Search Results (189)

Hydrogen-compatible elastomeric seals are critical for the reliability and safety of high-pressure hydrogen infrastructure. However, hydrogen exposure can alter the mechanical response and surface condition of elastomeric materials through coupled transport–mechanical interactions. This study presents a comparative experimental and data-driven investigation of the pressure-dependent degradation behavior of ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), and fluorocarbon elastomer (FKM) O-ring seals following 192 h exposure to hydrogen pressures ranging from 800 to 7000 psi at room temperature. Tensile testing was performed directly on complete O-ring geometries, and descriptor-based analysis was used to quantify peak-response behavior, energy absorption, stiffness evolution, and normalized deformation characteristics. Multivariate statistical methods, principal component analysis (PCA), clustering analysis, and Random Forest regression were applied to identify material-specific degradation patterns. NBR maintained the highest overall load-bearing capability and stiffness-related response across the investigated pressure range, whereas EPDM exhibited more compliant and non-monotonic deformation behavior. FKM showed the strongest pressure sensitivity, with substantial increases in force- and stiffness-related descriptors at elevated hydrogen pressures. Optical image analysis revealed pronounced increases in defect density and defect area fraction for NBR, while FKM exhibited comparatively stable surface-state behavior. PCA and clustering analyses identified distinct material-dependent degradation trajectories, and Random Forest regression achieved an R² value of 0.888 for energy-absorption prediction. The results demonstrate that hydrogen-induced degradation emerges through coupled interactions among stiffness evolution, deformation progression, energy absorption, and surface-state changes, providing a comparative framework for assessing elastomer performance in hydrogen environments. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Cavitation in machine elements is often accompanied by surface degradation, material loss, and a reduction in functional performance and reliability. Despite extensive research on cavitation in hydraulic systems, its role in the behavior and durability of machine elements remains insufficiently addressed. This paper presents a critical review of cavitation and cavitation-induced erosion in machine elements, based on an analysis of relevant literature and standards. The study covers different types of components, including gears, plain and rolling bearings, and seals, with particular attention to the mechanisms of damage and the methods used for their investigation. The analysis shows that, although the fundamental mechanisms of cavitation are well understood and standardized testing methods are available, their application to machine elements is limited. Existing standards are not sufficiently adapted to specific components, while current numerical and experimental approaches rarely provide a direct link between cavitation phenomena and material degradation. The findings indicate the need for improved standardization, development of integrated modelling approaches, and a closer connection between cavitation mechanisms and the performance characteristics of machine elements. The presented analysis is relevant for design, reliability assessment, maintenance strategies, and the development of cavitation-resistant machine components in hydraulic and mechanical systems. Full article

This investigation examines the novel application of bacterial concrete as a sustainable substitute for traditional concrete within the construction sector, utilizing bibliometric analysis in conjunction with machine learning. The main aim of the study is to gain insights into the application and potential benefits of using bio-based concrete in the construction industry. A comprehensive search of all publications indexed in Scopus was carried out for the period spanning from 2020 to 14 March 2025, followed by meticulous screening and extraction of relevant documents. The dataset obtained from Scopus was processed in strict accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to uphold transparency and replicability throughout the systematic review process. A descriptive analysis was undertaken to evaluate publication trends over time. The research on bio-concrete combined with machine learning is highly concentrated in Asia, Europe, and the USA; in contrast, vast areas of Africa show no research output regarding self-healing concrete based on this data extraction. Various types of bacteria, including Bacillus species, are explored for their calcium carbonate precipitation capabilities in this review. Microbial-induced calcite precipitation process reduces carbon emissions associated with cement production and extends concrete lifespan by sealing cracks. Full article

Lost circulation remains a major challenge during well construction, often leading to non-productive time, increased material consumption, and additional treatment costs. In field practice, the selection of lost circulation materials (LCMs) is still largely based on empirical rules or laboratory testing; however, these approaches are not always suitable for rapid decision-making under variable downhole conditions. This study presents a physics-guided surrogate modeling framework for predicting fracture sealing performance and supporting injection strategy selection. The approach combines laboratory observations with coupled computational fluid dynamics and discrete element method (CFD-DEM) simulations to represent both measured behavior and a broader range of mechanically consistent sealing scenarios. The final dataset included 300 cases, comprising 45 physical experiments and 255 CFD-DEM-generated synthetic cases. A hybrid machine learning architecture based on Temporal Convolutional Networks and Artificial Neural Networks was developed to predict sealing pressure under different material and fluid conditions. The model achieved an R² of 0.89 and a mean absolute percentage error of 6.4%, while showing 94% agreement with laboratory-based recommendations for injection strategy. The proposed framework can therefore serve as a rapid engineering support tool for preliminary formulation screening and a more computationally efficient digital workflow for fracture sealing design in drilling operations. Full article

This study investigates the flow stress behavior of Z-shaped steel wire used in cable sealing applications, over a temperature range of 20–500 °C and a strain rate range of 10⁻⁴ to 3000 s⁻¹. The primary objective is to establish reliable constitutive data to support accurate numerical simulations in relevant engineering contexts. To this end, quasi-static tensile tests, high-temperature tensile tests, and high-strain-rate dynamic compression tests were conducted using a high–low temperature electronic universal testing machine and a split Hopkinson pressure bar system. The true stress–strain responses were obtained, and the corresponding mechanical properties were systematically analyzed. Experimental results show that at room temperature (20 °C) and within the low strain rate range (10⁻⁴–10⁻¹ s⁻¹), the flow stress is insensitive to strain rate variations. However, following yielding, the slope of the flow stress curve increases noticeably with accumulating strain, indicating deformation behavior governed predominantly by strain hardening. Under high-strain-rate conditions at room temperature (20 °C, 10² to 10³ s⁻¹), the yield stress increases with increasing strain rate, revealing a pronounced strain rate sensitivity. At elevated temperatures combined with a low strain rate (300–500 °C, 10⁻³ s⁻¹), both the yield stress and the overall flow stress decrease markedly as the temperature rises, demonstrating significant thermal softening behavior. The microstructure and fracture of Z4 steel wire were observed by SEM to systematically investigate the effects of strain rate and temperature on its microstructural characteristics, thereby revealing the micro-mechanism of the material’s flow stress. Based on these experimental observations, a Johnson–Cook constitutive model was developed for the Z-shaped steel wire used in cable sealing applications. Validation results confirm that the model accurately captures the flow stress evolution of the material under coupled temperature and strain rate conditions. Full article

The reliable joining of thermoplastic composites is a critical requirement in modern manufacturing, where achieving zero-leakage joints is essential. For this application, ultrasonic welding is a highly efficient technology. Traditionally, standard heuristic methods and static experimental designs are used to optimize machine parameters. However, the process exhibits high stochastic variability due to complex, nonlinear thermomechanical interactions, which significantly influence the final seal quality and the reliability of the entire production system. This paper presents a practical prediction-optimization framework using a hybrid stacking ensemble neural network to process welding data. To improve the accuracy and stability of the manufacturing process, the predictive model is integrated with a Monte Carlo simulation. Evaluation showed that the proposed framework achieved the best performance among the evaluated benchmark models, with a coefficient of determination R² = 0.8523 and a mean absolute error MAE = 0.7224. The proposed framework identifies candidate optimized machine parameters in a simulation-based workflow and defines stable operating conditions for subsequent experimental validation, providing a bounded data-driven approach for minimizing leakage in ultrasonic welding. Full article

Breakthrough pressure (BP) is a key parameter for evaluating the sealing capacity of shale caprocks, whereas direct laboratory measurements are time-consuming and costly, limiting their use in continuous regional assessment. This study develops a conventional-log-based workflow for BP prediction in the Quan-4 Member (K1q4) caprock underlying the Qingshankou shale oil interval in the Gulong Sag, Songliao Basin. Four routinely available logs—gamma ray (GR), acoustic interval transit time (AC), bulk density (DEN), and compensated neutron log (CNL)—were integrated with core-measured BP data. A GR-AC multiple-regression baseline and five machine-learning algorithms, including stochastic gradient descent (SGD), extremely randomized trees (ERT), Random Forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), were compared under a unified workflow. The training set was used for normalization, model fitting, grid search, and internal five-fold cross-validation, whereas the held-out test set and external prediction wells were kept separate for performance evaluation. The results show that BP generally increases with GR and DEN and decreases with AC and CNL, indicating that clay content, compaction, and pore connectivity jointly control the logging response of caprock sealing capacity. Among the evaluated models, AdaBoost achieved the best overall performance, with RMSE, MAE, and R² values of 1.33 MPa, 0.97 MPa, and 0.89 on the held-out test set, and 1.19 MPa, 0.92 MPa, and 0.94 in external prediction wells. Train–test diagnostics, learning curves, and SHAP analysis indicate stable performance and physically plausible feature contributions within the present dataset. The proposed workflow can therefore provide a practical supplement to laboratory BP measurements for caprock evaluation in the study area, although broader application still requires well-level independent validation and explicit prediction-uncertainty quantification. Full article

Carotid artery disease, including atherosclerotic stenosis and non-atherosclerotic abnormalities, substantially increases ischemic stroke risk and motivates accessible tools for early screening. Current diagnostic pathways rely on clinic-based imaging and skilled operators, creating barriers to frequent monitoring and scalable deployment. We present a non-invasive diagnostic approach using a wearable MEMS accelerometer patch to capture mechano-acoustic vibrations generated by carotid blood flow at the neck. The miniature device integrates a hermetically sealed wideband accelerometer with out-of-plane sensitivity and micro-g resolution to detect subtle flow-induced vibrations. We validated the approach in a carotid flow phantom and a clinical study of 74 patients. Time–frequency representations were computed using the continuous wavelet transform (CWT), from which interpretable spectral and scalogram-derived candidate biomarkers were extracted. Six non-redundant features were then selected for multivariate classification, distinguishing pathology, defined as 50% or greater stenosis or a non-atherosclerotic abnormality, from non-pathology, defined as less than 50% stenosis. Finally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each biomarker to predicted disease probability. These findings resulted in an AUROC of 0.97 and AUPR of 0.947, with 81.7% sensitivity and 93.6% specificity at the prespecified threshold (precision 85.4%, F1 83.5%, accuracy 89.8%), highlighting the potential of wearable seismic sensing combined with interpretable machine learning for fast screening and longitudinal monitoring of the right and left carotid arteries. Full article

Due to excellent performance, polytetrafluoroethylene (PTFE), being sealing material, is widely used in chemical engineering, aerospace engineering, mechanical engineering, civil engineering, energy engineering and other sectors. However, due to obvious temperature drops in supplying or storing fluids, the mechanical behavior of PTFE under cryogenic conditions is still unclear. In this study, the creep mechanical performance of PTFE gaskets after cryogenic aging in liquid oxygen is experimentally investigated. The circular PTFE gasket samples are immersed into liquid oxygen for cryogenic aging treatment. The universal testing machine is utilized for material mechanic tests. Three different load levels, including 10 MPa, 15 MPa and 20 MPa, are designed and accounted for. It is found that the creep strain of PTFE exhibits three typical stages, namely the initial rapid increase phase, transition phase with a reducing growth rate, and stable linear growth phase. Moderate cryogenic immersion aging can effectively improve the creep resistance of PTFE, but excessive aging treatments will lead to mechanical property degradation of PTFE. The Burgers life prediction model is improved by introducing a nonlinear correction term, which can accurately predict the creep behavior of PTFE under different aging states. The present study can provide experimental evidence and a theoretical basis for a deep understanding of the mechanical response of PTFE materials under extreme cryogenic intermittent service conditions. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

MOS gas sensors have proven to be useful in electronic noses, which utilize these sensors to detect volatile organic compounds in human breath to detect various lung diseases. Unfortunately, the long settling time of MOS gas sensors is ill-suited to measuring human breath, where complete breathing cycles are often shorter than 5 s. Existing studies circumvent this limitation by collecting gas samples and injecting them into a sealed chamber to react with the sensors. However, it would be convenient if breath-by-breath analysis could be conducted without the need to store breath samples. To accomplish this, we present a novel forecasting methodology to predict the final value $t_{\infty }$ of a gas sensor’s response based on its initial transient behavior. To do this, we present and validate a second-order mathematical model of the sensors’ response characteristics, which we then use in our preliminary work using neural networks to predict the final sensor value. Although some challenges were encountered, the initial results are encouraging, and we plan to extend our study in the future to collect a more expansive dataset and explore the use of other types of machine learning algorithms for this application. Full article

Since September 2021, numerous seismic events with spectral peaks below 1 Hz occurred on the island of Vulcano, Italy, 131 years after its last eruption. The local monitoring network recorded microseismicity mostly in the form of months-long swarms, concurrent with anomalous values of other geophysical and geochemical parameters. By applying a machine learning technique (Self-Organizing Maps, SOMs), we obtained an inventory of ~6600 seismic signals, identifying and separating exogenous signals (anthropic noise) from distinct families of events. These families were located below La Fossa Crater (where the last eruption of the volcano happened) from the surface to a depth of 2.2 km b.s.l. Based on the seismic signature and source location of these events, we hypothesize unsealed/sealed processes through a network of shallow fractures favored by fluid pressure. After the return to background values of geochemical and geophysical parameters in 2023, a resumption of microseismicity occurred between May and June 2024. A test application of the SOM to the new data confirmed the non-destructive source of the new recorded signals, which shared families, location, and depths with our previous inventory. This test showed that SOM can be an effective tool for supporting real-time monitoring and warning of future unrest at Vulcano. Full article

The research explored the automation production lines for the bottling of particulate materials in the pharmaceutical industries, covering the integrated processes of loading bottles, filling with particles, sealing, screwing on caps, quality inspection, and storage. The hardware system of the project consists of programmable logic controllers(PLCs), edge servers, motion control equipment, industrial cameras, and mechanical grippers for handling and storage. The aim of this research is to assist the manufacturing industry in transitioning from traditional production models to digital and intelligent production methods. From the perspective of core components, it analyzed and expounded the key technologies for building a digital production line; at the same time, from the perspective of data collection and processing, it clarified the role and advantages of the cloud platform. The product packaging process simulation covers loading bottles, filling with particle materials, sealing, screwing on caps, quality inspection, and storage. The production line issues production instructions and scheduling plans through the human-machine interaction interface and the cloud platform. Full article

The steady operation of underground gas storage (UGS) is significant for securing national energy. However, long-term cyclic injection-production operation causes the dynamic changes in formation stress, potentially leading to fault reactivation and slippage. This could affect the seal performance of the fault zone and cause disastrous consequences. In this paper, a mechanical analysis model for fault slip is constructed to study the dynamic seal performance in response to long-term injection-production cycles. An intelligent approach is proposed to predicate the fault slip value based on machine learning algorithms. It can realize long-term prediction of fault slip value under a new condition of injection-production operation. The study shows that (1) formation pressure tends to accumulate near the fault zone due to the low permeability, and the interface of the reservoir layer, cap layer, and fault zone is the seal weak position of UGS; (2) the response of fault slip is driven by the injection-production rate and the reservoir pressure. There is a significant coupling relationship between the fault slip value and the accumulated injection gas volume; (3) the intelligent prediction approach can capture the nonlinear dynamic characteristics of slip tendency accurately, and it exhibits good prediction performance and generalization ability under the new operating condition. This study effectively assesses the dynamic risk for fault slip of depleted hydrocarbon reservoir UGS during the long-term injection-production procedure. It provides an effective technical approach for fault slip tendency analysis and injection-production process optimization, which is important for the sustainable operation of UGS reducing the risk of seal failure and supporting gas storage security. Full article