Search Results (414)

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

Inspired by the free-flight capabilities of the gannet in both aerial and underwater environments, a foldable-wing air–water cross-medium vehicle was designed. To enhance its propulsive performance and transition stability across these two media, aero-hydrodynamic performance analyses were conducted under three representative operating states: aerial flight, underwater navigation, and water entry. Numerical simulations were performed in ANSYS Fluent (Version 2022R2) to quantify lift, drag, lift-to-drag ratio (L/D), and tri-axial moment responses in both air and water. The transient multiphase flow characteristics during water entry were captured using the Volume of Fluid (VOF) method. The results indicate that: (1) in the aerial state, the lift coefficient increases almost linearly with the angle of attack, and the L/D ratio peaks within the range of 4–6°; (2) in the folded (underwater) configuration, the fuselage still generates effective lift, with a maximum L/D ratio of approximately 2.67 at a 10° angle of attack; (3) transient water entry exhibits a characteristic two-stage force history (“initial impact” followed by “steady release”), with the peak vertical load increasing significantly with water entry angle and velocity. The maximum vertical force reaches 353.42 N under the 60°, 5 m/s condition, while the recommended compromise scheme of 60°, 3 m/s effectively reduces peak load and improves attitude stability. This study establishes a closed-loop analysis framework from biomimetic design to aero-hydrodynamic modeling and water entry analysis, providing the physical basis and parameter support for subsequent cross-medium attitude control, path planning, and intelligent control system development. Full article

With increasing global energy demand and depletion of onshore oil–gas resources, deep-sea hydrocarbon exploration and development have become strategically vital. As core subsea transportation equipment, the performance of helico-axial multiphase pumps directly determines the efficiency and economic feasibility of deep-sea extraction. However, non-uniform inflow patterns caused by uneven gas–liquid distribution in pipelines degrade pressure-boosting capability and reduce pump efficiency under actual operating conditions. To address this, an optimization method employing helical static mixers was developed. A mixer with a 180° helical angle was designed and installed upstream of the pump inlet. Numerical simulations demonstrate that the mixer enhances gas-phase distribution uniformity in stratified flow, improving efficiency and head across varying gas void fractions (GVFs). At a stratification height ratio (Ψ) of 0.32, efficiency increased by 15.41% and head rose by 15.64 m, while turbulent kinetic energy (TKE) at the impeller outlet decreased by up to 50%. For slug flow conditions, the mixer effectively suppressed gas volume fraction fluctuations, consistently improving efficiency under different slug flow coefficients (φ) with a maximum head increase of 9.82%. The optimized flow field exhibits uniform gas–liquid velocity distribution, stable pressure boosting, and significantly reduced TKE intensity within impeller passages. Full article

The rapid growth of low-power Internet of Things (IoT) applications has created an urgent demand for compact, battery-free power solutions. However, most existing RF energy harvesters rely on active rectifiers, multi-phase topologies, or complex tuning networks, which increase circuit complexity and static power overhead while struggling to maintain high efficiency under microwatt-level inputs. To address this challenge, this work proposes a harmonic-recycling, passive, RF-energy-harvesting system with integrated power management (HR-P-RFEH). The system adopts a planar microstrip architecture compatible with MEMS fabrication, integrating a dual-stage voltage multiplier rectifier (VMR) and a stub-based harmonic suppression–recycling network. The design was verified through combined electromagnetic/circuit co-simulations, PCB prototyping, and experimental measurements. Operating at 915 MHz under a 0 dBm input and a 2 k$\Omega$ load, the HR-P-RFEH achieves a stable 1.4 V DC output and a peak rectification efficiency of 70.7%. Compared with a conventional single-stage rectifier, it improves the output voltage by 22.5% and the efficiency by 16.4%. The rectified power is further regulated by a BQ25570-based unit to provide a stable 3.3 V supply buffered by a 47 mF supercapacitor, ensuring continuous operation under intermittent RF input. In comparison with the state of the art, the proposed fully passive, harmonic-recycling design achieves competitive efficiency without active bias or adaptive tuning while remaining MEMS- and LTCC-ready. These results highlight HR-P-RFEH as a scalable and fabrication-friendly building block for next-generation energy-autonomous IoT and MEMS systems. Full article

Background: Osteoarthritis has a large and growing burden in an ageing population. Controversy exists in current management, particularly regarding opioid use due to increasing negative effects. Clinicians need guidance on the individual mortality associations for common osteoarthritis treatments when compared to a control. Aims: The aim is to undertake a structured narrative literature review comparing mortality associations for common osteoarthritis management options. Methods: A search strategy (Web of Science 23 September 2024) was performed to identify observational studies which reported all-cause mortality in a treatment group compared to a control. The control group could be either the general population or those with osteoarthritis who were treated with the following: NSAIDs (non-steroidal anti-inflammatory drugs), opioids, paracetamol, GLP-1 RAs (Glucagon-like peptide-1 receptor agonists), hip or knee arthroplasty, or exercise. Articles were screened by two authors, and each included article was assessed for adequate quality using the strengthening the reporting of observational studies in epidemiology (STROBE) framework. Results: Of 2362 studies retrieved, 39 cohort studies met the inclusion requirements. Exercise, compared to no or lower levels of exercise, had ten studies reporting substantially reduced all-cause mortality. GLP-1 RA agonists had two related studies showing all-cause mortality reduction up to 5 years. Mortality following joint arthroplasty followed a multi-phasic response. There was a short-term post-surgical increase in mortality. However, from 90 days post-surgery to 8–11 years, there were significant reductions in mortality. After 9–12 years post arthroplasty, mortality increased and became significantly higher. Opioids were associated with an increase in mortality in 6 out of 7 studies. Inconsistent trends were found for NSAIDs and paracetamol. Conclusions: Exercise and GLP-1 RA prescription are associated with reduced all-cause mortality. Arthroplasty was found to have survival benefit until 9–11 years post-operatively, whereafter mortality then increased. Opioids were found to consistently increase mortality when used for non-cancer pain at all time points. The other common osteoarthritis treatments assessed were not consistently associated with changes in mortality. Full article

Long-distance water conveyance systems require controlled filling after initial operation or maintenance. This process is complex and challenging to manage accurately. It involves transient two-phase flow with rapid velocity and pressure changes, which can risk pipeline damage. Studying the filling process is thus essential to ensure the safe and efficient operation of the system. Combining a specific engineering case, this work investigates gas–liquid two-phase flow in tunnel sections during filling. We employ a coupled Volume of Fluid (VOF) multiphase model and a Realizable k-ε turbulence model for our simulations. Hydraulic parameters (flow patterns, pressure, velocity) are analyzed using the results. Key findings indicate that higher filling flow rates destabilize the process. Gas retention behavior in low-pressure caverns varies, and gas–liquid eruptions occur at shaft water surfaces. Increased flow rates also intensify phase–pattern transitions, elevate peak pressure and velocity values, and amplify pressure pulsations and velocity fluctuations. Furthermore, faster gas transport in low-pressure caverns triggers flow instability, compromising exhaust efficiency. Full article

Vacuum membrane-based air dehumidification (MAD) is potentially more efficient than refrigeration cycles. Air permeance through a membrane is inevitable, especially when there is a large pressure difference between the supply and permeate sides. Given the high specific gas volume under vacuum conditions, removing the permeating air from the dehumidifier is crucial for the stable operation of the vacuum compressor. Energy-efficient air removal techniques are still lacking, thereby hindering the development of MAD technology. This paper proposes a novel MAD approach using a vacuum mixing condenser. The cooling water directly condenses moisture from the vacuum compressor without any heat exchanger. The permeating air and water mixture in the condenser then experiences a quasi-isothermal pressurization process through a multiphase pump, enabling continuous dehumidification and air removal with low power consumption. The fundamentals of the proposed approach are illustrated, and mathematical models are built. Influences of air permeance rate, cooling water flow rate, condenser pressure, membrane area, and gravitational work are investigated. The results show that a COP of 8~12 is achievable to dehumidify air to 50%RH, 25 °C. The vacuum compressor consumes about 80% of the power. A low air permeance rate, low condenser pressure, large membrane area, and high gravitational work positively impact the COP, while the cooling water flow rate has a more complex effect. The proposed dehumidifier can use less selective membranes for higher permeability and cost-effectiveness. Full article

The evaluation of pressure drops across the length of production wells is a crucial task, as it influences both the cost-effective selection of tubing and the development of an efficient production strategy, both of which are vital for maximizing oil recovery while minimizing operational expenses. To address this, our study proposes an innovative hybrid intelligent system designed to predict bottom-hole flowing pressure in vertical multiphase conditions with superior accuracy compared to existing methods using a data set of 150 field measurements amassed from Algerian fields. In this work, the applied hybrid framework is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which integrates artificial neural networks (ANN) with fuzzy logic (FL). The ANFIS model was constructed using a subtractive clustering technique after data filtering, and then its outcomes were evaluated against the most widely utilized correlations and mechanistic models. Graphical inspection and error statistics confirmed that ANFIS consistently outperformed all other approaches in terms of precision, reliability, and effectiveness. For further improvement of the ANFIS performance, a particle swarm optimization (PSO) algorithm is employed to refine the model and optimize the design of the antecedent Gaussian memberships along with the consequent linear coefficient vector. The results achieved by the hybrid ANFIS-PSO model demonstrated greater accuracy in bottom-hole pressure estimation than the conventional hybrid approach. Full article

Model predictive control offers high-performance regulation for multiphase drives but is critically dependent on the accuracy of mathematical models for prediction, making it vulnerable to parameter mismatches and uncertainties. To achieve parameter-independent control across both healthy and faulty operations, this paper proposes a novel dynamic mode decomposition with control (DMDc)-based model predictive current control (MPCC) scheme for five-phase permanent magnet synchronous motors. The core innovation lies in constructing discrete-time state-space models directly from operational data via the open-loop DMDc identification, completely eliminating reliance on explicit motor parameters. Furthermore, an improved fault-tolerant strategy is developed to mitigate the torque ripple induced by inter-turn short-circuit (ITSC) faults. This strategy estimates the key fault characteristic, the product of the short-circuit ratio and current, through a spectral decomposition of the AC component in the q-axis current variations, bypassing the need for complex parameter-dependent observers. The derived compensation currents are seamlessly integrated into the predictive control loop. Experimental results comprehensively validate the effectiveness of the proposed framework, demonstrating a performance comparable to a conventional MPCC under healthy conditions and a significant reduction in torque ripple under ITSC fault conditions, all achieved without any prior knowledge of motor parameters or the retuning of controller gains. Full article

The oceanic wind and waves, as well as the resultant ship motions, significantly impact the ship airwake and the operation of shipborne helicopters. A numerical method coupling wind, wave, ship and helicopter is developed using multiphase flow, in which the ship motions are simulated in real time by dynamic fluid body interaction module and the helicopter rotor is modeled using the momentum source approach. By integrating the ONRT ship with the UH-60A helicopter, the unsteady aerodynamic characteristics of the ship airwake and the helicopter rotor while the ship is pitching and heaving at sea state 36 that cover moderate to extreme marine environments are studied, and the time history of rotor thrust and pitch moment at four different sea states and different hovering heights are calculated. It is shown that ship motions and deck displacements in relative sea states are highly nonlinear, making the conditions faced by helicopter landing and take-off operations vary greatly from one sea state to another. The effects of each sea state when coupling waves and ship motions varies greatly. The fluctuation of velocity components and rotor air loads in sea state 6 is up to twice that of in sea state 5, while there are less differences between the velocity fluctuation and the corresponding helicopter airloads among common sea state 3~5. The dynamic aerodynamic interference resulting from the wind–wave–ship–helicopter coupling exhibits pronounced unsteady characteristics, as the hovering rotor continuously traverses areas with varying velocities and vorticities. At the most severe sea state 6, rotor thrust fluctuations can reach up to 20%, and strong perturbations of 5~10 Hz with an amplitude of 1/3 of the total range occur due to oscillating separated shear layers, which endanger the shipborne helicopter operation and needs to be eluded. Full article

This study presents a comprehensive analysis of the M/Hypo_K/1/m queuing model to evaluate the performance of marshaling yards in freight rail classification systems. The model effectively captures the complex, multi-phase nature of service and repair processes by incorporating hypo-exponential probability distributions. The marshaling yard is modeled as a finite-capacity, single-server queue subject to potential server failures, reflecting real-world disruptions. Two complementary methodological frameworks are employed: a mathematical model based on continuous-time Markov chains (CTMCs) and a simulation model constructed using Colored Petri Nets (CPNs). In the analytical approach, both service time and repair time follow hypo-exponential distributions, which are used to approximate the gamma distribution. The simulation model built in CPN Tools allows for dynamic visualization and performance evaluation. In the CPN model, we applied a gamma distribution, which allowed us to evaluate the accuracy of the approximation implemented in the analytical model. The result indicated that utilization of the marshaling yard in primary shunting was approximately 23.81%, and with secondary shunting, 22.53%. The study output proves that the hypo-exponential distribution is able to approximate the gamma distribution. This dual-framework approach, combining analytics with simulation, provides a deeper understanding of system behavior, supporting data-driven decisions for capacity planning, failure mitigation, and operational optimization in freight rail networks. Full article

Multi-phase brushless DC motors (BLDCMs) have broad prospects in the power propulsion systems of electric vehicles, submarines, electric ships, etc., due to their advantages of high efficiency and high power density. In the above application scenarios, accurately obtaining the rotor position information is crucial for ensuring the efficient and stable operation of multi-phase BLDCMs. Therefore, by analyzing the fault conditions of position sensors in this paper, a fault diagnosis method for position sensors in multi-phase brushless DC motor drive systems based on position signals and fault current characteristics is proposed, with the aim of improving the reliability of the system. This method utilizes the Hall state value determined by the Hall position signal and the current characteristics under the fault state to achieve rapid fault diagnosis and precise positioning of the position sensor. Its advantage lies in the fact that it does not require additional hardware support or complex calculations, and can efficiently identify the fault conditions of position sensors. To verify the effectiveness of the proposed method, this paper conducts experiments based on a nine-phase brushless DC motor equipped with nine Hall position sensors. The results of steady-state and dynamic experiments show that this method can achieve rapid fault diagnosis and location. Full article

Maintaining air quality in operating rooms is critical for infection control and patient safety. Particulate matter, originating from surgical instruments, personnel, and external sources, is influenced by airflow patterns and ventilation efficiency. This study employs Computational Fluid Dynamics (CFD) simulations using Simcenter STAR-CCM+ 2410 to analyze airflow and particulate behavior in a surgical-grade operating room. A steady-state solver with the k–ε turbulence model was used to replicate airflow, while the Lagrangian multiphase method simulated particle trajectories (0.5 µm, 1 µm, and 5 µm). The simulation results demonstrated close agreement with the experimental data, with average errors of 17.3%, 17.7%, and 39.7% for 0.5 µm, 1 µm, and 5 µm particles, respectively. These error margins are considered acceptable given the device’s 10% measurement sensitivity and the observed experimental asymmetry—attributable to equipment placement—which resulted in variations of 17.2%, 18.0%, and 26.5% at corresponding symmetric points. Collectively, these findings support the validity of the simulation model in accurately predicting particulate transport and deposition within the operating room environment. Findings confirm that optimizing airflow can achieve ISO Class 7 cleanroom standards and highlight the potential for future studies incorporating dynamic elements, such as personnel movement and equipment placement, to further improve contamination control in critical environments. Full article

This paper presents a new power delivery architecture to bring AC distribution voltages to core levels for computing loads using only two conversion stages with new converter topologies to potentially replace the traditional four-stage structure in the development of new data centers. This paper also includes new converters as solutions to the proposed two stages. A new switched capacitor (SC)-based AC-DC converter is proposed for the first stage and demonstrated for an intermediate bus with 90 V–110 VAC to 48–60 VDC conversion and power factor correction. The second stage also includes an SC-based hybrid converter with multi-phase operation suitable for power delivery for core voltages of up to ~1 V with a high current density. This work also reports a new phase sequence for the second stage for an extended output voltage range. Individually, the first stage was measured at 96.1% peak efficiency for output currents ranging from 0 to 4.5 A, while the second stage achieved 90.7% peak efficiency with a load range of 0–220 A at 1V. The measured peak power densities were 73 W/in³ for the first stage and 2020 W/in³ for the second stage. In combination, the direct conversion from ~110 VAC to 1 VDC led to a peak efficiency of 84.1% at 50 A, and this setup has been tested with output currents of up to 160 A, where the efficiency was 73.5%. 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