Search Results (356)

In view of the frequent occurrence of trapped annular pressure and the increasingly prominent risk of wellbore integrity under the periodic high-intensity injection and production conditions of gas storage wells, a trapped annular pressure prediction model suitable for deep gas storage wells is established based on the comprehensive heat transfer characteristics of the tubing string-cement sheath-formation. The calculation results of the model are in good agreement with field-measured pressure data, with a coincidence degree of about 95%. Based on the established model, the influence laws of four major factors, including tubing specification and dimension, thermophysical properties of annular fluid, casing material characteristics and daily gas production rate, on trapped annular pressure are systematically analyzed. Meanwhile, the pressure control effects of three measures, namely Annulus A pressure relief, application of insulated tubing and nitrogen injection into Annulus B, are quantitatively compared for the case well. The research results show that adopting tubing with larger outer diameter and thinner wall thickness, injecting fluid with lower thermal expansion coefficient or higher isothermal compressibility coefficient into the annulus and appropriately reducing daily gas production can effectively decrease trapped annular pressure. Among them, the influence of fluid properties on trapped annular pressure is far greater than that of pipe material parameters. Among the three pressure control measures, nitrogen injection into Annulus B presents the optimal pressure control effect; when the nitrogen volume accounts for approximately 3% of the total annular fluid volume, the trapped annular pressure is reduced by about 82%. The research findings provide a theoretical basis and technical guidance for the prediction and control of trapped annular pressure in gas storage wells. It is recommended to prioritize the nitrogen injection technology for Annulus B in the well construction stage, and realize pressure management for producing wells by combining Annulus A pressure relief and production regulation. Full article

A reinforcement-learning-based wall-modeled large-eddy simulation (RL-WMLES) framework is proposed to improve the physical consistency of near-wall turbulence predictions. In this approach, a reinforcement learning agent is coupled with the WMLES solver to dynamically adjust a compensating stress term, with the objective of enforcing agreement between the LES solution and the law of the wall. The agent is trained using the proximal policy optimization (PPO) algorithm, where the state is defined as the discrepancy between the near-wall LES velocity and the wall-model prediction, and the action corresponds to modifying a parameterized support viscosity distribution. The proposed method is implemented within a high-performance CFD solver and trained on turbulent channel flow. Numerical results demonstrate that the trained agent effectively reduces the log-layer mismatch and significantly improves the accuracy of near-wall velocity predictions. Furthermore, the RL-WMLES framework exhibits a degree of generalization capability: the trained agent performs robustly with varying levels of numerical dissipation and Reynolds numbers. By introducing a simple interpolation strategy, the same agent can be successfully applied to configurations with different matching locations. Overall, the RL-WMLES framework provides a flexible and data-driven approach for enforcing physical constraints in turbulence modeling. The method shows strong potential for extension to more complex flows. Full article

Interface debonding between the casing and cement sheath (CC interface) is a major cause of wellbore failure in hot dry rock geothermal wells. By adding iron filings to cement and varying their distribution along the radial direction, a cement sheath with gradient mechanical properties is obtained. This sheath is called a functional gradient cement sheath. In this paper, a theoretical mechanical model of the functional gradient cement sheath is established. Its mechanical parameters are obtained from laboratory experiments. Analytical solutions for the stress and displacement fields of the casing–functional gradient cement sheath–formation system are derived using elastic thick-walled cylinder theory. The effectiveness of the functional gradient cement sheath in preventing CC interface debonding is then studied. The results indicate the following: (1) cement block samples containing iron filings were prepared with particle sizes of 0.5 mm, 1 mm, and 2 mm and with iron filing-to-cement mass ratios of 0%, 10%, 20%, 30%, and 40%. The compressive strength and elastic modulus of these samples both varied with the iron filing content. As the iron filing content increases, the compressive strength and elastic modulus generally increase, but they decrease under certain conditions. (2) With the total mass of iron filings fixed, the influence of different elastic modulus distributions (exponential, linear, quadratic parabolic, and uniform) on the functional gradient cement sheath was investigated. It was found that the quadratic parabolic distribution of the elastic modulus yields the best mechanical properties. (3) The influence law of the size and dosage of iron filings on the functional gradient cement sheath was studied. Based on the experimental data (0%, 10%, 20%, 30%, 40%), three representative contents (15%, 30%, 45%) were selected for theoretical analysis. It was found that when the iron filing size was 0.5 mm and the dosage was 15%, the stress and displacement on the inner wall of the functional gradient cement sheath were the minimum. Full article

Steel pipelines for oil and gas transportation serve as the lifeline of energy conveyance, and their long-term safe operation constitutes a crucial safeguard for energy security. Nevertheless, in complex service environments, local defects formed on the inner pipe wall due to medium corrosion have emerged as a prominent hidden danger endangering pipeline integrity. Accurate evaluation of the residual strength of pipelines with corrosion defects is not only the technical foundation for ensuring the safe operation of pipelines, but also the key basis for formulating scientific maintenance strategies and prolonging the service life of pipelines. Taking three grades of steel pipelines (X52, X65 and X80), which represent the typical strength grades commonly used in long-distance oil and gas transmission pipelines, as the research objects, this paper establishes a three-dimensional finite element model of single-point uniform corrosion defects considering the nonlinear material behavior, and systematically investigates the influence laws of geometric parameters (depth, length and width) of corrosion defects on the failure pressure of pipelines under the action of monotonic internal pressure load. The accuracy of the proposed finite element model is verified by comparison with the test data from thirteen groups of full-scale burst experiments. On the basis of parametric analysis results, an explicit and high-precision predictive model for failure pressure is developed. The research findings reveal that corrosion depth acts as the dominant factor affecting pipeline failure pressure with a distinctly nonlinear influence characteristic: the load-bearing capacity of pipelines drops drastically when the relative depth d/t exceeds 0.6, where d is the corrosion depth and t is the pipe wall thickness. There exists a critical value for the impact of corrosion length, beyond which its weakening effect on failure pressure tends to level off. Within the commonly encountered engineering range (20~100°), corrosion width exerts a negligible influence on pipeline failure pressure and thus can be overlooked in engineering evaluation. In comparison with conventional industry assessment methods such as ASME B31G, DNV RP-F101, PCORRC and SY/T 6151, the newly established predictive model features higher prediction accuracy and broader applicability, which provides on-site engineers with a powerful theoretical tool and practical formula for the rapid and accurate evaluation of the residual strength of corroded pipelines. Full article

The post-combustion carbon capture process with monoethanolamine/piperazine (MEA/PZ) blends encounters notable modeling and optimization challenges. These arise from strong thermodynamic–kinetic nonlinear coupling, as well as limited availability of high-quality experimental data. To address this, we propose a machine learning and multi-objective optimization strategy driven by physicochemical features. By extracting explicit physical features and embedding physicochemical constraints into data-driven models, this study evaluated the predictive performance of three distinct algorithms based on wet-wall column experimental data. These algorithms included natural gradient boosting (NGBoost), sure independence screening and sparsifying operator (SISSO), and gaussian process regression (GPR). Subsequently, an optimization problem aimed at minimizing $P_{{\mathrm{CO}}_2}^{*}$ and maximizing $k_{\mathrm{g}}^{\prime }$ was formulated. The multi-objective beluga whale optimization (MOBWO) algorithm was then employed for global optimization and benchmarked against the traditional non-dominated sorting genetic algorithm II (NSGA-II). Results indicate that the Gaussian process regression (GPR) model performed best when it was enhanced by physicochemical features and optimized via Bayesian hyperparameter tuning. It achieved R² values of 0.989 and 0.953 for $P_{{\mathrm{CO}}_2}^{*}$ and $k_{\mathrm{g}}^{\prime }$, with average absolute relative deviations (AARDs) kept below 15.7% and 12.2% respectively. Feature importance analysis validated the underlying physical laws. Specifically, temperature dictates thermodynamic equilibrium, while CO₂ loading limits mass transfer kinetics. In the optimization phase, MOBWO outperformed NSGA-II by generating a more uniformly distributed Pareto front. Decision-making analysis further identified three typical operating regimes encompassing kinetics-dominant, thermodynamics-dominant, and comprehensive equilibrium conditions. This framework provides a robust paradigm for small-sample modeling and optimization in complex chemical processes. Full article

To address the issues of uneven accuracy across the entire hole depth and profile distortion caused by multi-source errors in spectral confocal deep-hole measurement, this paper proposes a measurement method involving global calibration using a stepped ring gauge and hierarchical compensation for multi-source errors. By classifying core measurement errors into three categories—geometric deviation, structural error, and dynamic process error—according to their propagation laws, this paper establishes a progressive comprehensive compensation system comprising “geometric calibration–structural correction–dynamic filtering”. Specifically, using a stepped ring gauge as the reference, the system’s intrinsic geometric parameters are identified via the Levenberg–Marquardt (LM) algorithm; structural errors introduced by the deflection of components due to self-weight are quantitatively corrected based on a statics model; periodic harmonic errors are sequentially separated; random noise is effectively suppressed by combining least-squares harmonic fitting with adaptive wavelet threshold filtering. Experimental results demonstrate that this method can limit the maximum absolute deviation in the inner diameter measurement of standard ring gauges to within 0.2 μm, stabilizing the measurement repeatability over the entire depth of deep-hole workpieces with length-to-diameter ratios exceeding 30:1 to within 0.8–1.6 μm, with an expanded uncertainty of $U$ = 3.8 μm ($k$ = 2). This method enables the precise reconstruction of deep-hole inner wall topography, providing a highly versatile technical foundation and implementation scheme for the high-precision non-destructive testing of deep holes with large length-to-diameter ratios. Full article

To elucidate the wear evolution and failure mechanisms of VL-type composite seals in aviation hydraulic actuators under multiple operating conditions, a two-dimensional plane-strain finite element model was developed for a VL seal consisting of a PTFE L-ring and an NBR O-ring. The model incorporated the Mooney–Rivlin hyperelastic constitutive law and the Archard wear model. The effects of O-ring compression ratio, hydraulic pressure, sliding velocity, and temperature on cumulative wear, wear rate, and contact state were systematically investigated. The results show that the compression ratio has a nonlinear influence on wear. Within 8–16%, the peak wear increases approximately linearly with compression ratio; above 16%, the peak wear reaches a plateau and a secondary wear zone appears, indicating a transition from single-contact to multi-contact sealing. Hydraulic pressure promotes wear over the range of 4–28 MPa, and at 28 MPa the opposite lip edge of the L-ring comes into contact with the cylinder wall, weakening the sealing effectiveness. Within 0.1–0.3 m/s, wear increases approximately linearly with sliding velocity. However, under high velocity and insufficient hydraulic pressure, the L-ring may undergo inversion, resulting in complete seal failure. Temperature exhibits a non-monotonic effect: material softening reduces local contact stress and wear from −55 to 80 °C, whereas excessive softening at 135 °C causes the peak wear rate to increase again. A parametric analysis scheme involving an increased L-ring height and thickness, a reduced O-ring cross-section diameter, and reserved deformation space raises the critical compression ratio for stable single-contact sealing from 16% to above 20%. These findings clarify the contact-stress/contact-area competition mechanism governing VL seal wear and provide guidance for the design of aviation hydraulic actuator seals. Full article

Entropy generation analysis provides a thermodynamic framework for quantifying irreversibility in thermal systems. However, most existing second-law studies rely on simplified boundary conditions and do not consider fully coupled conjugate heat transfer involving fluid convection, wall conduction, and external heat exchange. Consequently, thermodynamic assessments under realistic conditions remain limited. This study presents an entropy generation-based assessment of turbulent conjugate heat transfer in circular pipes by considering the combined effects of wall thickness ratio (0.02–0.08), wall thermal conductivity (0.2–400 W/m·K), and external convection (5–100 W/m²·K). A three-dimensional steady RANS-based conjugate heat transfer model is employed, and entropy generation is evaluated to quantify irreversibility within fluid and solid domains. The results indicate that wall-related thermal resistances significantly affect thermodynamic performance. Variations in wall conductivity lead to approximately 15–20% changes in total irreversibility, while increasing external convection from 5 to 20 W/m²·K results in up to 25–30% variation. Increasing wall thickness enhances conductive entropy generation, whereas higher Reynolds numbers increase overall irreversibility. These findings demonstrate that the Biot number is a key parameter governing irreversibility distribution. The results provide energy-efficient design insights for optimizing thermally coupled engineering systems under realistic operating conditions. Full article

Existing structural health monitoring of LNG (liquefied natural gas) liquid storage tanks is strictly constrained by explosion-proof safety and engineering conditions, making it impractical to achieve full-domain coverage through dense sensor deployment. How to achieve effective coverage of structural seismic weak parts under limited measuring point conditions is the core issue for monitoring scheme optimization. This paper takes a practical large full-containment LNG storage tank project as the research object and proposes a targeted sensor deployment method based on finite element seismic response analysis: identifying structural seismic weak parts through refined finite element modeling and seismic response analysis, thereby achieving coverage of critical regions and improved monitoring efficiency under limited sensor constraints. The research approach is as follows: a finite element model of the LNG storage tank is established using ADINA software and verified through modal analysis combined with on-site ambient vibration testing, ensuring the accuracy and engineering applicability of numerical simulation. Typical seismic records including El Centro, Tangshan, and TAFT are selected, and seismic response analysis of the tank is carried out, clarifying the displacement response laws under different seismic waves and identifying the junctions of dome roof and tank wall, buttress columns and tank wall, and the upper and local areas of the tank wall as structural seismic weak parts. Based on the characteristics of these parts and on-site explosion-proof conditions, a four-measuring-point targeted monitoring sensor deployment scheme is formulated and applied in engineering. This research constructs a structural health monitoring method for LNG storage tanks featuring “structural model verification–weak part identification–monitoring scheme customization,” providing a new approach for tank monitoring under explosion-proof safety constraints and partially addressing the limitations of traditional empirical deployment methods. This study establishes a technical path covering the full cycle of routine operation, seismic response, and post-earthquake assessment, providing methodological support for the structural health monitoring of LNG storage tanks, and its core concepts can also serve as a reference for the structural health monitoring of similar large-scale thin-walled storage tanks. Full article

For personalized treatments, including soft tissues repair, the use of in situ bioprinting is of increased interest. Many soft tissues, such as sphincters, have poorly known mechanical properties and a complex structure, with limited options for a medical practitioner to assess where the injections should be made and how much should be injected. The rate of injection and its variation have a direct implication on pain sensation for patients, but post-injection efficacy largely depends on the ability of the hydrogel to adapt to local loads and displacements, keeping the 3D structure compliant to the surrounding tissues. Such a method is known as ‘in situ bioprinting’. There are, however, limited data regarding hydrogels’ functionalities for such applications, and many commercial hydrogels, as medical devices, are used off-label. This study aims to introduce an innovative, robust, and reliable approach for evaluating the ejection-related mechanical properties of various commercial hydrogels. The ejectability of six clinically approved hydrogels was assessed through their rheological properties, characterized by measuring apparent viscosity using a mechanical testing device in a novel setup combined with the dynamic syringe pump analysis (for a pre-set constant ejection rate). It was shown that a well-established power-law approximation offers a straightforward, less computationally intensive approach than more complex models that attempt to account for viscosity, shear rate, and wall slip. It assesses hydrogel performance within an actual system, including the syringe and nozzle, rather than just characterizing the material in isolation, thus making it particularly valuable for predicting how gels will behave under real conditions. This method can be adapted for specific clinical bioprinting applications, including sphincter repair, lipoatrophy correction, or deep dermal/transdermal targets, optimizing speed, flow rate, and applied force. Full article

To enhance the energy efficiency and environmental sustainability of solar greenhouses, precise microclimate control is essential. Composite thermal blankets critically influence heating demand and carbon footprint, yet conventional heat transfer models often neglect their internal structural characteristics, limiting simulation accuracy and optimization. Accordingly, a heat transfer model for composite thermal blankets was developed based on the law of energy conservation. The model discretizes the internal structure and integrates radiation, convection, conduction, and latent heat from condensation. It uniquely incorporates dynamic environmental factors and blanket properties including layered composition, porosity, and moisture content. Accuracy was validated through numerical simulations and field experiments in both traditional brick-wall and prefabricated flexible-wall solar greenhouses under various weather conditions. Validation showed strong agreement: for the brick-wall greenhouse, mean absolute error (MAE) was 1.21 °C, root mean square error (RMSE) 1.27 °C, and R² 0.97; for the flexible-wall greenhouse, MAE was 0.56 °C, RMSE 1.08 °C, and R² 0.85. These indicators confirm that the model reliably quantifies the impact of thermal insulation blanket material and structure on thermal performance, providing a basis for design optimization and a reduction in supplemental heating demand and carbon emissions. Further analysis examined the porosity and moisture effects on spray-bonded cotton, PE foam, and needle-punched felt. Under low moisture, higher porosity reduced thermal conductivity by up to 27.4%, 57.6%, and 52.4%, respectively. However, under high moisture, conductivity increased with porosity in materials with interconnected pores (spray-bonded cotton and Needle-punched felt) due to continuous water channels, while closed-cell PE foam conductivity continued decreasing. All materials showed linearly increasing conductivity with moisture content, with higher-porosity materials exhibiting greater sensitivity. For example, at porosities of 0.95, 0.95, and 0.85, moisture content rising from 0 to 0.225 increased conductivity by 264%, 209.6%, and 196.7%. This model provides a robust theoretical foundation for the scientific selection, structural optimization, and performance evaluation of composite thermal blankets in greenhouse applications. Full article

Neural scaling laws describe how model performance improves as a power law with size, but existing work has focused almost entirely on models above 100 M parameters. The regime below 20 million parameters, where TinyML and edge AI systems operate, remains largely unexamined. We train 90 models spanning 22 K to 19.8 M parameters across two architecture families (a plain ConvNet and MobileNetV2) on CIFAR-100, varying width while holding depth and training protocol fixed. Both architectures follow approximate power laws, with exponents of $\alpha =0.156$ (ScaleCNN) and $\alpha =0.106$ (MobileNetV2). However, the power law does not hold uniformly: local exponents decay with scale, and MobileNetV2 saturates at 19.8 M parameters (${\alpha }_{\mathrm{local}}=0.006$), hitting a data wall. The structure of errors also changes with scale. The Jaccard overlap between error sets of the smallest and largest ScaleCNN models is only 0.35; compression changes which inputs are misclassified, not merely how many. Small models develop a triage strategy, concentrating capacity on easy classes (Gini of per-class accuracy: 0.26 at 22 K params vs. 0.09 at 4.7 M) while effectively abandoning the hardest ones (bottom-5 class accuracy: 10% vs. 53%). The smallest models achieve the lowest ECE values (0.013 vs. peak 0.110 at mid-size), reversing the typical overconfidence–capacity relationship, though this partly reflects a global-mean matching artifact rather than well-calibrated per-bin confidence. On CIFAR-100, aggregate accuracy alone is therefore a misleading basis for edge deployment decisions; validation must happen at the target model size. All findings in this study are based on CIFAR-100 (32 × 32, 100 classes); their generalizability to other datasets, resolutions, and architectures remains to be verified. Full article

To achieve the same service life of glass fiber reinforced polymer (GFRP) connectors and precast concrete sandwich panels, ensuring the structural stability and safety of the walls during long-term service, it is necessary to research the durability of GFRP connectors. In accordance with the ACI 440.3R-12 test method, an accelerated aging study was conducted by immersing 90 GFRP connectors in a simulated concrete pore solution at temperatures of 40 °C, 60 °C, and 80 °C for durations of 3.65, 18, 36.5, 92, and 183 days. This investigation aimed to analyze the effects of temperature and exposure time on the shear strength of the GFRP connectors. Scanning Electron Microscopy (SEM) was employed to analyze the micro-morphology of the specimens before and after exposure. The SEM observations revealed that after 183 days at 40 °C, the fiber-matrix interface remained relatively intact without significant debonding. However, at 60 °C, noticeable degradation occurred, characterized by corrosion of fibers and evident debonding from the surrounding matrix. At 80 °C, the GFRP specimens were severely damaged, precluding the extraction of viable samples for SEM analysis. The results further indicated that the most rapid decline in the shear strength occurred within the initial 3.65 days of exposure, with reductions of 8.62%, 10.12%, and 10.77% at 40 °C, 60 °C, and 80 °C, respectively. The degradation rate subsequently decelerated with prolonged exposure. After 183 days, the residual shear strength retention rates decreased by 21.03% and 26.89% at 40 °C and 60 °C, respectively. This behavior is primarily attributed to a high moisture absorption rate driven by a significant humidity gradient between the surface and the interior, leading to rapid swelling and plasticization of the vinyl ester resin matrix, which consequently reduced the stiffness and strength of the GFRP connectors. Finally, a predictive model for the time-dependent shear strength of GFRP connectors under various temperature conditions was developed based on Fick’s law. Full article

To address the challenges of complex wellbore trajectories in horizontal gas wells and the significant differences in droplet entrainment laws across various well sections, which make it difficult to accurately predict the most critical location for liquid loading, this study establishes a prediction model for the critical liquid-carrying flow rate in different well sections. The model is based on droplet force balance and Kelvin–Helmholtz wave theory, considering droplet deformation and energy losses due to wall collisions and friction. By integrating the critical liquid-carrying flow rate models for each section with a four-field coupled wellbore prediction model, a coupled temperature-pressure and liquid-carrying prediction model is developed. Sensitivity analysis was performed on factors influencing the critical liquid-carrying flow rate, and a field data analysis was conducted on 43 gas wells. The results indicate that the proposed model provides accurate predictions, with only one well being misjudged. For four wells near the liquid loading state, the predictions were within a ±15% error range, with an average deviation of only 5.9%. The research results provide a theoretical basis for the accurate prediction of liquid loading in horizontal gas wells. Full article

As a composite structure with both support and load-bearing functions, the composite underground structure wall has been widely applied in engineering. However, in terms of scientific research, a simplified calculation method that can reflect the internal force distribution law and the interaction mechanism between the two walls has not been found. In terms of design applications, the internal force calculated by the traditional total method has a relatively large deviation from the actual situation. This study proposes an internal force calculation method for composite underground structure walls based on the incremental method. The difference between the at-rest earth–water pressure and the active earth–water pressure is taken as the load increment, which is applied step-by-step according to the construction conditions. Based on the Wangsheren Subway Station in Jinan, China, the actual bending moments of the diaphragm wall and inner lining wall are back-analysis using Plaxis 2D V20 with measured horizontal deformation as input. Models of the incremental method and total method are built in Midas GEN 2022. Bending moment distributions under various conditions are compared. The results show the following: (1) The absolute values of the bending moments of the two walls calculated by the incremental method are inversely proportional along the depth direction, which is consistent with the trend of back-analysis, while the absolute values of the bending moments of the two walls calculated by the total method are directly proportional. (2) The incremental method has a higher calculation accuracy for the characteristic points of the bending moment. In terms of calculated values, the bending moment of the diaphragm wall is 0.87–1.90 times that of the back-analysis, and that of the inner lining wall is 1.06–4.93 times, while the deviation of the total method is significantly larger (0.47–3.34 times for the diaphragm wall and 1.49–16.64 times for the inner lining wall). (3) Under complex working conditions, the calculation results of the incremental method are still better than those of the total method. This incremental method can better simulate the interaction mechanism and the internal force redistribution characteristics of the composite underground structure wall. The calculation results are more in line with the engineering reality, which can save materials while ensuring the structural safety and provides a more scientific theoretical method for relevant designs. Full article