Search Results (418)

A novel right-sidewall gas blowing method is proposed to improve the flow behavior in a single-strand tundish. Despite advances in tundish flow control, the impact of slag layers and sidewall gas injection on flow dynamics and tracer transport remains underexplored. This study combines 1:3.57 scale water model experiments and Compuational Fluid Dynamics (CFD) simulations to investigate the effects of gas injection heights (50 mm and 100 mm) on flow structure, mixing efficiency, and slag layer interactions. Particle Image Velocimetry (PIV) and the stimulus-response method are used for quantitative validation. Results show that sidewall gas blowing suppresses short-circuit flow, increases average residence time by up to 37%, and reduces dead zone volume by up to 19%. The 50 mm blowing height induces stronger surface turbulence, while the 100 mm height improves flow uniformity. The presence of a slag layer significantly dampens surface fluctuations and alters vortex formation. These findings fill a critical research gap in tundish metallurgy and offer a practical reference for optimizing gas blowing strategies in industrial applications. Full article

Waterflooding, a key method for secondary hydrocarbon recovery, has been employed since the early 20th century. Over time, the role of water chemistry and ions in recovery has been studied extensively. Low-salinity water (LSW) injection, a common technique since the 1930s, improves oil recovery by altering the wettability of reservoir rocks and reducing residual oil saturation. Recent developments emphasize the integration of LSW with various recovery methods such as CO₂ injections, surfactants, alkali, polymers, and nanoparticles (NPs). This article offers a comprehensive perspective on how LSW injection is combined with these enhanced oil recovery (EOR) techniques, with a focus on improving oil displacement and recovery efficiency. Surfactants enhance the effectiveness of LSW by lowering interfacial tension (IFT) and improving wettability, while ASP flooding helps reduce surfactant loss and promotes in situ soap formation. Polymer injections boost oil recovery by increasing fluid viscosity and improving sweep efficiency. Nevertheless, challenges such as fine migration and unstable flow persist, requiring additional optimization. The combination of LSW with nanoparticles has shown potential in modifying wettability, adjusting viscosity, and stabilizing emulsions through careful concentration management to prevent or reduce formation damage. Finally, building on discussions around the underlying mechanisms involved in improved oil recovery and the challenges associated with each approach, this article highlights their prospects for future research and field implementation. By combining LSW with advanced EOR techniques, the oil industry can improve recovery efficiency while addressing both environmental and operational challenges. Full article

The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of classic continuous improvement tools (TPM and SMED) with advanced data science techniques (machine learning) forms a synergistic approach capable of significantly increasing operational efficiency in manufacturing environments. The study was conducted at a Peruvian plastic container manufacturing company with a first overall equipment efficiency (OEE) of 61.87%, affected by low availability of injection and blow molding machines and a high rework rate. Total Productive Maintenance (TPM) strategies were implemented to improve equipment maintenance, the SMED method to reduce setup times, and a machine learning model to predict defects and burs in products. The effectiveness of the approach was confirmed through simulations in Arena and analysis of historical data. As a result, OEE increased to 80.86%, reducing downtime and rework. In conclusion, this study shows that the combination of TPM, SMED, and machine learning not only improves operational performance but also offers a replicable and robust methodological framework for process optimization in the manufacturing industry. Full article

Combustion instability poses a significant challenge in aerospace propulsion systems, particularly in afterburners that employ bluff-body flame stabilizers. The flame transfer function (FTF) is essential for characterizing the dynamic response of flames to perturbations, which is critical for predicting and controlling these instabilities. This study experimentally investigates the effect of varying the number of fuel injection holes (N = 3, 4, 5, 6) on the FTF and flame dynamics in a model afterburner combustor. Using acoustic excitations, the FTF was measured across a range of frequencies, with flame behavior analyzed via high-speed imaging and chemiluminescence techniques. Results reveal that the FTF gain exhibits dual-peak characteristics, initially decreasing and then increasing with higher N values. The frequencies of these gain peaks shift to higher values as N increases, while the time delay between velocity and heat release rate fluctuations decreases, indicating a faster flame response. Flame morphology analysis shows that higher N leads to shorter, taller flames due to enhanced fuel distribution and mixing. Detailed examination of flame dynamics indicates that different pulsation modes dominate at various frequencies, elucidating the observed FTF behavior. This research provides novel insights into the optimization of fuel injection configurations to enhance combustion stability in afterburners, advancing the development of more reliable and efficient aerospace propulsion systems. Full article

As global temperatures continue to rise, surpassing the +2.5 °C threshold under current emissions scenarios, the urgency for sustainable, effective carbon management strategies has intensified. Geological carbon storage (GCS) has been explored as a potential mitigation tool; however, its large-scale feasibility remains highly uncertain due to concerns over storage permanence, leakage risks, and economic viability. This study proposes three advanced deep learning models—DeepDropNet, GateSeqNet, and RecurChainNet—to predict the Residual Trapping Index (RTI) and Solubility Trapping Index (STI) with enhanced accuracy and computational efficiency. Using a dataset of over 2000 high-fidelity simulation records, the models capture complex nonlinear relationships between key reservoir properties. Results indicate that GateSeqNet achieved the highest predictive accuracy, with an R² of 0.95 for RTI and 0.93 for STI, outperforming both DeepDropNet and RecurChainNet. Ablation tests reveal that excluding post injection and injection rate significantly reduced model performance, decreasing R² by up to 90% in RTI predictions. The proposed models provide a computationally efficient alternative to traditional numerical simulations, which makes them viable for real-time CO₂ sequestration assessment. This work advances AI-driven carbon sequestration strategies, offering robust tools for optimizing long-term CO₂ storage performance in geological formations and for achieving global sustainability goals. Full article

This study aimed to investigate the effects of diesel/ethanol/n-butanol mixed fuel on the marine diesel engine combustion and emissions at different ethanol blending ratios, different single injection times, and pre-injection times. In addition, this study takes the injector fault phenomenon as an example, simulates the three fault phenomena of the injector, and uses a variety of algorithms to optimize the probabilistic neural network model to achieve the fault state identification and diagnosis of the injector. The results of research showed that, with the increase in the ethanol blending ratio, the peak cylinder pressure shows a decreasing trend. The ignition delay period is extended, and the peak instantaneous heat release rate increases. Compared with D100, the nitrogen oxide (NO_x) emissions of D50E40B10 mixed fuel are reduced by 12.3%, soot emissions are reduced by 29.18%, and carbon monoxide (CO) emissions are increased by 5.7 times. With the injection time advances, the peak values of cylinder pressure and heat release rate show an increasing trend, soot emissions gradually decrease, and NO_x and CO emissions gradually increase. The peaks of the cylinder pressure and heat release rate in the pilot injection stage gradually decrease as the pilot injection time advances, while the peak heat release rate in the main injection stage increases. In terms of emissions, NO_x emissions first decrease and then increase as the pilot injection time advances, while soot emissions gradually increase. The average accuracy of the PSO-PNN neural network model reaches 90%, and the average accuracy of the WOA-PNN neural network model reaches 95%. Therefore, the WOA-PNN neural network model is determined to be the optimal injector fault diagnosis model, which can be applied to the identification and diagnosis of injector fault states of diesel engines. Full article

This field study describes the successful implementation and evaluation of a Polymer-free Delayed Acid System, a next-generation acid retarder system that is chemically superior to traditional emulsified acid systems with an amphoteric-based surfactant. It is a polymer-free system that stimulates ultra-tight carbonate reservoirs in extreme sour and high-temperature conditions. The candidate well, located in an onshore gulf region field, for a major oil and gas company demonstrated chronically unstable production behavior for over two years, with test volumes fluctuating unpredictably between 200 and 400 barrels of oil per day. This indicated severe near-wellbore damage, high skin, and limited matrix permeability (<0.3 mD). The well was chosen for a pilot trial of the Polymer-free Delayed Acid System technology after a thorough formation study, which included mineralogical characterization and capillary diagnostics. The innovative acid retarder formulation, designed for deep matrix penetration and controlled acid–rock reaction, uses intrinsic encapsulation kinetics to significantly increase the acid’s reactivity, allowing it to bypass damaged zones, minimize acid leak-off, and initiate dominant wormhole propagation into the tight formation. The stimulation procedure began with a custom pre-flush designed to change nanoscale wettability and interfacial tension, so increasing acid displacement and assuring effective contact with the formation rock. Real-time injectivity testing and operational data collecting were performed prior to, during, and following the acid job, with pre-stimulation injectivity peaking at 1.2 bpm, indicating poor formation conductivity. Treatment with the Polymer-free Delayed Acid System resulted in a 592% increase in post-stimulation injectivity, indicating significant increases in near-wellbore permeability and successful propagation. However, a substantial operational difficulty arose: the well remained shut down for more than two months following the acid stimulation work due to surface infrastructure delays, notably the scheduling and execution of a flowline cleanup campaign. This lengthy closure slowed immediate flowback analysis and impeded direct assessment of treatment performance because production could not be tracked in real time. Despite this, once the surface system was operational and the well was open to flow, a structured production testing program was carried out over four quarterly intervals. The well regularly produced at an average stable rate of 500 bbl/day, more than doubling pre-treatment performance and demonstrating the long-term effectiveness and mechanical durability of the acid-induced wormhole network. Despite the post-job shut-in, the Polymer-free Delayed Acid System maintained the stimulating impact even under non-ideal settings, demonstrating its robustness. The Polymer-free Delayed Acid System outperforms conventional emulsified acid systems, giving better control over acid placement and reactivity, especially under severe reservoir conditions with bottomhole temperatures reaching 200 °F. This project offers a field-proven methodology that combines advanced chemical engineering, formation-specific design, and live diagnostics, as well as a scalable blueprint for unlocking hydrocarbon potential in similarly complicated, low-permeability reservoirs. Full article

To describe a technique for managing acute corneal hydrops in eyes with keratoconus using dome stromal drainage with intracameral air injection under real-time three-dimensional (3D) microscope-integrated optical coherence tomography (OCT) guidance. We describe a retrospective case series of six eyes from six patients with keratoconus who developed acute corneal hydrops. All eyes underwent intracameral air injection with controlled dome puncture for stromal fluid drainage, without the use of sutures. The procedure was performed using a 3D visualization system that enables integrated and simultaneous viewing of the surgical field and intraoperative OCT scan (a 3D digitally assisted visualization system that displayed a split-screen view of the surgical field and OCT cross-sections simultaneously). Postoperative resolution of edema and improvement in clarity were documented. The resolution of corneal edema allowed for subsequent mushroom-shaped penetrating keratoplasty performed with a femtosecond laser in four eyes of four patients. All six eyes showed significant resolution of corneal edema within 2 to 4 weeks. Stromal clefts collapsed rapidly after drainage. In each case, the thick edema was reduced to a confined leucoma. No intraoperative or postoperative complications were observed. All four eyes that underwent a femtosecond laser-assisted mushroom-shaped penetrating keratoplasty showed optimal anatomical and functional success. Air-assisted dome drainage, combined with simultaneous 3D and OCT visualization, is a safe and effective technique for treating acute corneal hydrops. This technology enables real-time decision-making and enhances surgical precision, opening the door to advanced procedures that are otherwise limited by corneal opacity. Full article

Efficient simulation of multiphase, multicomponent fluid flow in heterogeneous reservoirs is critical for optimizing hydrocarbon recovery. In this study, we investigate advanced linearization techniques for fully implicit compositional reservoir simulations, a problem characterized by highly nonlinear governing equations that challenge both accuracy and computational efficiency. We implement four methods—finite backward difference (FDB), finite central difference (FDC), operator-based linearization (OBL), and residual accelerated Jacobian (RAJ)—within an MPI-based parallel framework and benchmark their performance against a legacy simulator across three test cases: (i) a five-component hydrocarbon gas field with CO₂ injection, (ii) a ten-component gas field with CO₂ injection, and (iii) a ten-component gas field case without injection. Key quantitative findings include: in the five-component case, OBL achieved convergence with only 770 nonlinear iterations (compared to 841–843 for other methods) and reduced operator computation time to 9.6 of total simulation time, highlighting its speed for simpler systems; in contrast, for the more complex ten-component injection, FDB proved most robust with 706 nonlinear iterations versus 723 for RAJ, while OBL failed to converge; in noninjection scenarios, RAJ effectively captured nonlinear dynamics with comparable iteration counts but lower overall computational expense. These results demonstrate that the optimal linearization strategy is context-dependent—OBL is advantageous for simpler problems requiring rapid solutions, whereas FDB and RAJ are preferable for complex systems demanding higher accuracy. The novelty of this work lies in integrating these advanced linearization schemes into a scalable, parallel simulation framework and providing a comprehensive, quantitative comparison that extends beyond previous efforts in reservoir simulation literature. Full article

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are improved using fuzzy logic and AI for rapid changeover optimization on the NEGRI BOSSI 650 machine. A decrease in downtime by 65% and an improvement in the Process Cycle Efficiency by 46.8% followed the identification of bottlenecks, externalizing tasks, and streamlining workflows. AI-driven analysis could make on-the-fly adjustments, which would ensure that resources are better allocated, and thus sustainable performance is maintained. The findings highlight how integrating Lean methods with advanced technologies enhances operational agility and competitiveness, offering a scalable model for continuous improvement in industrial settings. Full article

A mass abatement scalable system through managed aquifer recharge (MAR-MASS) improves mass extraction from groundwater with a variable-density flow. This method is superior to conventional injection systems because it promotes uniform mass displacement, reduces density gradients, and increases mass extraction efficiency over time. Simulations of various scenarios involving hydrogeologic variables, including hydraulic conductivity, vertical anisotropy, specific yield, mechanical dispersion, molecular diffusion, and mass concentration in aquifers, have identified critical variables and parameters influencing mass transport interactions to optimize the system. MAR-MASS is adaptable across hydrogeologic conditions in aquifers that are 25–75 m thick, comprising unconsolidated materials with hydraulic conductivities between 5 and 100 m/d. It is effective in scenarios near coastal areas or in aquifers with variable-density flows within the continent, with mass concentrations of salts or solutes ranging from 3.5 to 35 kg/m³. This system employs a modular approach that offers scalable and adaptable solutions for mass extraction at specific locations. The integration of programming tools, such as Python 3.13.2, along with technological strategies utilizing parallelization techniques and high-performance computing, has facilitated the development and validation of MAR-MASS in mass extraction with remarkable efficiency. This study confirmed the utility of these tools for performing calculations, analyzing information, and managing databases in hydrogeologic models. Combining these technologies is critical for achieving precise and efficient results that would not be achievable without them, emphasizing the importance of an advanced technological approach in high-level hydrogeologic research. By enhancing groundwater quality within a comparatively short time frame, expanding freshwater availability, and supporting sustainable aquifer recharge practices, MAR-MASS is essential for improving water resource management. Full article

Acoustic tweezers, as advanced micro/nano manipulation tools, play a pivotal role in biomedical engineering, microfluidics, and precision manufacturing. However, piezoelectric-based acoustic tweezers face performance limitations due to multi-physical coupling effects during microfabrication. This study proposes a novel approach using injection molding with embedded electronics (IMEs) technology to fabricate piezoelectric micro-ultrasonic transducers with micron-scale precision, addressing the critical issue of acoustic node displacement caused by thermal–mechanical coupling in injection molding—a problem that impairs wave transmission efficiency and operational stability. To optimize the IME process parameters, a hybrid multi-objective optimization framework integrating NSGA-II and MOPSO is developed, aiming to simultaneously minimize acoustic node displacement, volumetric shrinkage, and residual stress distribution. Key process variables—packing pressure (80–120 MPa), melt temperature (230–280 °C), and packing time (15–30 s)—are analyzed via finite element modeling (FEM) and validated through in situ tie bar elongation measurements. The results show a 27.3% reduction in node displacement amplitude and a 19.6% improvement in wave transmission uniformity compared to conventional methods. This methodology enhances acoustic tweezers’ operational stability and provides a generalizable framework for multi-physics optimization in MEMS manufacturing, laying a foundation for next-generation applications in single-cell manipulation, lab-on-a-chip systems, and nanomaterial assembly. Full article

Driven by stringent emission regulations, advanced combustion modes utilizing turbulent jet ignition technology are pivotal for enhancing the performance of marine low-speed natural gas dual-fuel engines. This review focuses on three novel combustion modes, yielding key conclusions: (1) Compared to the conventional DJCDC mode, the TJCDC mode exhibits a significantly higher swirl ratio and turbulence kinetic energy in the main chamber during initial combustion. This promotes natural gas jet development and combustion acceleration, leading to shorter ignition delay, reduced combustion duration, and a combustion center (CA50) positioned closer to the Top Dead Center (TDC), alongside higher peak cylinder pressure and a faster early heat release rate. Energetically, while TJCDC incurs higher heat transfer losses, it benefits from lower exhaust energy and irreversible exergy loss, indicating greater potential for useful work extraction, albeit with slightly higher indicated specific NOx emissions. (2) In the high-compression ratio TJCPC mode, the Liquid Pressurized Natural Gas (LPNG) injection parameters critically impact performance. Delaying the start of injection (SOI) or extending the injection duration degrades premixing uniformity and increases unburned methane (CH₄) slip, with the duration effects showing a load dependency. Optimizing both the injection timing and duration is, therefore, essential for emission control. (3) Increasing the excess air ratio delays the combustion phasing in TJCPC (longer ignition delay, extended combustion duration, and retarded CA50). However, this shift positions the heat release more optimally relative to the TDC, resulting in significantly improved indicated thermal efficiency. This work provides a theoretical foundation for optimizing high-efficiency, low-emission combustion strategies in marine dual-fuel engines. Full article

Blood is a widely used sample for diagnosing diseases such as malaria and diabetes. While diagnostic techniques have advanced, sample preparation remains labor-intensive, requiring steps like mixing and centrifugation. Microfluidic technologies have automated parts of this process, including cell lysis, yet challenges persist. Electrical lysis offers a chemical-free, continuous approach, but lysing small cells like red blood cells requires high electric fields, which can damage electrodes and cause system failures. Here, we present a microfluidic device utilizing ion concentration polarization (ICP) for rapid blood cell lysis at 75 V. Fluorescence imaging confirmed the formation of an ion depletion region near the Nafion^® nanochannel membrane, where the electric field was concentrated across the entire microchannel width. This phenomenon enabled the efficient trapping and lysis of blood cells under these conditions. Continuous blood injection achieved a lysis time of 0.3 s with an efficiency exceeding 99.4%. Moreover, lysed cell contents accumulated near the Nafion membrane, forming a concentrated lysate. This approach eliminates the need for high-voltage circuits or chemical reagents, offering a simple yet effective method for blood cell lysis. The proposed device is expected to advance lab-on-a-chip and point-of-care diagnostics by enabling rapid and continuous sample processing. Full article