Search Results (846)

Efficient coalbed methane (CBM) recovery combined with carbon dioxide (CO₂) sequestration is a promising strategy for sustainable energy production and greenhouse gas mitigation. However, the molecular mechanisms controlling pressure-dependent CH₄ displacement by CO₂ in coal nanopores remain insufficiently understood. In this study, molecular dynamics simulations were conducted to investigate CO₂-driven CH₄ recovery in a slit-pore coal model under driving pressures of 15, 20, and 25 Mpa. The simulations quantitatively captured the competitive adsorption, diffusion, and migration behaviors of CH₄, CO₂, and water, providing insights into how pressure influences enhanced coalbed methane (ECBM) recovery at the nanoscale. The results show that as the pressure increases from 15 to 25 Mpa, the mean residence time of CH₄ on the coal surface decreases from 0.0104 ns to 0.0087 ns (a 16% reduction), reflecting accelerated molecular mobility. The CH₄–CO₂ radial distribution function peak height rises from 2.20 to 3.67, indicating strengthened competitive adsorption and interaction between the two gases. Correspondingly, the number of CO₂ molecules entering the CH₄ region grows from 214 to 268, demonstrating higher invasion efficiency at elevated pressures. These quantitative findings illustrate a clear shift from capillary-controlled desorption at low pressure to pressure-driven convection at higher pressures. The results provide molecular-level evidence for optimizing CO₂ injection pressure to improve CBM recovery efficiency and CO₂ storage capacity. Full article

Coalbed methane (CBM) development involves multiple interacting physical fields, and different coupling schemes can lead to distinctly different production behaviors. A thermo-hydro-mechanical model accounting for gas–water two-phase flow and matrix dynamic diffusion (TP-D-THM) is developed and validated, achieving an error rate below 10%. By embedding the numerically estimated reservoir physical parameters of the Qinshui Basin into the numerical model, multi-field couplings during CBM production, the evolution of physical parameters, and the depth-dependent effects on production characteristics were revealed. The main findings are as follows: The inhibitory effect of water on CBM recovery consistently exceeds the promoting effect of temperature. As burial depth expands, the inhibitory effect first diminishes, then intensifies, ranging from 19.73% to 28.41%, while the thermal promotion effect exhibits a monotonically increasing trend, fluctuating between 8.55% and 16.33% and stabilizing below 1000 m. Temperature and burial depth do not alter the trend in gas production rate. For equilibrium permeability, reproducing a decrease–increase–decrease rate pattern requires explicit inclusion of water and matrix-fracture mass exchange terms, which can explain why different scholars obtained varying gas production rate trends using the THM model. Matrix adsorption-induced strain is the primary control on permeability evolution, and temperature amplifies the magnitude of permeability change. The critical depth essentially reflects the statistical characteristics of reservoir petrophysical properties. A dimensionless critical depth criterion has been proposed, which comprehensively considers reservoir pressure, permeability, and a fractional coverage index. For burial depths ranging from 650 to 1350 m, the TP-D-THM model can be simplified to the gas-mechanical model accounts for matrix dynamic diffusion (D-HM) with an error below 5%, indicating that thermal and water effects nearly cancel each other. Full article

The evaluation of the fracturing effect of coalbed methane (CBM) wells is crucial for the efficient development of CBM reservoirs. Currently, studies focusing on the evaluation of the hydraulic fracture stimulation effect of coal seams and the integrated analysis of “drilling-fracturing-monitoring” are relatively insufficient. Therefore, this paper takes three drainage and production wells in the coalbed methane block on the northwest wing of the Xiangxia anticline in the Bijie Experimental Zone of Guizhou Province as the research objects. In view of the complex geological characteristics of this area, such as multiple and thin coal seams, high gas content, and high stress and low permeability, the paper systematically summarizes the results of drilling and fracturing engineering practices of the three drainage and production wells in the area, including the application of key technologies such as a two-stage wellbore structure and the “bentonite slurry + low-solid-phase polymer drilling fluid” system to ensure wellbore stability, low-solid-phase polymer drilling fluid for wellbore protection, and staged temporary plugging fracturing. On this basis, a study on microseismic signal acquisition and tomographic energy inversion based on a ground dense array was carried out, achieving four-dimensional dynamic imaging and quantitative interpretation of the fracturing fractures. The results show that the fracturing fractures of the three drainage and production wells all extend along the direction of the maximum horizontal principal stress, with azimuths concentrated between 88° and 91°, which is highly consistent with the results of the in situ stress calculation from the previous drilling engineering. The overall heterogeneity of the reservoir leads to the asymmetric distribution of fractures, with the transformation intensity on the east side generally higher than that on the west side, and the maximum stress deformation influence radius reaching 150 m. The overall transformation effect of each well is good, with the effective transformation volume ratio of fracturing all exceeding 75%, and most of the target coal seams are covered by the fracture network, significantly improving the fracture connectivity. From the perspective of the transformed planar area per unit fluid volume, although there are numerical differences among the three wells, they are all within the effective transformation range. This study shows that microseismic fracture monitoring technology can provide a key basis for the optimization of fracturing technology and the evaluation of the production increase effect, and offers a solution to the problem of evaluating the hydraulic fracture stimulation effect of coal seams. Full article

Elucidating the characteristics of methane adsorption in coal is essential for accurately assessing coalbed methane (CBM) potential. Methane adsorption is primarily governed by the compositional complexity of coal and its pore structure. Molecular simulation enables characterization of coal’s molecular composition at the microscopic level and facilitates the construction of nanoscale pore models. In this study, Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Photoelectron Spectroscopy (XPS) were used to characterize the molecular structure of coal. Pore models of various sizes were constructed in Materials Studio (MS) to simulate methane adsorption under different temperatures and pressures. To further clarify the influence of molecular structure, a reconstructed macromolecular model (RMM) was compared with a graphite model, revealing differences in methane adsorption behavior across varying pore sizes, temperatures, and pressures. The results show that absolute methane adsorption increases with pore size, while excess adsorption behavior is strongly associated with the adsorption layer. In the pore size range of 0.4 nm to 1.2 nm, excess adsorption increases due to spatial confinement, but decreases as pore size exceeds 1.2 nm. Structural differences between the RMM and graphite models also resulted in distinct temperature responses, with the graphite model underestimating methane adsorption capacity, highlighting the importance of realistic macromolecular representations in adsorption studies. Full article

Gas injection displacement technology plays a critical role in enhancing coalbed methane (CBM) and mine gas extraction efficiency. Numerical simulation is essential for revealing multi-field coupling mechanisms and optimizing process parameters, effectively addressing challenges such as high field test costs and limited laboratory scalability. This study systematically reviews progress in modeling physical fields (e.g., flow and diffusion), focusing on multi-physical field coupling mechanisms and permeability model evolution. It conducts iterative numerical model analysis—from basic flow–diffusion to fully coupled THMC models—compares simulation software (COMSOL shows greater coupling depth and compatibility than COMET3), and characterizes key mechanisms. By systematically reviewing the key advancements in the fields of numerical simulation in recent years (including important achievements such as the Buddenberg–Wilke equation and the improved Palmer–Mansoori model), a decision-making framework was proposed based on these achievements, covering “Multi-physical Field Coupling Equation Selection, Key Parameter Calibration, Permeability Equation Selection, Model Validation and Error Correction” simulation error ≤10% in heterogeneous coal seams. Although general-purpose tools enable high-precision multi-physics coupling, improvements are still needed in modeling flow–diffusion mechanisms, heterogeneity, and chemical field integration. This study provides a systematic methodological reference for the engineering application of gas injection displacement numerical simulation, and the framework constructed hereby can also be extended to shale hydraulic fracturing and other related fields. Full article

Automatically segmenting coal cracks in CT images is crucial for 3D reconstruction and the physical properties of mines. This paper proposes an automatic pixel-level deep learning method called Attention Double U²-Net to enhance the segmentation accuracy of coal cracks in CT images. Due to the lack of public datasets of coal CT images, a pixel-level labeled coal crack dataset is first established through industrial CT scanning experiments and post-processing. Then, the proposed method utilizes a Double Residual U-Block structure (DRSU) based on U²-Net to improve feature extraction and fusion capabilities. Moreover, an attention mechanism module is proposed, which is called Atrous Asymmetric Fusion Non-Local Block (AAFNB). The AAFNB module is based on the idea of Asymmetric Non-Local, which enables the collection of global information to enhance the segmentation results. Compared with previous state-of-the-art models, the proposed Attention Double U²-Net method exhibits better performance over the coal crack CT image dataset in various evaluation metrics such as PA, mPA, MIoU, IoU, Precision, Recall, and Dice scores. The crack segmentation results obtained from this method are more accurate and efficient, which provides experimental data and theoretical support to the field of CBM exploration and damage of coal. Full article

Coalbed-methane (CBM) extraction involves complex processes such as desorption, diffusion, and seepage, significantly increasing the difficulty of numerical simulation. To enable efficient CBM development, this study establishes an integrated simulation workflow for CBM, encompassing geological modeling, geomechanical modeling, hydraulic fracture simulation, and production dynamic simulation. Specifically, the unconventional fracture model (UFM), integrated within the Petrel commercial software, is applied for fracture simulation, with an unstructured grid constructing the CBM production model. Subsequently, based on the case study of well pad A in the Daning–Jixian block, the effects of well spacing and hydraulic fractures on gas production were analyzed. The results indicate that the significant stress difference between the coal seam and the top/bottom strata constrains fracture height, with simulated hydraulic fractures ranging from 169.79 to 215.84 m in length, 8.91 to 10.45 m in height, and 121.92 to 248.71 mD·m in conductivity. Due to the low matrix permeability, pressure drop and desorption primarily occur in the stimulated reservoir volume (SRV) region. The calibrated model predicts a 10-year cumulative gas production of 616 × 10⁴ m³ for the well group, with a recovery rate of 10.17%, indicating significant potential for enhancing recovery rates. Maximum cumulative gas production occurs when well spacing slightly exceeds fracture length. Beyond 200 mD·m, fracture conductivity has diminishing returns on production. Fracture length increases from 100 to 250 m show near-linear growth in production, but further increases yield smaller gains. These findings provide valuable insights for evaluating development performance and exploiting remaining gas resources for CBM. Full article

Unexpected failures of avionics equipment critically affect flight safety, operational availability, and maintenance costs. To address these issues, Condition-Based Maintenance Plus (CBM+) has emerged as a strategy to optimize maintenance timing based on equipment condition rather than fixed schedules. However, while aviation research has largely focused on engines and structures, studies on avionics systems remain limited, often relying on simulations. This study proposes a novel data-driven approach to predict avionics equipment failures using actual aircraft operational data. Maneuver-related sequences were analyzed to investigate correlations between flight patterns and equipment faults, and a two-stage framework was developed. In the feature extraction stage, a CNN-LSTM encoder compresses 10 s maneuver sequences into compact yet informative representations. In the fault prediction stage, AI models classify failures of the Fire Control Radar based on these features. Experiments with real flight data validated the effectiveness of the method, showing that the CNN-LSTM encoder preserved essential maneuver information, while the combination of Standard Scaling and Multi-Layer Perceptron achieved the best performance, with a maximum Fault Recall of 98%. These findings demonstrate the feasibility of practical CBM+ implementation for avionics equipment using only flight data, providing a promising solution to improve maintenance efficiency and aviation safety. Full article

Condition-Based Maintenance Plus (CBM+) has been widely adopted in aerospace and mechanical systems, but its application to single-board computers (SBCs) remains difficult due to scarce failure data and subtle degradation signatures. This study investigates CBM+ for the MVME6100 SBC using accelerated life testing (ALT) to generate degradation trajectories and capture precursor signals. Temperature–humidity cycling and vibration tests were performed, while CPU temperature, memory temperature, and output voltage were continuously monitored. Under stable operation, signals followed ambient variations and showed little statistical drift, making degradation visually indistinguishable. However, precursors emerged before failure: CPU temperature exhibited abnormal behavior during thermal cycling, while vibration stress induced communication noise and irregular thermal behavior. These findings indicate that thermal responses provide reliable precursors for electronic degradation. To evaluate data-driven detection, two neural approaches were applied: an Autoencoder (AE) trained only on normal data and a Long Short-Term Memory (LSTM) network trained on both normal and faulty datasets. The Autoencoder reliably detected anomalies via reconstruction error, while the LSTM accurately classified health states and reproduced lifecycle progression. Together, the results demonstrate that precursor-informed CBM+ is feasible for SBCs and that a hybrid AE–LSTM framework enhances prognostics and health management in mission-critical electronics. Full article

The concept of digital twins has been studied for over two decades and the core tenet lies in it being a “digital representation of a connected physical object”. Utilization of digital twins promises to enable superior decision-making, enhanced operational understanding and future predictions to enable levels of Condition Based Maintenance (CBM) through Integrated Vehicle Health Management (IVHM) which exceeds existing capabilities. Digital twins are being embraced by many industries, including aviation, and are often depicted as electronic images of an asset of interest. However, in a less visually appealing manner, they can also be described simply as a collection of data in an organized and easily accessible format from across the lifecycle which describes a feature that addresses a specific use case. This review demonstrates how the creation and maintenance of digital twins will play a critical role in enhancing IVHM to enable CBM within the aerospace industry. Through a literature review, this paper demonstrates the need for digital twins, of a sufficient level of fidelity, to facilitate the transition to being condition based through deeper levels of operational and component understanding. It emphasizes how detailed knowledge, represented through ontologies, regarding component design, manufacturing, and operational history aid in achieving the desired fidelity levels. By synthesizing insights from various industries with a focus on aerospace applications, this paper aims to provide a comprehensive understanding, focused on the aviation industry, of digital twin definitions, their creation processes, fidelity measurement, and their implications for CBM, while acknowledging the limitations of the current research landscape. Full article

One of the main goals of the Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) is to investigate the properties of strongly interacting matter under high baryon densities and explore the QCD phase diagram. Fluctuations of conserved quantities like baryon number, electric charge, and strangeness are key probes for phase transitions and critical behavior, as are connected to thermodynamic susceptibilities predicted by lattice QCD calculations. In this paper, we report on up-to-the-fourth-order cumulants of (net-)proton number distributions in gold–gold ion collisions at the nucleon–nucleon center of mass energies $\sqrt{s_{NN}}$ = 3.5–19.6 GeV using the Parton–Hadron-Quantum-Molecular Dynamics (PHQMD) model. Protons and anti-protons are selected at midrapidity ($\left|y\right|$ < 0.5) within a transverse momentum range 0.4 $<p_T<$ 2.0 GeV/c of STAR experiment and 1.08 $<y<$ 2.08 and 0.4 $<p_T<$ 2.0 GeV/c of CBM acceptances. The results obtained from the PHQMD model are compared with the existing experimental data to undersatand potential signatures of critical behavior and to probe the vicinity of the critical end point in the CBM energy range. The results obtained here with the PHQMD calculations for $\kappa {\sigma }^2$ (the distribution kurtosis times variance squared) are consistent with the overall trend of the measurement results for the most central (0–5% centrality) collisions, although the calculations somewhat overestimate the experimental values. Full article

Dents are a common geometric deformation defect in pipelines where the dented section becomes susceptible to local buckling, significantly threatening the integrity and reliability of the pipeline. This paper developed a novel finite element (FE) machine learning (ML)-based approach to analyze and predict the critical buckling moment (CBM) of dented pipelines under combined internal pressure and bending moment (BM) loading. By quantifying the parametric effects on CBM and developing a dataset, an Extreme Learning Machine (ELM) framework through hybrid algorithm integration, combining Bald Eagle Search (BES), Lévy flight, and Simulated Annealing (SA), was proposed to achieve highly accurate CBM predictions. This study offers valuable insights into evaluating the buckling resistance of dented pipelines subjected to complex loading conditions. Full article

Coalbed methane (CBM) reservoirs in southwestern China are characterized by thick, multi-layered coal sequences partitioned into several independent pressure systems by impermeable strata. Commingled production from multiple coal seams in such multi-superposed CBM systems often suffers from severe inter-layer interference, leading to suboptimal gas recovery. To address this challenge, we developed a systematic four-step optimization workflow integrating geological data screening, pressure compartmentalization analysis, and numerical reservoir simulation. The workflow identifies the key “main” coal seams and evaluates various co-production layer combinations to maximize gas recovery while minimizing negative interference. We applied this method to a CBM well (LC-C2) in the Western Guizhou–Eastern Yunnan region, which penetrates three discrete CBM pressure systems. In the case study, single-layer simulations first revealed that one seam (No. 7 + 8) contributed over 30% of the total gas potential, with a few other seams (e.g., No. 18, 13, 4, 16) providing moderate contributions and many seams yielding negligible gas. Guided by these results, we simulated five commingling scenarios of increasing complexity. The optimal scenario was to co-produce the seams from the two higher-pressure systems (a total of six seams) while excluding the low-pressure shallow seams. This optimal six-seam configuration achieved a 10-year cumulative gas production of approximately 2.53 × 10⁶ m³ (about 700 m³/day average)—roughly 75% higher than producing the main seam alone, and even about 15% greater than a scenario involving all available seams. In contrast, including all three pressure systems (ten seams) led to interference effects where the high-pressure seams dominated flow and the low-pressure seams contributed little, resulting in lower overall recovery. The findings demonstrate that more is not always better in multi-seam CBM production. By intelligently selecting a moderate number of compatible seams for co-production, the reservoir’s gas can be extracted more efficiently. The proposed quantitative optimization approach provides a practical tool for designing multi-seam CBM wells and can be broadly applied to similar geologically compartmentalized reservoirs. Full article

The transition from linear to circular systems remains slow and fragmented, despite the increasing recognition of circular economy (CE) as a strategic pathway to sustainability. This review identifies and categorizes the main policy levers supporting the adoption of Circular Business Models (CBM) in an analytical framework comprising eight determinants: policy agenda, governance, regulation, standardization, economic incentives, information, cooperation, and digitalization. Based on a semi-systematic review of 95 scientific and grey literature sources, the study reveals persistent gaps in policy coherence, governance coordination, and support for high-circularity strategies. The proposed framework offers a practical tool for policymakers to assess existing policy landscapes, identify gaps, and design integrated policy mixes tailored to specific contexts. It also provides a foundation for future empirical research and benchmarking across jurisdictions. By highlighting the interplay between top-down and bottom-up initiatives, the study underscores the need for inclusive, stable, and digitally enabled policy environments to accelerate the circular transition. Full article

ROGDI-related neurodevelopmental and dental disorder (ROGDI-RD), also known as Kohlschütter–Tönz syndrome (KTZS, MIM #226750), is a rare condition characterized by developmental abnormalities affecting both the central nervous system (CNS) and the dentition. These phenotypes highlight the role of complex gene–environment interactions and developmental networks shared by the nervous and stomatognathic systems, both of which originate mostly from neural crest-derived cells. In this review, we analyze clinical and genetic data from 54 previously reported ROGDI-RD patients to better define the phenotypic spectrum of the disorder. Most of the reported cases harbor protein-truncating variants. Here, we also present the first description of a patient carrying a missense variant in ROGDI atypical leucine zipper gene, ROGDI in trans to a frameshift variant. This individual presented with tooth agenesis—a dental anomaly not previously associated with the syndrome—alongside classic neurological and dental enamel features, suggesting that the phenotypic spectrum of ROGDI-RD may be broader than currently recognized. Using a complexity and network science framework, we discuss how dysregulation in multilevel, interacting developmental systems may explain the pleiotropic features of ROGDI-RD. Our findings underscore the importance of early, interdisciplinary clinical evaluation in patients with neurodevelopmental symptoms and enamel defects. As enamel phenotypes such as amelogenesis imperfecta are heterogeneous, comprehensive genomic analyses and collaborative clinical approaches are essential for accurate diagnosis and improved care. Full article