Search Results (3,380)

During deep and ultra-deep well drilling operations, the throttling performance of the hydraulic-while-drilling jar is significantly affected by the combined influence of temperature-induced differential thermal expansion among components and changes in the rheological properties of hydraulic oil. These effects often lead to unstable jarring behavior or even complete failure to trigger jarring during stuck pipe events. Here, we propose a high-temperature degradation evaluation model for the throttling performance of the throttle valve in an HWD jar based on thermal expansion testing of individual components and high-temperature rheological experiments of hydraulic oil. By using the variation characteristics of the throttling passage geometry as a linkage, this model integrates the thermo-mechanical coupling of the valve body with flow field simulation. Numerical results reveal that fluid pressure decreases progressively along the flow path through the throttle valve, while flow velocity increases sharply at the channel entrance and exhibits mild fluctuations within the throttling region. Under fluid compression, the throttling areas of both the upper and lower valves expand to some extent, with their spatial distributions closely following the pressure gradient and decreasing gradually along the flow direction. Compared with ambient conditions, thermal expansion under elevated temperatures causes a more pronounced increase in throttling area. Additionally, as hydraulic oil viscosity decreases with increasing temperature, flow velocities and mass flow rates rise significantly, leading to a marked deterioration in the throttling performance of the drilling jar under high-temperature downhole conditions. Full article

The student–teacher relationship is widely acknowledged as a key factor influencing both academic achievement and emotional well-being. In Grade 12 mathematics, where academic demands and pressure are particularly high, the quality of this relationship can significantly affect students’ performance. This study investigates how students with different learning profiles perceive their relationship with their mathematics teacher and how this relationship correlates with their academic outcomes. Grounded in sociocultural perspectives on learning and psychological theories of motivation, the study explores dimensions such as closeness, support, and conflict. The sample included 120 Grade 12 students (aged 17–18) from seven state-funded high schools in Attica, Greece, evenly divided into two groups based on learning characteristics. Data were collected through a structured questionnaire and official school records of mathematics achievement. Findings revealed clear and statistically significant differences between the groups. Students who reported more positive relational experiences also demonstrated higher academic achievement. Across the full sample, stronger relational bonds were associated with better academic performance, while conflict was linked to lower achievement. This study makes a novel contribution by offering a comparative perspective on how the student–teacher relationship functions in high-stakes mathematics education. The results underscore the importance of fostering supportive and inclusive classroom environments, and they highlight the need for targeted professional development to help teachers build effective relationships with diverse learners. Full article

Traditional in-service inspection (ISI) methods for pipelines have certain limitations in identifying pipeline leakages. When these methods are directly applied to the ISI of Hua-long pressurized reactor (HPR1000) nuclear power plants, where the system complexity has significantly increased, they may lead to insufficient inspection efficiency and an extremely heavy workload. In this study, based on the framework of typical risk-informed analysis methods for nuclear power plants in the industry and integrating domestic engineering practical experience, an optimized ISI model for pipelines in HPR1000 nuclear power plants was constructed, and a pilot application was carried out on the chemical and volume control system (RCV) of the primary circuit. The inspection strategy was optimized through a series of steps, including determining the analysis scope, conducting pipe segment failure analysis, constructing a risk matrix, selecting inspection elements, and assessing risk impacts. Case studies showed that the risk-informed in-service inspection (RI-ISI) method successfully classified over 3000 welds in the RCV system based on risk levels (high, medium, low). After optimization, 16 low-risk welds (risk level 7) and one of the two medium-risk welds (risk level 4) that originally required volumetric inspection were exempted from inspection. Quantitative risk analysis confirmed that the increments in core damage frequency (CDF) and large early-release frequency (LERF) caused by this optimization were far below the regulatory limits. This method significantly reduces the inspection burden of medium- and low-risk pipelines while ensuring that high-risk areas receive priority attention, providing important technical support for the safe and efficient operation and maintenance of HPR1000 and subsequent third-generation nuclear power units. Full article

A comprehensive understanding of aerodynamic instabilities, such as flutter, non-synchronous vibration (NSV), rotating stall, and forced response, is crucial for the safe and efficient operation of turbomachinery, particularly fans and compressors. These instabilities impose significant limitations on the operating envelope, necessitating precise monitoring and accurate quantification of vibration amplitudes during experimental investigations. This study addresses the challenge of measuring these amplitudes by comparing multiple measurement systems applied to the open-test case of the ultra-high bypass ratio (UHBR) fan ECL5. During part-speed operation, the fan exhibited a complex aeromechanical phenomenon, where an initial NSV of the second blade eigenmode near peak pressure transitioned to a dominant first-mode vibration. This mode shift was accompanied by substantial variations in blade vibration patterns, as evidenced by strain gauge data and unsteady wall pressure measurements. These operating conditions provided an optimal test environment for evaluating measurement systems. A comprehensive and redundant experimental setup was employed, comprising telemetry-based strain gauges, capacitive tip timing sensors, and a high-speed camera, to capture detailed aeroelastic behaviour. This paper presents a comparative analysis of these measurement systems, emphasizing their ability to capture high-resolution, accurate data in aeroelastic experiments. The results highlight the critical role of rigorous calibration procedures and the complementary use of multiple measurement technologies in advancing the understanding of turbomachinery instabilities. The insights derived from this investigation shed light on a complex evolution of instability mechanisms and offer valuable recommendations for future experimental studies. The open-test case has been made accessible to the research community, and the presented data can be used directly to validate coupled aeroelastic simulations under challenging operating conditions, including non-linear blade deflections. Full article

Most existing ultra-deep gas wells are characterized by high temperature, high pressure, and high sulfur content. During development, they face serious challenges such as unclear mechanisms of acid gas-induced blowouts and difficulties in wellbore pressure inversion, posing significant challenges to well control operations. To reveal the reasons behind the tendency of acidic gases to trigger blowouts and to clarify the impact of different concentrations of acidic gases on the flow behavior of annular fluids, this study considers the effects of solubility and phase changes on the physical properties of acidic gases. A method replacing critical parameters with pseudo-critical parameters is used to analyze the variation trends of gas density, solubility, and other properties along the well depth. A mathematical model for the annular flow of acidic gas overflow incorporating solubility phase change effects is established. The model is numerically solved using a four-point difference scheme, exploring the essential characteristics of gas flow in the annulus after overflow, and discussing the distribution patterns of physical properties of acidic gases, as well as dynamic parameters such as wellbore pressure and temperature along the well depth. Numerical simulations show that the physical properties of acidic gases change significantly with well depth: the more acidic gas present in the wellbore, the smaller the deviation factor, and the greater the density and viscosity, with parameter changes exceeding 40% near the pseudo-critical point for binary mixtures with 40% H₂S. Compared to pure methane, mixed fluids containing acidic gas experience more than 20% volume expansion near the wellhead for ternary mixtures with 20% CO₂ and 20% H₂S, and the flow velocity increases by more than 10% for mixtures with ≥30% acidic gas content, leading to a higher risk of a sudden pressure drop during well control. This study clarifies the migration patterns of acidic gas overflow in HPHT (high pressure, high temperature) gas wells, providing valuable guidance for optimizing well control design, improving well control emergency plans, and developing well-killing measures. Full article

The ablation process of a silica fiber-reinforced polymer (SiFRP) composite under aerodynamic heating and a shear environment was investigated by experiments and numerical study. The flat plate samples were tested in an arc jet wind tunnel under heat flux and pressure ranging from 107 W/cm² at 2.3 kPa to 1100 W/cm² at 84 kPa. The heating surface experiences shear as high as 1900 Pa. The in-depth thermal response and ablating surface temperature of the specimens are measured during ablation. According to the ablation experimental results, a multi-layer ablation model was established that accounts for the effects of carbon deposition, investigating the thermophysical properties of the ablation deposition layer. The accuracy of the proposed ablation model was evaluated by comparing the calculated and experimental surface ablation recession and internal temperature of a silica–phenolic composite under steady-state ablation. Carbon–silica reaction heat is the important endothermic mechanism for silica-reinforced composites. The research provides valuable reference for understanding the ablative thermal protection mechanism of silicon–phenolic composites in a high shear environment. Full article

With the growing demand for energy, oil and gas exploration and development are progressively moving into deep and ultra-deep formations, where extreme temperatures and pressures create complex challenges for drilling operations. While drilling fluids are critical for controlling bottom-hole pressure, cooling drill bits, and removing cuttings, accurately characterizing their rheological behavior under high-temperature and high-pressure (HTHP) conditions remains a key focus, as existing research has limitations in model applicability and parameter prediction range under extreme downhole environments. To address this, the study aims to determine the optimal rheological model and establish a reliable mathematical prediction model for drilling fluid rheological parameters under HTHP conditions, enhancing the precision of downhole temperature and pressure calculations. Rheological experiments were conducted on eight field-collected samples (4 water-based and four oil-based drilling fluids) using a Chandler 7600 HTHP rheometer, with test conditions up to 247 °C and 140 MPa; nonlinear fitting via a hybrid Levenberg–Marquardt and Universal Global Optimization algorithm and multivariate regression were employed for model development. Results showed that oil-based and water-based drilling fluids exhibited distinct rheological responses to temperature and pressure, with the Herschel–Bulkley model achieving superior fitting accuracy (coefficient of determination > 0.999). The derived prediction model for Herschel–Bulkley parameters, accounting for temperature-pressure coupling, demonstrated high accuracy (R² > 0.95) in validation. This research provides an optimized rheological modeling approach and a robust prediction tool for HTHP drilling fluids, supporting safer and more efficient deep and ultra-deep drilling operations. Full article

Large underwater vehicles, designed for multiple cruising speeds, are required to operate under diverse conditions such as full speed, surfacing, diving, and hovering. This demands that the axial flow pumps used in these applications have a broad operational range, typically functioning efficiently from 0.1 times rated flow to 1.5 times rated flow. In the process of adjusting operational conditions, axial flow pumps may experience rotating stall phenomena. Importantly, the presence of tip leakage vortices within the pump markedly influences the internal flow dynamics. To assess the impact of tip leakage vortices on the internal flow field under varied operational states, this study delves into the inherent link between tip leakage vortices and pressure pulsation across three specific scenarios: optimal, critical stall, and deep stall conditions. Analyzing from the perspective of the vorticity transport equation, it is found that the compression–expansion term dictates the core strength of tip leakage vortices, while the viscous dissipation factor determines the frequency of pressure pulsation. With an increase in the core strength of tip leakage vortices, a gradual rise in pressure pulsation is observed; in optimal scenarios, the core of tip leakage vortices progressively shifts toward the interior of the clearance, keeping the pulsation amplitude at each monitoring point within the blade tip clearance at integer multiples of the blade passing frequency. During critical stall and deep stall scenarios, the viscous dissipation effect of tip leakage vortices contributes to the emergence of high-frequency harmonic components within pressure pulsation. Full article

The development of hot dry rock needs working fluids, and mineral reactions happen due to contact between hot dry rock minerals and fluids. Mineral reactions influence hot dry rock reservoir characteristics, whereas their impacts on hot dry rock mechanical properties lack understanding. In this study, a simulation study on the influence of reactions between granitic hot dry rock minerals and water on rock non-closed crack compressive shear initiation was conducted. High temperature (180 °C) and high pressure (24 MPa) mineral reaction experiments were performed to obtain reaction kinetics parameters. A model of non-closed crack compressive shear initiation induced by mineral reactions was established. Based on the model, the influences of mineral reactions on the compressive shear initiation of non-closed cracks were analyzed. Results show that the mineral reactions primarily contain feldspar dissolution and quartz precipitation, and their overall effect is crack enlargement. The crack enlargement reduces crack initiation potential for a crack inclination angle of α = 0°, while it increases crack initiation potential for α = 45° and 90°. The difference in crack initiation potential under various α values is attributed to the relative position of the crack to the maximum principal stress direction. This work reveals the influences of mineral reactions on hot dry rock reservoir crack initiation, contributing to achieving sustainable hot dry rock exploitation. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time–frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness. Full article

Vacuum membrane-based air dehumidification (MAD) is potentially more efficient than refrigeration cycles. Air permeance through a membrane is inevitable, especially when there is a large pressure difference between the supply and permeate sides. Given the high specific gas volume under vacuum conditions, removing the permeating air from the dehumidifier is crucial for the stable operation of the vacuum compressor. Energy-efficient air removal techniques are still lacking, thereby hindering the development of MAD technology. This paper proposes a novel MAD approach using a vacuum mixing condenser. The cooling water directly condenses moisture from the vacuum compressor without any heat exchanger. The permeating air and water mixture in the condenser then experiences a quasi-isothermal pressurization process through a multiphase pump, enabling continuous dehumidification and air removal with low power consumption. The fundamentals of the proposed approach are illustrated, and mathematical models are built. Influences of air permeance rate, cooling water flow rate, condenser pressure, membrane area, and gravitational work are investigated. The results show that a COP of 8~12 is achievable to dehumidify air to 50%RH, 25 °C. The vacuum compressor consumes about 80% of the power. A low air permeance rate, low condenser pressure, large membrane area, and high gravitational work positively impact the COP, while the cooling water flow rate has a more complex effect. The proposed dehumidifier can use less selective membranes for higher permeability and cost-effectiveness. Full article

Cyclic solvent injection (CSI) with CO₂ is a promising non-thermal enhanced oil recovery (EOR) method for heavy oil reservoirs that also supports CO₂ sequestration. However, its effectiveness is limited by short foamy oil flow durations and low CO₂ utilization. This study explores how waterflooding and nanoparticle-assisted flooding can enhance CO₂-CSI performance through experimental and numerical approaches. Three sandpack experiments were conducted: (1) a baseline CO₂-CSI process, (2) a waterflood-assisted CSI process, and (3) a hybrid sequence integrating CSI, waterflooding, and nanoparticle flooding. The results show that waterflooding prior to CSI increased oil recovery from 30.9% to 38.9% under high-pressure conditions and from 26.9% to 28.8% under low pressure, while also extending production duration. When normalized to the oil saturation at the start of CSI, the Effective Recovery Index (ERI) increased significantly, confirming improved per-unit recovery efficiency, while nanoparticle flooding further contributed an additional 5.9% recovery by stabilizing CO₂ foam. The CO₂-CSI process achieved a maximum CO₂ sequestration rate of up to 5.8% per cycle, which exhibited a positive correlation with oil production. Numerical simulation achieved satisfactory history matching and captured key trends such as changes in relative permeability and gas saturation. Overall, the integrated CSI strategy achieved a total oil recovery factor of approximately 70% and improved CO₂ sequestration efficiency. This work demonstrates that combining waterflooding and nanoparticle injection with CO₂-CSI can enhance both oil recovery and CO₂ sequestration, offering a framework for optimizing low-carbon EOR processes. Full article

Intravascular aspiration thrombectomy catheters are widely used to treat stroke, pulmonary embolism, and deep venous thrombosis. However, their performance is frequently compromised by clot material becoming lodged within the catheter tip. To address this, we develop a novel ultrasound-enhanced aspiration catheter approach that generates cavitation within the tip to mechanically degrade clots, with a view to facilitate extraction. The design employs hollow cylindrical transducers that produce inwardly propagating cylindrical waves to generate sufficiently high pressures to perform histotripsy. This study investigates the feasibility of self-sensing cavitation detection by analyzing voltage signals across the transducer during treatment. Experiments were conducted for two transmit pulse lengths at varying driving voltages with water or clot in the lumen. Cavitation clouds within the lumen were assessed using 40 MHz ultrasound imaging. Changes in the signal envelope during the pulse body and ringdown phases occurred above the cavitation threshold, the latter being associated with more rapid wave damping in the presence of bubble clouds within the lumen. In the frequency domain, voltage-dependent cavitation signals—subharmonics, ultra-harmonics, and broadband—emerged alongside transmit pulses. This work demonstrates a highly sensitive, sensor-free method for detecting cavitation within the lumen, enabling feedback control to further improve histotripsy-assisted aspiration. Full article

A central problem in human–robot interaction is the risk of severe injury in humans in the event of a collision with a rigid robot arm. The introduction of variable stiffness into a robot arm mitigates the effects of impact and generates a safe interaction in its compliant state. An approach to vary the stiffness of members in a robotic arm is Laminar Jamming. In this article, a new lock/unlock mechanism for Laminar Jamming is proposed. The solution consists of a pneumatic actuator that drives a trapezoidal pin to interfere mechanically with the layers, and, in turn, changing the stiffness of the Laminar Jamming Structure. Additionally, frames are placed along the structure to avoid local buckling of the layers. Experiments and finite element simulations were carried out to study the mechanical performance of this new mechanism. Experiments show that the proposed mechanism reached a maximum stiffness ratio of 3.65, which is 15% higher than the stiffness ratio of an equivalent flat clamp mechanism. Experiments also demonstrate that the proposed mechanism does not show the stick-slip phenomenon that exists in the flat clamp mechanism. Computational case studies were carried out to investigate the effects of the angle of the trapezoidal pin, the number of frames, the direction of the transverse force and the behavior at high deflections. Simulations show that the 30° trapezoidal pin has the highest stiffness for pressures larger than 500 kPa, three frames placed along the Laminar Jamming generate the maximum stiffness ratio, the stiffness slightly varies when the transverse force changes direction, and the stiffness decreases with increasing deflection. Full article