Search Results (161)

To address the technical challenge of synergistic optimization between lightweight design and structural performance for mechanical locking drill pipes of rotary drilling rigs, this study takes such drill pipes as the research object. Seven typical operating conditions are classified to construct a multidimensional verification model encompassing static strength, stiffness, stability, and fatigue strength, while a lightweight optimization model with multi-performance constraints is established to minimize the cross-sectional area. Aiming at the limitations of the Enhanced Gaining–Sharing Knowledge (eGSK) algorithm in initial population distribution, integer constraint adaptation, and local exploration, an Improved Enhanced Gaining–Sharing Knowledge algorithm (ieGSK) is proposed, integrating hybrid initialization, integer solution processing, and elite local search mechanisms. Comparative tests with Enzyme-Activated Optimization (EAO), State-Based Optimization (SBO), and eGSK algorithms demonstrate that ieGSK converges to engineering-practical integer solutions in 258 iterations (computational efficiency, 4.475 s). Compared with EAO, SBO, and eGSK algorithms, the computational efficiency is improved by 61.6%, 43.1%, and 9.6%, respectively, while achieving a 9.8% weight reduction and maintaining optimal stability and robustness. This verifies the superiority of ieGSK in drill pipe structural optimization, offering technical support for the lightweight design of core components in rotary drilling rigs. Full article

Microplastic pollution is a defining environmental crisis of the Anthropocene, threatening ecosystems and human health due to its persistence and global dispersion. This review synthesizes current knowledge through a chemical engineering framework, analyzing the contaminant’s lifecycle from formation and environmental fate to detection and removal. We systematically evaluate conventional and advanced mitigation technologies, highlighting the potential of engineered adsorbents (e.g., functionalized sponges, biochar) for targeted capture while underscoring the limitations of current wastewater treatment for nano-plastics. The analysis extends beyond end-of-pipe solutions to underscore the imperative for sustainable polymer design and circular economy systems, where biodegradable polymers and chemical recycling must be integrated. Crucially, we identify techno-economic analysis (TEA) and life-cycle assessment (LCA) as essential, yet underdeveloped, tools for quantifying the true cost and sustainability of management strategies. The synthesis concludes that addressing microplastic pollution requires the integrated application of chemical engineering principles across molecular, process, and system scales, and it identifies key research priorities in advanced material design, standardized analytics, hybrid treatment processes, and comprehensive impact modeling. Full article

Early prediction of casing-running sticking is essential, as the mitigation of stuck-pipe incidents often incurs significant time and economic costs. Previous studies have largely relied on purely theoretical torque and drag models that are constrained by simplified assumptions, preventing them from fully leveraging available field data and often leading to insufficient prediction accuracy. To address this challenge, we developed a hybrid mechanistic-data-driven intelligent model for hook-load prediction and casing-sticking risk assessment. The model combines mechanical models with ensemble learning algorithms, incorporating both mechanically derived parameters (theoretical hook load, casing–borehole compatibility, casing-bottom deflection and tilt angle) as well as operational and casing structural features. To evaluate its cross-field generalizability, the proposed model was trained on 13,449 samples from 14 wells across three oilfields and tested on 3961 samples from an independent well in a separate Oilfield. Three ensemble algorithms (XGBoost, Random Forest, and LightGBM) were evaluated, among which XGBoost achieved the highest predictive accuracy (RMSE = 3.50, MAE = 2.51, R² = 0.97) and was selected for subsequent friction-factor-based casing sticking risk assessment. A genetic-algorithm-based optimization framework was further developed to minimize sticking risk by optimizing the centralizer configuration under a friction constraint. The proposed sticking-risk assessment and optimization strategy was validated through field implementation. This mechanistic-data-driven intelligent model outperforms traditional theoretical approaches in predictive accuracy, interpretability, and engineering applicability, providing a practical and explainable tool for casing-running risk mitigation and design optimization in complex three-dimensional wells. Full article

This study presents a novel hybrid intelligent framework integrating fuzzy logic and artificial neural networks (ANN) to model the erosion-corrosion behavior of glass-fiber-reinforced pipes (GRP) under harsh operating conditions. Experimental data encompassing multiple operational parameters—including abrasive sand concentrations (250 g, 400 g, 500 g), flow rates (0.0067 m³/min, 0.01 m³/min, 0.015 m³/min), chlorine content (0–10 wt.%), and exposure times (1–5 h)—were utilized to develop the computational models. The fuzzy logic system effectively captured qualitative expert knowledge and uncertainty in material degradation processes, while ANN models provided quantitative predictions of erosion and corrosion rates. Results demonstrated good prediction accuracy, with R² values of 0.81 for corrosion rate and moderate prediction accuracy 0.56 for erosion rate. The analysis revealed that flow rate (correlation: 0.6) and fuzzy severity (0.6) were the most influential parameters, followed by chlorine content (0.41) and sand concentration (0.32). The hybrid model identified optimal operating conditions to minimize material degradation: low sand concentration (250 g), low flow rate (0.0067 m³/min), absence of chlorine, and shorter exposure times. This intelligent modeling approach provides a powerful tool for predictive maintenance, operational optimization, and service life prediction of GRP systems in aggressive environments, bridging the gap between experimental data and computational intelligence for enhanced material performance assessment. Full article

This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical failure data from Malatya, Türkiye. The primary predictive variables identified were pipe diameter, pipe type, pipe age, and seasonal average ambient air temperature. The MLP demonstrated superior performance compared to the other models, attaining the lowest RMSE (1.48) and the highest R² (0.993) with respect to the training data, effectively capturing the nonlinear characteristics and failure patterns. The MLP was validated using two datasets from 24 District Metered Areas (DMAs) in Sakarya and Kayseri, Türkiye. The model’s anticipated failure frequencies exhibited strong concordance with the observed failure frequencies, even in regions of elevated failure density, indicating the model’s proficiency in identifying high-risk locations and facilitating the prioritization of maintenance activities. The work demonstrates the potential of machine learning in water infrastructure management. It emphasizes the importance of employing a hybrid method with Geographic Information Systems (GISs) in future research to enhance forecast accuracy and spatial analysis. Full article

For human–robot gesture-driven control, this paper proposes a deep learning-based approach that employs both static and dynamic gestures to drive and control robots for object-grasping and delivery tasks. The method utilizes two-dimensional Convolutional Neural Networks (2D-CNNs) for static gesture recognition and a hybrid architecture combining three-dimensional Convolutional Neural Networks (3D-CNNs) and Long Short-Term Memory networks (3D-CNN+LSTM) for dynamic gesture recognition. Results on a custom gesture dataset demonstrate validation accuracies of 95.38% for static gestures and 93.18% for dynamic gestures, respectively. Then, in order to control and drive the robot to perform corresponding tasks, hand pose estimation was performed. The MediaPipe machine learning framework was first employed to extract hand feature points. These 2D feature points were then converted into 3D coordinates using a depth camera-based pose estimation method, followed by coordinate system transformation to obtain hand poses relative to the robot’s base coordinate system. Finally, an experimental platform for human–robot gesture-driven interaction was established, deploying both gesture recognition models. Four participants were invited to perform 100 trials each of gesture-driven object-grasping and delivery tasks under three lighting conditions: natural light, low light, and strong light. Experimental results show that the average success rates for completing tasks via static and dynamic gestures are no less than 96.88% and 94.63%, respectively, with task completion times consistently within 20 s. These findings demonstrate that the proposed approach enables robust vision-based robotic control through natural hand gestures, showing great prospects for human–robot collaboration applications. Full article

This study investigates the combined effects of oxygen content in the purging gas and pre-weld surface finish on the discoloration and corrosion resistance of AISI 316L pipe joints, with relevance to pipe welding where internal cleaning is constrained. The hybrid GTAW–FCAW process was used. Welds were produced at two oxygen levels (500 and 5000 ppm) and two finishes (40- vs. 60-grit). Discoloration and oxide morphology were examined by SEM/EDS, and corrosion behavior was evaluated without oxide removal using cyclic polarization and electrochemical impedance spectroscopy. The results reveal that higher oxygen levels in the purging gas produced more porous, less protective oxide layers, along with intensified oxidation around surface defects such as micro-holes. Surface roughness was also found to influence corrosion behavior: rougher surfaces exhibited higher resistance to pit initiation, whereas smoother surfaces were more susceptible to initiation but offered greater resistance to pit propagation. The corresponding governing mechanisms were identified and discussed in terms of how surface preparation affects crystallographic texture, heterogeneities and recrystallization. Taken together, the results link oxide morphology and near-surface microstructure to electrochemical response and offer practical guidance for pipe welding when internal cleaning is constrained, balancing purging control with surface preparation to preserve corrosion performance. The findings further highlight the critical roles of both purging-gas composition and surface preparation in the corrosion performance of stainless steel welded pipes. Full article

Earth–Air Heat Exchangers (EAHEs) exploit stable subsurface temperatures to pre-condition supply air. To address limitations of conventional systems in hot–arid climates, this study investigates the performance of a hybrid EAHE prototype combining a serpentine subsurface pipe with a buried water tank. Installed in a residential building in Lichana, Biskra (Algeria), the system was designed to enhance land compactness, thermal stability, and soil–water heat harvesting. Experimental monitoring was conducted across 13 intervals strategically spanning seasonal transitions and extremes and was complemented by calibrated numerical simulations. From over 30,000 data points, outlet trajectories, thermal efficiency, Coefficient of Performance (COP), and energy savings were assessed against a straight-pipe baseline. Results showed that the hybrid EAHE delivered smoother outlet profiles under moderate gradients while the baseline achieved larger instantaneous ΔT. Thermal efficiencies exceeded 90% during high-gradient episodes and averaged above 70% annually. COP values scaled with the inlet–soil gradient, ranging from 1.5 to 4.0. Cumulative recovered energy reached 80.6 kWh (3.92 kWh/day), while the heat pump electricity referred to a temperature-dependent ASHP totaled 34.59 kWh (1.40 kWh/day). Accounting for the EAHE fan yields a net saving of 25.46 kWh across the campaign, only one interval (5) was net-negative, underscoring the value of bypass/fan shut-off under weak gradients. Overall, the hybrid EAHE emerges as a footprint-efficient option for arid housing, provided operation is dynamically controlled. Future work will focus on controlling logic and soil–moisture interactions to maximize net performance. Full article

Aiming to address the frequent design conflicts arising during multi-disciplinary collaboration in the detailed design phase of large cruise ships, coupled with the inadequacy of traditional methods in detecting unknown constraints, this paper proposes a hybrid conflict detection framework integrating interval propagation with intelligent algorithms. First, using the piping design of a cruise ship’s water supply system as a typical scenario, design constraints are categorized into known and unknown sets. For known constraints, the interval propagation algorithm is employed for rapid inference and verification. For unknown constraints that are difficult to express explicitly, an improved particle swarm optimization (IPSO) algorithm is proposed to optimize the parameters of a radial basis function (RBF) neural network, thereby constructing an IPSO-RBF conflict detection model. Case studies demonstrate the interval propagation algorithm’s efficacy in identifying conflicts within water supply pipeline designs. Concurrently, testing against historical design datasets reveals that the IPSO-RBF model outperforms multiple comparative models, including PSO-RBF, AFSA-RBF, etc., in terms of conflict detection accuracy, precision, and recall. This validates the method’s effectiveness and superiority in resolving design conflicts within complex systems for large cruise ships. Full article

Accurate prediction of gas pipeline incidents through risk factor interdependencies is critical for proactive safety management. This study develops a hybrid SARIMA–association rule mining (ARM) framework integrating time-series forecasting with causal pattern decoding, using 60-month U.S. pipeline incident records (2010–2024) from the Pipeline and Hazardous Materials Safety Administration (PHMSA) database, covering leaks, mechanical punctures, and ruptures. Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling with six-month rolling-window validation achieves precise leak forecasts (MAPE = 14.13%, MASE = 0.27) and reasonable mechanical damage predictions (MAPE = 31.21%, MASE = 1.15), while ruptures exhibit pronounced stochasticity. Crucially, SARIMA incident probabilities feed Apriori-based ARM, revealing three failure-specific mechanisms: (1) ruptures predominantly originate from natural force damage, with underground cases causing economic losses (lift = 3.70) and aboveground class 3 incidents exhibiting winter daytime ignition risks (lift = 2.37); (2) leaks correlate with equipment degradation, where outdoor meter assemblies account for 69.7% of fire-triggering cases (108/155 incidents) and corrosion dominates >50-year-old pipelines; (3) mechanical punctures cluster in pipelines <20 years during spring excavation, predominantly occurring in class 2 zones due to heightened construction activity. These findings necessitate cause-specific maintenance protocols that integrate material degradation laws and dynamic failure patterns, providing a decision framework for pipe replacement prioritization and seasonal monitoring in high-risk zones. Full article

Thermosyphon heat pipes (THPs) are increasingly employed in advanced thermal management applications due to their highly effective thermal conductivity, compact design, and passive operation. In this study, a numerical investigation was conducted on a copper or aluminum thermosyphon charged with different working fluids, with methanol serving as a reference case. A two-dimensional compressible CFD model was implemented in OpenFOAM, coupling the Volume of Fluid (VOF) method with a hybrid phase-change formulation that integrates the Lee and Tanasawa approaches. It provides, indeed, a balance between computational efficiency and physical fidelity. The vapor flow, considered as an ideal gas, was assumed compressible. The isoAdvector algorithm was applied as a reconstruction technique in order to improve interface capturing, to reduce spurious oscillations and parasitic currents, and to ensure more realistic simulation of boiling and condensation phenomena. The performance dependency on operating parameters such as the inclination angle, liquid filling ratio, and thermophysical properties of the working fluid is analyzed. The numerical predictions were validated against experimental measurements obtained from a dedicated test bench, showing discrepancies below 3% under vertical operation. This work provides new insights into the coupled influence of orientation, fluid inventory, and working fluid properties on THP behavior. Beyond the experimental validation, it establishes a robust computational framework for predicting two-phase heat and mass transfer phenomena by linearizing and treating the terms involved in thebalances to be satisfied implicitly. The results reveal a strong interplay between the inclination angle and filling ratio in determining the overall thermal resistance. At low filling ratios, the vertical operation led to insufficient liquid return and increased resistance, whereas inclined orientations enhanced the liquid spreading and promoted more efficient evaporation. An optimal filling ratio range of 40–60% was identified, minimizing the thermal resistance across the working fluids. In contrast, excessive liquid charge reduced the vapor space and degraded the performance due toflow restriction and evaporationflooding. Full article

Thermally activated building systems (TABS) rely on high thermal mass materials, such as concrete, which perform well thermally but have a high carbon footprint. This study systematically investigates the thermal behavior of bio-based materials—spruce, pine, beech, and oak—in TABS using numerical simulations, comparing them with conventional and hybrid materials like concrete and clay. A total of 120 variants were simulated with different pipe diameters, spacing, embedment depths, and inlet temperatures. Thermal properties, particularly thermal conductivity and specific heat capacity, significantly influenced component activation efficiency. Concrete exhibited a characteristic cooling time of 71 h at an inlet temperature of 26 °C (pipe diameter 16 mm), while pine reached 80 h under the same conditions. The use of capillary tube mats extended the cooling times to 75 h for concrete and 92 h for pine. Although concrete provides the best thermal performance, certain bio-based materials achieve comparable results under optimized conditions. Hybrid systems with mineral components offer additional potential for improvement. These findings demonstrate that ecologically sustainable component activation using bio-based materials is feasible with only moderate efficiency losses compared to mineral-based systems, provided system parameters are appropriately adapted. Full article

Urban water infrastructure faces increasing deterioration, necessitating accurate, cost-effective condition assessment. Traditional inspection techniques are intrusive and inefficient, creating demand for scalable machine learning (ML) solutions. This study develops a hybrid ML meta-model to predict underground pipe conditions using a comprehensive dataset of 11,544 records. The objective is to enhance multi-class classification performance while preserving interpretability. A stacked hybrid architecture was employed, integrating Random Forest, LightGBM, and CatBoost models. Following data preprocessing, feature engineering, and correlation analysis, the neural network-based stacking meta-model achieves 96.67% accuracy, surpassing individual base learners while delivering enhanced robustness through model diversity, improved probability calibration, and consistent performance on challenging intermediate condition classes, which are essential for condition prioritization. Age emerged as the most influential feature, followed by length, material type, and diameter. ROC-AUC scores ranged from 0.894 to 0.998 across all models and classes, confirming high discriminative capability. This work demonstrates hybrid architectures for infrastructure diagnostics. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

This paper introduces a novel 3D simulation framework that integrates the Pipe-in-Pipe (PiP) model with Finite Element Analysis (FEA) using Ansys Parametric Design Language (APDL). This framework is designed to incorporate a 3D building model directly, assessing ground-borne vibrations from metro tunnels and their impact on surrounding structures. The PiP model efficiently calculates displacement fields around tunnels in full-space, applying the resulting fictitious forces to the FEA model, which includes a directly coupled 3D building model. This integration allows for precise simulation of vibration propagation through soil into buildings. A comprehensive verification test confirmed the model’s accuracy and reliability, demonstrating that the hybrid PiP-FEA model achieves significant computational savings-approximately 40% in time and 65% in memory usage-compared to the traditional full 3D FEA model. The results exhibit strong agreement between the PiP-FEA and full FEA models across a frequency range of 1–250 Hz, with less than 1% deviation, highlighting the effectiveness of the PiP-FEA approach in capturing the dynamic behavior of ground-borne vibrations. Additionally, the methodology developed in this paper extends beyond the specific case study presented and shows potential for application to various urban vibration scenarios. While the current validation is limited to numerical comparisons, future work will incorporate field data to further support the framework’s applicability under real metro-induced vibration conditions. Full article