Search Results (3,841)

Exploitation of deep (>4000 m) tight geothermal reservoirs is constrained by low native permeability and premature thermal breakthrough, limiting sustainable heat recovery. Here, we investigate THM (thermo–hydro–mechanical) controls on fluid flow and heat transport during cold-water reinjection in deep tight sandstone reservoirs through an integrated framework linking two-dimensional mechanistic analysis with three-dimensional field-scale modeling. A two-dimensional thermo-poroelastic model reveals that strong thermal contrasts induced by cold-fluid injection cause contraction of the rock framework and transient pore-space dilation under confinement, producing short-term permeability enhancement. This process alters local flow capacity and redirects early cold-front migration, with persistent impacts on subsequent heat transport. Field-scale simulations further quantify the coupled effects of well spacing and reinjection temperature on thermal breakthrough, defined as a 1 °C decline in production-well temperature. Increased well spacing delays cold-front arrival and significantly retards breakthrough, whereas lower reinjection temperature enhances early heat extraction but accelerates convective transport, leading to earlier breakthrough. The combination of thermally enhanced permeability and intensified convection promotes preferential flow channels, increasing breakthrough risk. Balancing thermal-breakthrough delay against the heat-extraction driving force, the simulations delineate a favorable engineering window for the investigated conditions and clarify how cooling-sensitive permeability evolution affects preferential flow and reservoir-scale thermal response. Full article

With the expansion of ammonia energy applications, long-distance liquid ammonia pipelines are expected to support large-scale cross-regional ammonia transport. In the liquid ammonia pipeline commissioning process, gaseous ammonia purging involves ammonia–nitrogen mixing and possible liquefaction, while liquid ammonia injection may induce flashing and severe local cooling, all of which can affect commissioning safety. To characterize these thermodynamic phenomena, a transient gas–liquid two-phase flow model was established and validated using OLGA 2022.1.0 software for simulating the long-distance liquid ammonia pipeline commissioning. The model adopts the cross-sectionally averaged one-dimensional approach. A volume-corrected Soave–Redlich–Kwong (SRK) equation of state for ammonia was adapted, validated, and used to generate OLGA-compatible thermodynamic property tables. The results show that, during gaseous ammonia purging, a higher flowrate shortens the displacement time by accelerating nitrogen removal, and this effect is more pronounced at higher ambient temperatures due to enhanced molecular diffusion. Along the pipeline, pressure gradually decreases from frictional resistance, with a steeper drop near the outlet caused by gas acceleration, and temperature gradually approaches ambient through heat exchange with the pipe wall and surrounding soil. A high gaseous ammonia flowrate can cause partial liquefaction, regasification, and temperature fluctuations. During liquid ammonia injection, local condensation and slight liquid accumulation occur before the liquid front arrives, and the low-temperature region moves with the liquid front. The liquid ammonia mass flowrate has the strongest influence on the injection process, as it reduces the completion time but increases the outlet temperature, outlet pressure, and the low-temperature risk downstream of the valve. Therefore, it should be controlled within an appropriate range to balance efficiency and low-temperature safety risks. This work provides a rapid and efficient prediction model for key thermo-hydraulic parameters during liquid ammonia pipeline commissioning, and the overall analyses offer insights for on-site process design and safety control. Full article

Direct grid-connected wireless charging based on direct AC–AC conversion is attractive for electric vehicles (EVs) because it can reduce power conversion stages and improve charger compactness. In matrix-converter-based wireless power transfer (WPT) systems, the grid-frequency AC voltage can be directly converted into high-frequency AC voltage without using bulky DC-link electrolytic capacitors. However, the removal of the intermediate energy-storage stage also makes the EV wireless charger more sensitive to grid-voltage fluctuation. For an LCC-S compensated WPT system, the voltage-source output characteristic makes the charging-side voltage sensitive to grid-voltage disturbance, resulting in severe MC output-current and battery charging-current overshoot. This transient overcurrent may threaten both the power converter and the EV battery charging process. In this paper, a dual-frequency state-space model is developed for the matrix-converter-based electrolytic-capacitor-less LCC-S WPT system to analyze the disturbance propagation from the grid side to the high-frequency resonant stage and the EV battery side. Based on the model, the current-overshoot suppression capability and bandwidth limitation of the conventional dual closed-loop control strategy are investigated. To further enhance transient current protection, a grid-voltage feedforward strategy is proposed to compensate for the disturbance before severe current overshoot is formed. Finally, experimental results verify that the proposed method effectively suppresses the MC output-current and battery charging-current overshoot under grid-voltage fluctuation, thereby improving the grid-disturbance resilience and dynamic safety of direct grid-connected EV wireless charging systems. Full article

Dioxazolones are important precursors for generating nitrenes (highly reactive intermediates widely used for carbon–nitrogen bond formation in organic synthesis) upon exposure to light or heat. The photochemical reaction dynamics of 3-phenyl-1,4,2-dioxazol-5-one in CHCl₃ were investigated using femtosecond time-resolved infrared spectroscopy and electronic structure calculations. Photoexcitation at 267 nm rapidly populates an excited singlet state that serves as the key branching point for subsequent photophysical and photochemical processes. Transient infrared spectra reveal the formation of carbon dioxide, phenyl isocyanate, and singlet benzoyl nitrene through their characteristic vibrational features. Kinetic analysis shows that decarboxylation from the excited singlet state occurs with a time constant of 4.7 ± 1 ns, producing phenyl isocyanate and benzoyl nitrene with time constants of 8.1 ± 2 ns and 11 ± 3 ns, respectively. Competing relaxation pathways include internal conversion to the ground state (7.5 ± 2 ns) and intersystem crossing to the T₁ state (25 ± 5 ns). The T₁ state relaxes to the ground state (350 ± 30 ns) without contributing to product formation. These results demonstrate that both isocyanate and nitrene products originate from the S₁ state and provide detailed mechanistic insight into the competing pathways governing dioxazolone photochemistry in solution. Full article

Snake-like robots require body adaptation during locomotion when creeping through environments with obstacles. Central Pattern Generator (CPG) provides an effective way to generate rhythmic signals through parameter modulation. During body-shape adaptation, the body wave generated by the CPG can be modified by adjusting its parameters. In this paper, a CPG network based on Hopf oscillators is adopted, and the amplitude parameter is used for body-shape adaptation. However, the influence of amplitude variation during the transition process has not been fully understood. More specifically, when the amplitude parameter changes abruptly, the attractor shifts immediately, while the oscillator state cannot follow the new attractor instantaneously. This mismatch produces transient responses and waveform distortion during the transition process. To address this issue, a linear parameter transition method is introduced. The proposed method is subsequently extended to a coupled CPG network for controlling the multi-joint snake-like robots. Simulations are conducted under different parameter transition conditions. The results demonstrate that the parameter transition method strongly affects the transient torque response. Compared with abrupt parameter variation, the proposed linear transition method significantly reduces transient torque peaks. Additionally, the results further show that even a short transition interval is sufficient to achieve most of the torque reduction. Experiment results show that the proposed method can be applied to body-shape modulation and obstacle avoidance during snake-like robot locomotion. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Anomaly detection models trained exclusively on healthy data are widely used in wind turbine condition monitoring because failure data are scarce, heterogeneous, and often unavailable. However, these models produce anomaly indicators that are sensitive not only to fault-related degradation but also to normal operational variability, transient disturbances, and changes in loading conditions. As a result, the practical behavior of an alarm system depends not only on the anomaly detection model but also on the decision rule used to activate and maintain alarm states. This study presents a decision-oriented evaluation of persistence-based alarm confirmation in wind turbine anomaly detection. Four representative techniques are analyzed within a unified framework: Isolation Forest, One-Class Support Vector Machine, Referenced Moving Window Principal Component Analysis using Q-statistic and percentage component weight indicators, and Autoencoder-based reconstruction error. The evaluation combines controlled OpenFAST simulations of rotor unbalance under different severity and noise conditions with an industrial SCADA case study involving a documented main bearing fault. Results show that temporal persistence strongly shapes alarm outcomes across methods and datasets. Low persistence values favor early detection but promote alarms from isolated threshold exceedances, whereas moderate persistence substantially reduces false positives while preserving detection capability in severe and well-observable faults. Excessive persistence increases detection latency and missed detections, particularly for weak, intermittent, or slowly evolving fault signatures. These findings indicate that persistence-based alarm confirmation should be treated as an explicit decision-level configuration variable, rather than as a fixed post-processing or alarm-state heuristic, when designing anomaly detection systems for wind turbine condition monitoring. Full article

Sharp wave–ripples (SWRs) are transient hippocampal population events that coordinate neuronal ensemble activity and play a central role in memory consolidation and affective processing. Although SWRs exhibit marked functional specialization along the dorsoventral axis of the hippocampus, and several cellular mechanisms underlying SWRs are sex-sensitive, systematic comparisons of SWR properties between females and males are lacking. Here, we examined sex- and region-dependent differences in SWRs and associated multiunit activity (MUA) in acute hippocampal slices from adult female and male rats. Spontaneous SWRs were recorded from the CA1 stratum pyramidale of the dorsal and ventral hippocampus, and SWR occurrence rate, amplitude, ripple oscillation properties, and SWR-locked neuronal firing were quantified. Linear mixed-effects analysis revealed robust region-dependent differences across multiple SWR parameters. In contrast, sex effects were selective. SWR occurrence rate and amplitude did not differ significantly between females and males, whereas SWR-associated MUA showed a significant main effect of sex, with higher values in males. Ripple power was also influenced by sex, with higher values in females, together with a significant effect of region, suggesting differences in oscillatory structure. Baseline MUA did not differ between sexes, indicating that sex-related effects are specific to the SWR state. These findings suggest that sex does not substantially alter the generation of SWRs per se but influences neuronal recruitment and oscillatory properties during these events. Our results reveal previously underappreciated dimensions of hippocampal network organization and provide a descriptive framework for future studies investigating how sex-dependent circuit properties may shape hippocampal contributions to cognition and affective regulation. They further highlight the importance of incorporating sex as a fundamental biological variable in studies of hippocampal network dynamics. Full article

Listeria monocytogenes is a ubiquitous Gram-positive bacterium responsible for listeriosis, a foodborne zoonotic disease affecting humans and animals. Although infection in immunocompetent individuals is often asymptomatic or limited to mild self-limiting gastroenteritis, Listeria monocytogenes may cause severe invasive disease in vulnerable groups, including pregnant women, neonates, elderly individuals, and immunocompromised patients. Although the incidence of listeriosis is relatively low compared with many other foodborne pathogens, the high hospitalization and mortality rates associated with clinical cases make this bacterium a major concern for food safety and public health. The evolutionary success of L. monocytogenes reflects the interaction between a conserved core genome and a dynamic accessory genome shaped by horizontal gene transfer (HGT), ecological selection, and expansion of specific clones. Transient intestinal carriage in humans and animals, potentially influenced by gut microbiome composition, creates ecological interfaces where plasmids, transposons, prophages, and integrative conjugative elements contribute to the exchange of antimicrobial resistance determinants, virulence factors, and stress tolerance systems. Virulence diversification is further influenced by the differential distribution of pathogenicity islands such as LIPI-1, LIPI-3, and LIPI-4 across specific clonal lineages. These evolutionary processes occur across interconnected farm, food-production, environmental, and clinical ecosystems consistent with the One Health framework. Advances in whole-genome sequencing have clarified lineage-specific gene flow, expansion of specific clones, and the dynamics of the resistome and mobilome in L. monocytogenes populations. This narrative review aims to synthesize current knowledge on the mobile genetic elements and ecological interfaces that shape horizontal gene transfer in L. monocytogenes. Its novelty lies in integrating antimicrobial resistance, virulence-associated genomic islands, stress adaptation, and gut microbiome-mediated selection within a One Health and metapopulation framework. The main message of this review is that HGT should be interpreted as a context-dependent contributor to L. monocytogenes adaptation, acting together with clonal background, ecological selection, and mobile genetic elements. Full article

Triboelectric nanogenerators (TENG) show significant potential in pressure sensing by converting mechanical disturbances into electrical signals positively correlated with the magnitude of the applied force, yet their development as practical pressure sensors is severely hindered by the major drawback of only detecting transient mechanical inputs. Additionally, traditional dual-mode pressure sensors have typically required complex multilayer structures and time-consuming fabrication processes. Here, a simple dual-mode pressure sensor of novel structure integrated with TENG and anodic aluminum oxide (AAO) for both dynamic and static pressure detection is proposed. Nanoporous AAO is directly grown on an aluminum substrate to simplify the traditionally complex multi-layer structure of dual-mode pressure sensors. The AAO layer serves a dual functionality by acting as an active triboelectric layer that significantly enhances the triboelectric output performance while concurrently functioning as the capacitive dielectric layer. A polydimethylsiloxane (PDMS) film is employed as the elastic counterpart to pair with the AAO substrate. The influence of PDMS thickness on the charge accumulation and extraction of the TENG mode is investigated to optimize the device output. Under optimal configurations, the streamlined Al-AAO/PDMS sensor demonstrates good sensitivity and linearity (R² > 0.99) for both dynamic triboelectric voltage (1.05 V/kPa) and static capacitance (5.56 pF/kPa) over a wide sensing range of 1–73 kPa. This dual-mode sensor effectively overcomes the transient limitation of conventional single-mode TENGs and shows significant potential for future smart tactile applications. Full article

High Velocity Oxy-Fuel (HVOF) thermal spraying enables the deposition of dense coatings with low porosity, high hardness, and good fracture resistance. Tungsten carbide–cobalt (WC-Co) coatings are widely used in industrial and aerospace applications due to their excellent wear resistance; however, improving crack resistance and coating–substrate adhesion remains a key challenge. In this study, WC-Co+Ni composite coatings were deposited on ductile cast iron, with emphasis on the role of Ni addition in controlling microstructure development under HVOF conditions. Microstructural characterization was performed using optical, scanning, and transmission electron microscopy (OM, SEM, TEM), while phase composition and chemical analysis were determined by X-ray diffraction (XRD) and energy-dispersive spectroscopy (EDS). The coatings exhibited a dense, low-porosity microstructure composed of fine WC and W₂C carbides embedded in a Co–Ni binder, with locally nanocrystalline regions. XRD analysis confirmed WC and W₂C as the dominant phases, with weak reflections corresponding to the η-phase (Co₆W₆C), indicating local decarburization. The addition of Ni increases the fraction of the transient liquid phase during particle flight, enhancing carbide dissolution and mass transport in the binder, which accelerates decarburization kinetics and promotes η-phase formation. Simultaneously, Ni modifies the binder into a more ductile Co–Ni matrix, reducing the detrimental effect of brittle η-phase on coating integrity. Mechanical and tribological testing (instrumented indentation and scratch testing) demonstrated improved crack resistance, wear resistance, and adhesion. The results show that Ni addition enables process-driven microstructural tailoring of HVOF-sprayed WC-Co coatings, leading to enhanced performance despite the presence of η-phase. Full article

In milling operations, cutters entering and exiting workpiece boundaries cause varying radial immersions and chip thicknesses. This generates aperiodic cutting forces that often induce vibrations and degrade surface quality. To address this, this study aims to accurately predict milling forces and surface profiles during these critical engagement and disengagement phases. An analytical approach was developed to estimate the changing distances between the cutting teeth and workpiece boundaries, enabling the precise calculation of the dynamic chip thickness as the cutter transitions through the material. Based on these dynamic calculations, milling forces and system responses were simulated. Experimental validation demonstrated a strong agreement between the simulated cutting forces, machined surface profiles, and real-world results. Notably, findings revealed that even cutting parameters deemed stable by traditional stability lobes can still trigger vibrations during these boundary transitions. Consequently, a novel parameter selection strategy is proposed to effectively prevent these transient vibrations, significantly enhancing the final surface finish. Ultimately, this comprehensive modelling framework provides a deeper understanding of the system dynamics throughout the entire milling process, offering high relevance for broader applications, such as optimising energy consumption, predicting tool wear, and improving machining parameter optimisation. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a +27.36% reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to 15.2 s, and restricting transient overshoot to just 0.18%. Full article

With the large-scale development of renewable energy such as wind, solar and ocean energy, the demand for energy storage is more urgent. Pumped hydro energy storage (PHES) is one of the fundamental solutions to the problem of intermittent supply of renewable energy. The large-capacity/low-head pumped hydro energy storage (LL-PHES) system with the use of tubular pump turbine is a beneficial extension of traditional PHES systems owing to large flow rate and cheaper civil structures. However, the continuous competition between the “static water pressure difference caused by gravity” and the “pressure increase caused by accelerated impeller rotation” leads to prominent instability in the start-up process of the LL-PHES system under pump conditions. An explicit coupling algorithm is proposed for analyzing the transient characteristics in the start-up process of the LL-PHES system under pump conditions. This algorithm is based on the idea of dimensional transformation, and performs 3D flow calculations and 2D rigid body dynamics equation solution in the pump domain and the flap gate domain, respectively. This algorithm avoids the problems of high computational cost and poor convergence that exist in existing fully three-dimensional coupling algorithms and ensures the efficiency of transient hydraulic characteristic calculation. A comprehensive analysis of the transient characteristics of the LL-PHES system during pump start-up process is conducted using the proposed new algorithm. The entire process of the increase in rotational speed, valve opening, flow rate, and the continuous evolution of blade surface pressure during the start-up process is quantitatively described. The amplitude and spectral characteristics of the alternating pressure on multiple blades are clarified. The evolution law of blade load during the stage of severe pressure fluctuations during the start-up process is explained. The load distribution characteristics of “high in the leading and trailing edge areas and low in the middle” in the blade stream direction is presented. The research results have a direct guiding role in improving the hydraulic design and enhancing the operational stability of LL-PHES systems. Full article

Additive manufacturing (AM) enables the fabrication of complex, customisable components from metals, composites and polymers such as polylactic acid (PLA); however, the process commonly produces poor surface finishes and inherent defects. Centrifugal disc finishing (CDF) is an established mass finishing technique in conventional manufacturing but remains insufficiently characterised for additively manufactured polymers. This exploratory study investigates the influence of CDF on fused deposition modelling (FDM)-fabricated PLA components with varying geometrical features, focusing on three-dimensional surface parameters including average areal surface roughness, skewness and kurtosis. Samples were processed up to 720 min with analysis at predetermined intervals to capture transient and steady-state-like behaviour. Surface characterisation was conducted using non-contact optical interferometry to obtain quantitative roughness data and three-dimensional topographical maps, supported by digital optical microscopy and gravimetric analysis to quantify material removal rates. Analysis of the experimental data indicated apparent relationships between processing time, geometry and surface response. Results indicate that material removal behaviour and roughness evolution may be geometry-dependent. Flat and convex surfaces appeared to follow expected transient-like and steady-state-like behaviour, whereas restricted geometries and intricate features exhibited distinct responses with characteristic transition times. Surface roughness reductions ranged from 36% to 89% depending on geometry. These findings provide preliminary quantitative insight into geometry-specific mass finishing behaviour, supporting improved process understanding and informing future optimisation of post-processing strategies for additively manufactured polymer components. Full article