Udwadia–Kalaba (U–K) generalized inverse, we note that unlike conventional pseudo-inverses, the U–K method incorporates physical constraints derived from partial mode shapes. This allows a more accurate and physically consistent reconstruction of unmeasured responses, particularly under sparse sensing. To clarify the benefit of using the U–K generalized inverse over conventional pseudo-inverses, we emphasize that the U–K method allows the incorporation of physical constraints derived from partial mode shapes directly into the reconstruction process. This leads to a constrained dynamic solution that not only reflects the known structural behavior but also improves numerical conditioning, particularly in underdetermined or ill-posed cases. Unlike conventional Moore–Penrose pseudo-inverses, which yield purely algebraic solutions without physical insight, the U–K formulation ensures that reconstructed responses adhere to dynamic compatibility, thereby reducing artifacts caused by sparse measurements or noise. Compared to unconstrained least-squares solutions, the U–K approach improves stability and interpretability in practical SHM scenarios. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

To facilitate the local recycling of coal mine waste gas and investigate multi-component gas adsorption under high pressure conditions, this study develops a coal nanopore model using molecular dynamics (MD) and grand canonical Monte Carlo (GCMC) methods and simulates the adsorption behavior of coal mine waste gas components (CO₂, H₂O, N₂) under varying pressure levels and gas molar ratios at 353.15 K. We evaluated the adsorption capacity and selectivity for both single-component and multi-component gases, quantifying adsorption interactions through adsorption heat, interaction energy, and energy distribution. The simulation results revealed that the contribution of the three gases to the total adsorption amount followed the order: H₂O > CO₂ > N₂. The selective adsorption coefficient of a gas exhibits an inverse correlation with its molar volume ratio. Isothermal heat adsorption of gases in coal was positive, decreasing sharply with increasing pressure before leveling off. Electrostatic interactions dominated CO₂ and H₂O adsorption, while van der Waals forces governed N₂ adsorption. As the gas mixture complexity increased, the overlap of energy distribution curves pronounced, highlighting competitive adsorption behavior. These findings offer a theoretical foundation for optimizing coal mine waste gas treatment and CO₂ sequestration technologies. Full article

Chemiresistive gas sensors have gained significant attention and have been widely applied in various fields. However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. Herein, we present a comprehensive overview of the modeling approaches for chemiresistive gas sensors, focusing on two critical processes: the reception and transduction stages. For the reception process, density functional theory (DFT), molecular dynamics (MD), ab initio molecular dynamics (AIMD), and Monte Carlo (MC) methods were analyzed. DFT quantifies atomic-scale charge transfer, and orbital hybridization, MD/AIMD captures dynamic adsorption kinetics, and MC simulates equilibrium/non-equilibrium behaviors based on statistical mechanics principles. For the transduction process, band-bending-based theoretical models and power-law models elucidate the resistance modulation mechanisms, although their generalizability remains limited. Notably, the finite element method (FEM) has emerged as a powerful tool for full-process modeling by integrating gas diffusion, adsorption, and electronic responses into a unified framework. Future directions highlight the use of multiscale models to bridge microscopic interactions with macroscopic behaviors and the integration of machine learning, accelerating the iterative design of next-generation sensors with superior performance. Full article

More accurate prediction of CO₂/CH₄ adsorption selectivity coefficients in the CO₂ Enhanced Coal Bed CH₄ Recovery (CO₂-ECBM) project can help to judge the CO₂ adsorption concentration and the desorption purity of CH₄ during the CO₂ injection process, and to achieve the maximization of CO₂ sequestration as well as the optimization of the CH₄ recovery rate. To this end, a coal molecular slit model with 16 sizes including micro-, meso-, and macropores was constructed in this study, and the competitive adsorption characteristics of CO₂ and CH₄ gas mixtures in bituminous coal molecules were investigated using molecular dynamics and giant canonical Monte Carlo simulations. The CO₂/CH₄ adsorption selectivity coefficients (S_c) as a function of gas ratio, gas pressure, pore size, and temperature were analyzed using a large amount of adsorption isotherm data. Based on the simulation results, considering the neglect of pressure and component changes when calculating the adsorption selectivity coefficient using the traditional extended Langmuir (E-L) model, a correction term regarding the pressure of the mixed gas and the mole fraction of CO₂ is set, and a modified equation is proposed. The results show that the adsorption potential energy of CO₂ is significantly higher than that of CH₄, giving it an absolute advantage in the competition. Through multiple regression analysis, the ranking of the influence weights of the four factors on S_c is as follows: pore size > mixed gas pressure > molar fraction of CO₂ > temperature. The negative exponential function can describe the variation of S_c with four factors. The fitting degree between the modified prediction model and the S_c data obtained through simulation reaches 0.84, and the model effect is good. The research results provide theoretical guidance for the optimization of gas injection parameters in the CO₂-ECBM project. Full article