Search Results (1,641)

The operational risks of equipment in coalbed methane (CBM) gathering stations exhibit dynamic characteristics. To address this, a dynamic risk assessment method based on Dynamic Bayesian Networks (DBNs) is proposed for CBM station equipment. Additionally, a comprehensive safety management evaluation model is established for gathering station equipment. This approach enables accurate risk assessment and effective implementation of safety management in CBM gathering stations. This method primarily consists of three core components: risk factor identification, dynamic risk analysis, and comprehensive safety management evaluation. First, the Bow-tie model is applied to comprehensively identify risk factors associated with station equipment. Next, a DBN is constructed based on the identified risks, and Markov theory is employed to determine the state transition matrix. Finally, a Comprehensive Safety Management (CSM) evaluation model for gathering station equipment is established. The feasibility of the proposed method is validated through case study applications. The results indicate that during the operation of equipment at CBM gathering stations, priority should be given to strengthening maintenance for medium-hole and enhancing prevention and emergency measures for jet fires. Temperature-controlled spiral-wound heat exchangers, skid-mounted circulating pumps, and pipelines have been identified as critical factors affecting accident occurrence at CBM gathering stations. Enhanced daily inspection and maintenance of this equipment should be implemented. Furthermore, compared to other safety evaluation indicators, the Emergency Preparedness and Response indicator has the most significant impact on the operational safety of CBM gathering station equipment. It requires high-priority attention, thorough implementation of relevant measures, and continuous improvement through targeted actions. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

This study presents the development and experimental validation of a dynamic simulation model for a transcritical CO₂ heat pump system coupled with a stratified water tank, with particular focus on strong transient behavior and detailed heat exchanger characteristics. Due to the unique thermophysical properties of CO₂ under transcritical conditions, conventional modeling approaches are insufficient. The model was validated against experimental results under a range of operating conditions. It accurately predicted outlet water temperatures within ±3.2 °C and system COP within ±6.8% deviation from measurements. In contrast to previous models, this approach offers improved accuracy in capturing dynamic system responses, including startup transients, and demonstrates high adaptability across varying ambient temperatures and load profiles. Importantly, the model also considers the vertical installation layout of components, enabling analysis of gravitational effects on system dynamics and offering insights into optimal configuration strategies. The validated model serves as a powerful tool for system optimization and advanced control design in residential CO₂ heat pump applications. Full article

Although electro-hydrostatic actuators (EHAs) hold broad application prospects in more-electric aircraft and high-end equipment, they face a difficult trade-off between dynamic response and energy efficiency. To simultaneously enhance the dynamic response and energy efficiency of the EHA, this paper designs an innovative variable pump displacement and variable motor speed (VPVM) configuration that utilizes an electro-hydraulic servo valve for active displacement control. To address the flow mismatch problem associated with traditional asymmetric single-rod cylinders without reducing the power density of EHA, this paper also designs an innovative symmetric single-rod cylinder configuration. Based on the above two innovative configurations, this paper further develops a corresponding EHA prototype with a rated power density of 0.72 kW/kg. Simulation and experimental results demonstrate that compared to the traditional EHA with the fixed pump displacement and variable motor speed configuration (FPVM-EHA), the EHA with the proposed VPVM configuration (VPVM-EHA) not only improves energy efficiency and reduces motor heat generation under low-speed and heavy-load conditions, but also achieves a dynamic response close to that of the FPVM-EHA under fast dynamic response conditions. Full article

We report high-resolution, cavity-enhanced direct frequency comb Fourier transform spectroscopy of cold acetylene (C₂H₂) molecules in a planar supersonic jet expansion. The experiment is based on a near-infrared frequency comb with a 300 MHz effective repetition rate, matched to a high-finesse enhancement cavity traversing the jet. The rotational and translational cooling of acetylene was achieved via expansion in argon carrier gas through a slit nozzle. By interleaving successive mode-resolved spectra measured at different comb repetition rates, we retrieved full absorption line profiles. Spectroscopic analysis reveals sharp, Doppler-limited transitions corresponding to a jet core rotational temperature below 7 K. Frequency comb and cavity stabilization were achieved through active Pound–Drever–Hall locking and mechanical vibration damping, enabling a spectral precision better than 2 MHz, limited by the vibrations induced by the pumping system. The demonstrated sensitivity reaches a minimum detectable absorption of 7.8 × 10⁻⁷ cm⁻¹ over an 18 m effective path length in the jet core. This work illustrates the potential of cavity-enhanced direct frequency comb spectroscopy for precise spectroscopic characterization of cold supersonic expansions, with implications for studies in molecular dynamics, reaction kinetics, and laboratory astrophysics. Full article

Abnormal shutdown detection in oilfield pumping units presents significant challenges, including degraded image quality under low-light conditions, difficulty in detecting small or obscured targets, and limited capabilities for dynamic state perception. Previous approaches, such as traditional visual inspection and conventional image processing, often struggle with these limitations. To address these challenges, this study proposes an intelligent method integrating multi-scale feature enhancement and low-light image optimization. Specifically, a lightweight low-light enhancement framework is developed based on the Zero-DCE algorithm, improving the deep curve estimation network (DCE-Net) and non-reference loss functions through training on oilfield multi-exposure datasets. This significantly enhances brightness and detail retention in complex lighting conditions. The DAFE-Net detection model incorporates a four-level feature pyramid (P3–P6), channel-spatial attention mechanisms (CBAM), and Focal-EIoU loss to improve localization of small/occluded targets. Inter-frame difference algorithms further analyze motion states for robust “pump-off” determination. Experimental results on 5000 annotated images show the DAFE-Net achieves 93.9% mAP@50%, 96.5% recall, and 35 ms inference time, outperforming YOLOv11 and Faster R-CNN. Field tests confirm 93.9% accuracy under extreme conditions (e.g., strong illumination fluctuations and dust occlusion), demonstrating the method’s effectiveness in enabling intelligent monitoring across seven operational areas in the Changqing Oilfield while offering a scalable solution for real-time dynamic anomaly detection in industrial equipment monitoring. Full article

The filtration behaviors of dynamic membrane (DM) under gravity-driven and pump-driven modes were investigated in a pilot-scale DM bioreactor (DMBR) for domestic wastewater treatment. After DM formation, both modes achieved effective solid–liquid separation, producing effluent with turbidity below 1 NTU, with the gravity-driven module exhibiting marginally lower turbidity than the pump-driven system. Although the flux in the gravity-driven mode (30–48 L/m²·h) was approximately half that of the pump-driven mode, the transmembrane pressure (TMP) required was only 10–20% of that under the pump-driven operation. The DM formed under pump-driven conditions was thicker and more compact, leading to more frequent and rapid TMP increases. Inorganic content accounted for 85% of the pump-driven DM mass, significantly higher than that in the gravity-driven DM (50%) and activated sludge (15%), indicating a pronounced accumulation of inorganic solids on the mesh filter surface, particularly under the pump-driven operation. This accumulation increased filtration resistance and elevated TMP. Therefore, enhancing the removal of inorganic solids prior to the DMBR can improve system stability and facilitate broader application of the DMBR technology. Full article

As critical industrial equipment, the operational stability of a centrifugal pump is profoundly affected by hydraulic radial forces acting on the impeller. However, existing research has limitations in systematically characterizing time-varying force patterns, elucidating the correlations between fluid–structure interaction (FSI) and vibration and noise, and developing multi-operating condition analysis methodologies. This study focuses on a horizontal end-suction centrifugal pump, integrating computational fluid dynamics (CFD) simulations to develop a transient radial force dataset covering nine operating conditions ranging from 0.4 Q_n to 1.2 Q_n. Feature engineering was utilized to extract 23 time-frequency domain features. Through Pearson correlation analysis and agglomerative hierarchical clustering (AHC) algorithms, multi-operating condition classification patterns of hydraulic radial forces were unveiled. Key findings include: (1) the X/Y directional force components exhibit distinct anisotropic correlations with the flow rate; (2) hierarchical clustering based on cosine distance and average linkage divides operating conditions into low, medium, and high flow regimes; (3) feature redundancy elimination requires balancing statistical metrics with physical interpretability. This work proposes an unsupervised learning framework, offering a data-driven approach for the hydraulic optimization of centrifugal pumps and intelligent diagnostics, with engineering significance for improving equipment reliability and operational efficiency. Full article

Water hammer is a critical transient phenomenon in pumping systems, occurring when a sudden change in flow velocity generates pressure waves propagating along the pipeline. This study focuses on the dynamic response of a long rising pipeline subjected to an emergency pump shutdown, with particular emphasis on the sudden release and propagation of hydraulic energy in the form of pressure waves. Such scenarios are typical for mine dewatering and water supply systems with high elevation differences. Two numerical approaches were investigated: the Method of Characteristics (MOC) implemented in TSNet as a reference model, and the Train Analogy Method (PKP) implemented in MATLAB R2024b/Simulink, where the fluid is represented as discrete masses connected by elastic links, enabling the inclusion of pump and motor dynamics. Simulations were performed for two configurations: first–with a check valve installed only at the pump discharge and second–with a check valve at the pump discharge and in the middle of the pipeline. The results demonstrate that both models capture the essential features of water hammer: a sharp initial pressure drop, the formation of transient waves, and pressure oscillations with decreasing amplitude. These oscillations reflect the propagation and gradual dissipation of hydraulic energy stored in the moving fluid, primarily due to frictional and elastic effects within the pipeline. The presence of a check valve accelerates the attenuation of oscillations, effectively reducing the impact of returning waves on the downstream pipeline. The novelty of this study lies in the use of the PKP method to simulate transient flow and energy exchange in long rising pipelines with dynamic pump behavior. The method offers a physically intuitive and modular approach that enables the modelling of local flow phenomena, pressure wave propagation, and system components such as pump–motor inertia and check valves. This makes PKP a valuable tool for investigating complex water hammer scenarios, as it enables the analysis of pressure wave propagation and damping, providing insight into the scale and evolution of energy released during sudden operational incidents, such as an emergency pump shutdown. The close agreement between the PKP and MOC results confirms that the PKP method implemented in Simulink is a reliable tool for predicting transient pressure behavior in hydraulic installations and supports its use for further validation and dynamic system analysis. Full article

In this study, we demonstrate the generation and observation of noise-like pulses (NLPs) with unique pulsating characteristics in an ultrafast fiber laser. Furthermore, these NLPs display distinct periodic intensity modulation during temporal evolution, and the corresponding shot-to-shot spectra based on the dispersive Fourier transform (DFT) method exhibit chaotic characteristics with random variable envelopes in each period. Notably, the pulsating period of these NLPs decreases with the increment of pump power. Moreover, both the average output power and pulse energy show a clear linear growth trend as the pump power is raised. Numerical simulations are further conducted to validate these experimental findings. This work will enrich the study of NLPs and pulsation dynamics and provide valuable insights for the development of ultrafast fiber lasers. Full article

The Sarco Endoplasmic Reticulum Ca²⁺-ATPase (SERCA) pumps cytosolic Ca²⁺ into the endoplasmic reticulum lumen (ER) to maintain cytosolic and ER Ca²⁺ levels under physiological conditions. Previous reports suggest that cellular Ca²⁺ homeostasis is compromised in Alzheimer’s Disease (AD) and that SERCA activity can modulate the phenotype of AD mouse models. Here, we used a C. elegans strain that overexpresses the most toxic human ß-amyloid peptide (Aß_(1-42)) in body-wall muscle cells to study the effects of SERCA (sca-1) silencing on Aß_(1-42)-induced body-wall muscle dysfunction. sca-1 knockdown reduced the percentage of paralyzed worms, improved locomotion in free-mobility assays, and restored pharynx pumping in Aß_(1-42)-overexpressing worms. At the cellular level, sca-1 silencing partially prevented Aß_(1-42)-induced exacerbated mitochondrial respiration and mitochondrial ROS production and restored mitochondrial organization around sarcomeres. sca-1 knockdown reduced the number and size of Aß_(1-42) aggregates in body–wall muscle cells and prevented the formation of Aß_(1-42) oligomers. Aß_(1-42) expression induced a slower kinetics of spontaneous cytosolic Ca²⁺ transients in muscle cells and sca-1 partially restored these changes. We propose that partial sca-1 loss of function prevents the toxicity associated with beta-amyloid accumulation by reducing the formation of Aß_(1-42) oligomers and improving mitochondrial function, in a mechanism that requires remodeling of cytosolic Ca²⁺ dynamics and partial ER Ca²⁺ depletion. Full article

We explored the pharmacology of the P-glycoprotein (P-gp) efflux pump and its role in multidrug resistance. We used Protein Data Bank (PDB) database mining and the artificial intelligence (AI) model Boltz-2.1.1, developed for simultaneous structure and affinity prediction, to explore the multimeric nature of recent P-gp inhibitors. We construct a MARTINI coarse-grained (CG) force field description of P-gp embedded in a model of the endothelial blood–brain barrier. We found that recent P-gp inhibitors have been captured in either monomeric, dimeric, or trimeric states. Our CG model demonstrates the ability of P-gp substrates to permeate and transition across the BBB bilayer. We report a multimodal binding model of P-gp inhibition in which later generations of inhibitors are found in dimeric and trimeric states. We report analyses of P-gp substrates that point to an extended binding surface that explains how P-gp can bind over 300 substrates non-selectively. Our coarse-grained model of substrate permeation into membranes expressing P-gp shows benchmarking similarities to prior atomistic models and provide new insights on far longer timescales. Full article

In this study, a sensorless speed control method is proposed to enhance the speed control performance under load variations by utilizing a discrete-time flux observer in a V/f control environment. Due to their simple structure, low cost, and high reliability, induction motors are widely used in various fields, such as fans, pumps, and home appliances. Among the control methods for induction motors, V/f control operates as an open-loop system, without using speed sensors. It is mainly applied in industrial environments where fast dynamic performance is not required, due to its simple implementation and low cost. However, in cases of load variations or low-speed operation, it suffers from performance degradation and control limitations due to flux variations. To overcome these issues, this paper proposes a method that uses a discrete-time flux observer to estimate the stator flux. We calculate the rotor speed based on the estimated flux, and then improve V/f control performance by adding a compensation signal to the reference frequency, which signal is generated through a PI controller based on the difference between the estimated rotor speed and the reference speed. The proposed method is validated through MATLAB/Simulink-based simulations and experiments using a 5.5 kW induction motor M−G set, confirming that compared to conventional V/f control, the speed maintenance capability and overall robustness against load variations are enhanced. This study presents a practical solution to effectively improve the performance of existing V/f control systems without adding external sensors. Full article

Colloidal quantum dots (CQDs) have attracted significant attention in optoelectronics due to their size-tunable bandgap, high photoluminescence quantum yield, and solution processability, which enable integration into compact and energy-efficient systems. This review consolidates recent progress in multifunctional CQD-based light-emitting devices and on-chip integration strategies. This review systematically examines fundamental CQD properties (quantum confinement, carrier dynamics, and core–shell heterostructures), key synthesis methods including hot injection, ligand-assisted reprecipitation, and microfluidic flow synthesis, and device innovations such as light-emitting field-effect transistors, light-emitting solar cells, and light-emitting memristors, alongside on-chip components including ongoing electrically pumped lasers and photodetectors. This review concludes that synergies in material engineering, device design, and system innovation are pivotal for next-generation optoelectronics, though challenges such as environmental instability, Auger recombination, and CMOS compatibility require future breakthroughs in atomic-layer deposition, 3D heterostructures, and data-driven optimization. Full article

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability on decadal timescales. This article synthesizes the extensive research on ENSO rectification, exploring a paradigm that resolves this causality problem by recasting ENSO as an active architect of its own mean state. Tracing the intellectual development of this theory, starting from fundamental concepts such as the “dynamical thermostat” and “heat pump” hypotheses, modern analysis has identified the core physical mechanism as nonlinear dynamical heating (NDH), which is rooted in nonlinear heat advection during asymmetric ENSO cycles. The convergence of evidence from forced ocean models and observational diagnostics confirms a rectified signal characterized by an off-equatorial spatial pattern, providing a primary mechanism for tropical Pacific decadal variability (TPDV). By establishing a coherent framework linking high-frequency asymmetry with low-frequency variations, this review lays the foundation for future research and emphasizes the critical role of the rectification effect in improving decadal climate prediction. Full article