Search Results (599)

Deep-sea gear transmission systems encounter critical lubrication challenges arising from the synergistic coupling of extreme hydrostatic pressure and cryogenic temperatures. These environmental stressors induce exponential viscosity escalation in lubricants, precipitating severe fluidity degradation, elevated startup resistance, and lubrication starvation. Concurrently, seawater intrusion triggers lubricant emulsification, additive deactivation, and electrochemical corrosion at meshing interfaces, collectively escalating the risk of catastrophic lubrication failure and compromising long-term operational reliability. This study systematically elucidates the lubrication degradation mechanisms inherent to deep-sea environments and proposes targeted mitigation strategies. Through comprehensive characterization of deep-sea environmental parameters and their impact on lubricant rheological behavior, we critically evaluate the applicability and inherent limitations of conventional Thermal Elasto-Hydrodynamic Lubrication (TEHL) theory under extreme conditions. Our analysis reveals that established TEHL frameworks necessitate substantial modification to accurately capture pressure-viscosity-temperature coupling phenomena and seawater contamination kinetics. Meshing interface texturing, as an effective anti-friction and wear-mitigation strategy, is investigated to delineate its mechanistic pathways for enhancing lubricant film formation and tribological performance under starved lubrication regimes. Key findings demonstrate that optimized micro-texture architectures can effectively compensate for viscosity-induced fluidity deficits and attenuate the deleterious effects of seawater ingress. Critical knowledge gaps are identified, and future research trajectories are charted: (i) multiphysics coupling models integrating thermo-hydrodynamic, chemo-physical, and mechanical degradation processes; (ii) synergistic texture-coating design paradigms; (iii) high-pressure low-temperature experimental validation protocols; and (iv) engineering implementation frameworks for deep-sea gear transmission systems. This review establishes theoretical foundations and provides technical guidelines for robust lubrication design and long-term operational stability of deep-sea transmission equipment. Full article

Gas turbines play a critical role in modern power systems, yet their transient operations (e.g., start-up, load mutation) induce significant thermal inertia in metal components, leading to deviations between simulation results and actual performance. Traditional low-dimensional (1D/0D) simulation models sacrifice detailed flow and temperature field information to reduce computational load, while high-dimensional (3D) computational fluid dynamics (CFD) models are impractical for full-system simulations due to excessive computational costs. This discrepancy creates a critical trade-off between simulation accuracy and efficiency in gas turbine thermal inertia studies. To address this challenge, this study proposes a temperature-gradient-guided dynamic genetic optimization sampling algorithm (TDGA) and integrates it into a multi-dimensional data scaling framework for gas turbines. A fully coupled simulation framework was established, combining 3D CFD models for turbine flow paths (resolving detailed flow and temperature fields) and 1D thermal models for metal components (casing, hub, blades). The TDGA was designed to enable efficient data interoperability between models: it incorporates a dynamic encoding mechanism, temperature gradient weight matrix, density penalty term, quantity penalty term, and regularization term to optimize sampling point distribution. Dynamic weight coefficients for each objective function term and adaptive crossover/mutation probabilities were introduced to balance global exploration (early iterations) and local exploitation (late iterations) during optimization. Comparative analysis showed that the TDGA achieved a mean squared error (MSE) of 15.52K, far lower than those of traditional Latin Hypercube Sampling (75.07K) and Bootstrap Sampling (64.38K). It allocated 70.11% of sampling points to high-temperature gradient regions while reducing the total number of sampling points to 2765. During the middle stage of the gas turbine start-up process, compared with the traditional Latin Hypercube Sampling and Bootstrap Sampling, the average error of the proposed sampling algorithm is reduced by 17.4% and 13.3%, respectively. The proposed TDGA-based framework effectively balances simulation accuracy and computational efficiency, providing a reliable approach for the transient thermal analysis of gas turbines. Full article

The Integrated Fixed-film Activated Sludge (IFAS) partial nitritation/anammox (PN/A) process offers robust nitrogen removal, yet startup using pre-colonized carriers incurs high logistical costs. This study investigated the mechanism of inoculating a pilot-scale IFAS system with granular anammox sludge to treat anaerobic digestion supernatant. The treatment train integrated coagulation, pre-aeration, and an IFAS-PN/A unit. The granular-inoculated IFAS-PN/A unit achieved stable biofilm formation and a nitrogen removal rate of 0.36 kg N m⁻³ d⁻¹, benefiting from the effective interception of excessive organic carbon by the preceding coagulation and pre-aeration steps. Microbial analysis identified Candidatus brocadia as the dominant anammox genus, revealing a distinct migration pathway: bacteria transferred from disintegrating granules to the suspended sludge—acting as a transitional vector—before ultimately colonizing the carriers. While granular biomass diminished, anammox abundance in the biofilm increased to 12.0% by day 166. Furthermore, distinct spatial niches were observed: ammonium-oxidizing bacteria (AOB) dominated the suspended sludge, while nitrite-oxidizing bacteria (NOB) were effectively suppressed. These findings demonstrate the feasibility of granular inoculation for cost-effective IFAS startup and provide critical insights into the bacterial migration dynamics required for stable operation. Full article

The paper presents the results of a study of linear wear of gas-flame and ion-plasma coatings of KNA-82 seals with an yttrium content of 0.5%, used in gas turbine engine assemblies, during physical modeling of their thermomechanical loading on small-sized samples. Tribotechnical tests were carried out in four stages, simulating the operating conditions of real gas turbine engines—from the first start-up with running-in of the coating cut-in areas to reaching a steady state with their service properties formed. The surface of the coatings was in contact with the ridges of triangular-shaped plates without heating (20 °C), at average heating (350–470 °C), after holding the samples at 1100 °C and average heating of 410–460 °C, and after grinding off the worn layer that had worn out after holding the samples at 1100 °C at average heating of 320–440 °C. Trends in the change in the linear ear of coatings and the formation of friction tracks caused by the uneven manifestation of the physical and mechanical properties of coatings, which are unevenly distributed throughout their body, were determined. It was found that both coatings tend to stabilize the wear process at certain mechanical pressures in the friction contact zone and only in the temperature range from 20 °C to 400 °C. These pressures range from 4 MPa to 6.7 MPa for gas-flame coatings and from 3 MPa to 4.2 MPa for ion-plasma coatings. It has been determined that within the depth range of 30–100 μm, the wear resistance (as assessed by linear wear) of ion-plasma coatings is higher than that of gas-flame coatings. This predetermines the fact that in the event of a catastrophic collision between the coatings and a blade, the geometry of the damage to the surface of the gas-flame coating will be greater than that of the ion-plasma coating. In the event of damage exceeding 75–100 μm in depth, both coatings become inoperable, since their wear characteristics are no longer maintained. This is indicated by a rapid decrease in their wear resistance under step loading. Moreover, the gas-flame coating is more prone to catastrophic failure than the ion-plasma coating. Full article

The US National Academy of Sciences has reported that CO₂ mineral carbonation is among the largest, most energy-efficient CO₂ utilization technologies closest to commercial scale due to its thermodynamic favorability and end-product market size. However, the natural rate of reaction is generally slow in terms of kinetics, whereby only by dramatically increasing the CO₂ dissolution rate can a major impact on the rate of reaction for CO₂ mineral carbonation happen. Hence, despite the clear advantages of CO₂ mineral carbonation over other options in Carbon Capture and Sequestration CCS technologies, the current research gaps highlighted here should be addressed to ensure future technology deployment success. Therefore, this study investigated the feasibility of the design, operation and experimental improvement of a continuous high-pressure CO₂ reactor in producing and optimizing high-quality precipitated calcium carbonates PCC synthesized for consumer and industrial application. A novel mineral carbonation reactor is hereby proposed, in which, by incorporating the application of a high-pressure or supercritical CO₂ phase into the reactor, CO₂ diffusion can be increased into the continuously fine-sprayed aqueous reaction media within the reactor to form PCC. The effective reactor volume can be simultaneously decreased from the reduced high-pressure CO₂ volume. Next, by incorporating a backpressure regulator, a continuous flow of the liquid phase in and out of the reactor can be controlled. The initial reactor design had undergone successful start-up, but experimental improvement alone was unable to provide the anticipated particle size of the calcium carbonate precipitate PCC. Optimized design of the new reactor to limit internal dead flow zones was proven to successfully reduce the particle size of precipitated calcium carbonate PCC from an initially P50/P90 of 87/131 μm to 3.8/9.1 μm. Additionally, a continuous 100 h stable run was successfully executed to thoroughly investigate the three main factors influencing the quality of PCC synthesized, in which the reactant flow rate and feedstock concentration were found to be significant, with the exception of CO₂ gas pressure. The overall 3D surface trend of the particle size spread P50/P90 of the PCC synthesized was plotted over the experimental range and found to meet most of the industrial requirements and technical specifications, except for TiO₂ replacement which requires sub-micron quality. Instantaneous electricity power consumption was also measured at various operating points. Performance-wise, the continuous high-pressure CO₂ mineral carbonation reactor in this work was calculated to be able to process a maximum of 4200 g/h lime CaO feedstock at a lime concentration of 7 g/L and flow rate of 10 g/L, using a 40 L internal volume vessel, effectively increasing the productivity of lime CaO production by several fold from what was reported by peer studies assuming similar electricity costs were used for all productivity factors under consideration. Full article

This article presents the concept of building a discrete event simulation model of a production system in terms of statistical and probabilistic models, which is based on a fragment of a broader production process in the furniture industry. The purpose of the study was to evaluate the efficiency of a single-shift production process during the start-up phase and to determine the impact of implementing two- and three-shift systems. The discrete event simulation model was developed using actual production data collected during a single-shift operation. Scenarios were then designed to identify and quantify the necessary process adjustments required for the successful implementation of two- and three-shift systems. The authors demonstrated that simulation modeling of production processes based on probabilistic distributions provides information that is essential for effective capacity planning. The proposed percentile grids enabled clear visualization and precise assessment of production resource utilization in various shift configurations, facilitating decision-making regarding capacity expansion based on previously assumed data. Full article

The tight gas reservoirs developed in the central Shenfu block are characterized by ultra-low porosity and permeability (typically < 10% porosity, <1 mD permeability), and high irreducible water saturation (40–60%). The frequent water blocking issue sharply reduces gas relative permeability during the production period, severely limiting well productivity. In this study, core flooding experiments using artificial cores were conducted to systematically evaluate the feasibility of CO₂ injection for enhanced gas recovery (EGR). The results show that the effectiveness of CO₂ EGR is sensitive to many factors, such as injection pressure, injection rate, total injection volume, and core permeability. The higher injection pressure and rate would improve the pressure gradient, CO₂ sweep efficiency, and EGR. An optimal total volume with the value (around 2.0 pore volumes, PV) was recommended as the amount of CO₂ injection are varied in the range of 0.5–2.5 PV. A higher permeable tight reservoir is prone to a higher nature gas recovery. The experimental findings, within the controlled conditions of this study, suggest that a flowback strategy of “slow startup and controlled depressurization” could be considered. Combining CO₂ injection with managed pressure drop of production and optimized fracturing process is proposed as a potential comprehensive strategy focused on “energy supplement, damage mitigation, and water control,” which may provide a useful reference for the efficient development of high-water-saturation tight gas reservoirs. Full article

During the diesel engine start-up phase, low rotational speed and coolant temperature result in poor fuel atomization and prolonged ignition delay. This impedes the in-cylinder combustion process and directly impacts the engine’s emission performance. As the first combustion cycle during the starting process, the initial starting cycle significantly influences subsequent combustion cycles and overall starting performance. This paper proposes a target-torque-based control strategy for fuel injection quantity during the starting process. It optimally determines the target acceleration curve for the starting process, thereby calculating the optimal fuel injection quantity for the initial starting cycle. Based on this, a combustion system simulation model of the diesel engine was established using the 3D CFD software AVL FIRE v2010. The simulation investigated the impact of first injection speed on the combustion process and performance of the first firing cycle under different ambient temperatures: normal temperature (20 °C), low temperature (5 °C), and cold start (−10 °C). The results indicate that the optimal first cycle injection quantities under normal, low, and cold start conditions are 17.3 mg, 18.5 mg, and 20.4 mg, respectively. The impact of first injection speed on the first firing cycle combustion process primarily manifests in the mixture formation rate and time, and higher speeds do not necessarily yield better results. The optimal first injection speeds at normal temperature (20 °C), low temperature (5 °C), and cold start (−10 °C) were 220 r/min, 240 r/min, and 220 r/min, respectively. Corresponding indicated thermal efficiencies were 30.74%, 28.67%, and 28.7%, with relatively low emissions of pollutants such as CO, NOx, and HC. Full article

Electrospinning is a widely used technique for fabricating nanomaterials with tailored morphology and functional properties. This study investigates how two fundamental process parameters—applied voltage and needle tip-to-collector distance—affect the spatial geometry and deposited mass of electrospun nanofiber mats in a top-down vertical electrospinning setup using a 10% (w/v) PVA solution prepared in deionized water. To support this hypothesis, both experimental measurements and 3D geometric modeling were performed to evaluate the area, perimeter, and deposited mass under different parameter combinations. Digital image analysis and cross-sectional reconstruction were applied to model nanofiber deposition. Regression and ANOVA analyses reveal that the tip-to-collector distance has a statistically significant impact on both area and perimeter of the electrospun nanofiber mat, while the applied voltage in the tested range (15–20 kV) has no significant effect. Interestingly, the total deposited mass shows no clear dependence on either parameter, likely due to startup irregularities or solution droplets. Full article

New ventures operating in volatile and unpredictable environments must rely on rapid adaptation and decisive action, making improvisation a critical entrepreneurial capability. This study examines how improvisation enhances new venture performance by uncovering the psychological and learning-based mechanisms through which its effects unfold. Drawing on the Knowledge-Based View (KBV) and Social Learning Theory (SLT), the model proposes that improvisation strengthens entrepreneurial self-efficacy, enabling entrepreneurs to approach uncertainty with greater confidence and adaptive judgment. Using a two-wave survey of 322 startup founders in Turkey and analyses conducted through PROCESS and complementary SEM estimation, the findings show that improvisation significantly boosts both entrepreneurial self-efficacy and new venture performance. Entrepreneurial self-efficacy emerges as a key mediating mechanism, indicating that improvisational experiences help entrepreneurs develop mastery, reinforce capability beliefs, and translate spontaneous action into improved outcomes. The results further suggest that improvisational episodes provide immediate learning cues that enhance situational awareness and decision-making agility, deepening the psychological pathway that links spontaneous behavior to venture performance. Additionally, relative explorative learning significantly moderates the relationship between improvisation and entrepreneurial self-efficacy, demonstrating that entrepreneurs benefit more from improvisation when they actively pursue new knowledge, experiment with unfamiliar approaches, and challenge routine assumptions. This moderating role clarifies when improvisation produces its strongest effects, while the mediating mechanism explains how performance improvements materialize through confidence-building processes. By integrating these mechanisms into a unified explanation, the study advances understanding of the improvisation–performance relationship and highlights the importance of learning-oriented behavior in converting spontaneous action into sustained entrepreneurial advantage. The findings offer theoretical contributions and actionable insights for entrepreneurs seeking to strengthen adaptability, resilience, and competitiveness in fast-changing environments. Full article

This study establishes a comprehensive structural isomorphism between Conway’s Game of Life and the entrepreneurial process, analysing the latter as a complex adaptive system governed by non-linear dynamics rather than linear predictability. Through a rigorous qualitative approach based on a systematic literature review and abductive inference, the research identifies and correlates four fundamental dimensions: uncertainty, adaptability, growth, and sustainability. Transcending traditional metaphorical comparisons, this paper introduces a novel mathematical model that modifies Conway’s deterministic logic by incorporating an «Agency» variable (A). This critical addition quantifies how an entrepreneur’s internal capabilities can counterbalance environmental pressures (neighbourhood density) to determine survival thresholds, effectively transforming the simulation into a «Game of Life with Agency» where participants actively influence their viability potential (Ψ). The analysis explicitly correlates specific algorithmic configurations with real-world business phenomena: high-entropy initial states («The Soup») mirror early-stage market uncertainty where outcomes are probabilistic; «gliders» represent the necessity of strategic pivoting and continuous displacement for survival; and «oscillators» symbolise dynamic sustainability through rhythmic equilibrium rather than static permanence. Furthermore, the study validates the «Gosper Glider Gun» pattern as a model for scalable, generative growth. By bridging abstract systems theory with managerial practice, the research positions these simulations as «mental laboratories» for decision-making. The findings theoretically validate iterative methodologies like the Lean Startup and conclude that successful entrepreneurship operates on the «Edge of Chaos», providing a rigorous framework for navigating high stochastic uncertainty. Full article

Environmental regulations serve as a critical determinant of industrial competitiveness in the global market. Recent policy shifts have driven a gradual convergence of rural environmental standards with urban norms, fostering a dynamic landscape of “top-down competition” between urban and rural regulatory frameworks. While the economic consequences of regional regulatory disparities are well-documented, the specific impacts of this regulatory convergence remain insufficiently explored. To address this gap, this study constructs a novel index to measure the convergence of environmental regulations between urban districts and rural counties at the prefecture level. Utilizing an unbalanced panel dataset of 5600 county-level timber processing enterprises, the Heckman two-stage model is employed for empirical analysis. The results demonstrate that the convergence of urban and rural environmental regulations significantly enhances both the export probability and export intensity of county-level firms, with these effects exhibiting persistence and cumulative growth over time. These findings remain robust across a series of validation tests, including instrumental variable estimation, double machine learning, and alternative model specifications. Mechanism analysis reveals that regulatory convergence promotes exports primarily by improving access to green credit and enhancing peer quality within the industry. Furthermore, heterogeneity tests indicate that the positive effects are most pronounced for start-ups and firms in the decline stage, as well as for enterprises located in eastern China, those outside the Yangtze River Economic Belt, and those subject to minimal government intervention. This study provides critical micro-level evidence that helps enterprises navigate the evolving policy landscape and supports the formulation of strategies to boost export trade amidst the integration of environmental regulations. Full article

The untreated discharge of dairy industry wastewater, characterized by high organic and nutrient loads, poses a severe eutrophication threat, leading to oxygen depletion and the disruption of aquatic ecosystems, which necessitates advanced treatment strategies. Anaerobic digestion (AD) represents an effective and sustainable alternative, converting organic matter into biogas while minimizing sludge production and contributing to Circular Economy strategies. This study investigated the effects of fat concentration and operational temperature on the anaerobic digestion of dairy effluents. Three types of effluents, skimmed, semi-skimmed, and whole substrates, were evaluated under mesophilic 35 °C and thermophilic 55 °C conditions to degrade substrates with different fat content. Low-fat effluents exhibited higher COD removal, shorter lag phases, and stable activity under mesophilic conditions, while high-fat substrates delayed start-up due to accumulation of fatty acids and brief methanogen inhibition. Thermophilic digestion accelerated hydrolysis and methane production but demonstrated increased sensitivity to lipid-induced inhibition. Kinetic modeling confirmed that the modified Gompertz model accurately described mesophilic digestion with rapid microbial adaptation, while the Cone model better captured thermophilic, hydrolysis-limited kinetics. The thermophilic operation significantly enhanced methane productivity, yielding 105–191 mL CH₄ g⁻¹VS compared to 54–70 mL CH₄ g⁻¹VS under mesophilic conditions by increasing apparent hydrolysis rates and reducing lag phases. However, the mesophilic process demonstrated superior operational stability and robustness during start-up with fat-rich effluents, which otherwise suffered delayed methane formation due to lipid hydrolysis and volatile fatty acid (VFA) inhibition. Overall, the synergistic interaction between temperature and fat concentration revealed a trade-off between methane productivity and process stability, with thermophilic digestion increasing methane yields up to 191 mL CH₄ g⁻¹ VS but reducing COD removal and robustness during start-up, whereas mesophilic operation ensured more stable performance despite lower methane yields. Full article

Voltage monitoring circuits are a fundamental block in energy-harvesting-powered applications, as typically the system operation has to be enabled only after a certain supply voltage is reached after a cold start or intermediate voltage levels have to be detected during start-up. The voltage values of interest vary depending on the specific system; hence, a versatile voltage monitoring circuit scheme that can be easily adapted for the desired voltage is particularly appealing. Furthermore, in energy-harvesting-powered applications, special care must be paid to power consumption minimization, in order to ensure self-sustainability of the system, and to area occupation, thus enabling a small form factor and low cost. To address these requirements, this paper proposes a novel, highly versatile voltage-monitoring circuit for energy-harvesting-powered applications that minimizes power consumption and area occupation. Indeed, the proposed voltage monitor implementation, relying on cascaded PMOS-based and NMOS-based voltage detectors, can be easily adapted to any desired voltage level, also achieving high voltage levels to be detected by adding (multiple) diode-connected transistors in the first stage while maintaining the voltage monitor output rail-to-rail and avoiding static power consumption from the cascaded digital gates. The proposed solution, targeting a 800 mV voltage level to be detected, was designed in a 180 nm CMOS triple-well technology and extensively validated through simulations in Cadence Virtuoso. Furthermore, it was bench-marked with an implementation in the same process based on the standard voltage monitor scheme (including the necessary cascaded logic gates for achieving a rail-to-rail output) available in literature, showcasing a reduction up to about 1700× in power consumption and 3.87× in area occupation, considering a preliminary area estimation, when triple-well devices are employed, whereas, when relying only on standard devices, although no significat area benefit is obtained, a reduction of up to about 400× in power consumption is achieved. Full article

This paper presents a self-contained startup charging circuit designed for energy-harvesting batteryless IoT devices. The proposed circuit consists of a current-biasing block, a current mirror, a reference voltage generator, and a comparator circuit. The current-biasing circuit drives the current mirror, which supplies the charging current to the energy storage element. Simultaneously, the reference voltage generator—also biased by the current source—produces a stable DC reference voltage. When the energy storage device (e.g., a supercapacitor) lacks sufficient charge, the comparator enables the charging path by activating the current-biasing and mirror circuits. Once adequate energy is stored, the comparator disables these circuits to prevent overcharging. This self-contained solution is intended to autonomously initialize and manage the cold-start charging process in energy-harvesting systems without relying on external controllers. This paper highlights the circuit architecture and validated performance, demonstrating a charging current of up to 27 mA, a reference voltage of 700 mV, and an operating range from 0.9 V to 1.8 V across a temperature range of −40 °C to 85 °C. Full article