Search Results (16,484)

It is demonstrated that the general representation of a dynamic multiphase wellbore flow model may be identified from the available physical measurements. The proposed approach is based on the techniques of numerical optimization and also requires the availability of solvers for the general type of partial differential equations describing two-phase gas–oil flow. A solution is obtained both for the case of the homogeneous no-slip model and the drift-flux model with velocity slip. The feasibility of the proposed approach for system identification and parameter estimation has been demonstrated using simulated flow data. Two distinct scenarios have been considered: firstly, when the well is fully instrumented with multiple pressure sensors and a multiphase flow meter, and secondly, when only a single downhole pressure gauge is available. Full article

Combustion at the meso-scale is constrained by large surface-to-volume ratios that shorten residence time and intensify wall heat loss. We perform steady, three-dimensional CFD of two asymmetric vortex combustors: Model A (compact) and Model B (larger-volume) over inlet-air mass flow rates $\dot{m}$ (40–170 mg s⁻¹) and equivalence ratios ϕ (0.7–1.5), using an Eddy-Dissipation closure for turbulence–chemistry interactions. A six-mesh independence study (the best mesh is 113,133 nodes) yields ≤ 1.5% variation in core fields and ~2.6% absolute temperature error at a benchmark station. Results show that swirl-induced CRZ governs mixing and flame anchoring: Model A develops higher swirl envelopes (S up to ~6.5) and strong near-inlet heat-flux density but becomes breakdown-prone at the highest loading; Model B maintains a centered, coherent Central Recirculation Zone (CRZ) with lower $u_{\theta }$ (~3.2 m s⁻¹) and S ≈ 1.2–1.6, distributing heat more uniformly downstream. Peak flame temperatures (~2100–2140 K) occur at ϕ ≈ 1.0–1.3, remaining sub-adiabatic due to wall heat loss and dilution. Within this regime and $\dot{m}$ ≈ 85–130 mg s⁻¹, the system balances intensity against flow coherence, defining a stable, thermally efficient operating window for portable micro-power and thermoelectric applications. Full article

The objective of this study is to analyze spatial entropy flows that reveal the directional dynamics of climate change—patterns that remain obscured in traditional statistical analyses. This approach enables the identification of pathways for “climate information transport”, highlights associations with atmospheric circulation types, and allows for the localization of both sources and “informational voids”—regions where entropy is dissipated. The analytical framework is grounded in a quantitative assessment of long-term climate variability across Europe over the period 1901–2010, utilizing Shannon entropy as a measure of atmospheric system uncertainty and variability. The underlying assumption is that the variability of temperature and precipitation reflects the inherently dynamic character of climate as a nonlinear system prone to fluctuations. The study focuses on calculating entropy estimated within a 70-year moving window for each calendar month, using bivariate distributions of temperature and precipitation modeled with copula functions. Marginal distributions were selected based on the Akaike Information Criterion (AIC). To improve the accuracy of the estimation, a block bootstrap resampling technique was applied, along with numerical integration to compute the Shannon entropy values at each of the 4165 grid points with a spatial resolution of 0.5° × 0.5°. The results indicate that entropy and its derivative are complementary indicators of atmospheric system instability—entropy proving effective in long-term diagnostics, while its derivative provides insight into the short-term forecasting of abrupt changes. A lag analysis and Spearman rank correlation between entropy values and their potential supported the investigation of how circulation variability influences the occurrence of extreme precipitation events. Particularly noteworthy is the temporal derivative of entropy, which revealed strong nonlinear relationships between local dynamic conditions and climatic extremes. A spatial analysis of the information entropy field was also conducted, revealing distinct structures with varying degrees of climatic complexity on a continental scale. This field appears to be clearly structured, reflecting not only the directional patterns of change but also the potential sources of meteorological fluctuations. A field-theory-based spatial classification allows for the identification of transitional regions—areas with heightened susceptibility to shifts in local dynamics—as well as entropy source and sink regions. The study is embedded within the Fokker–Planck formalism, wherein the change in the stochastic distribution characterizes the rate of entropy production. In this context, regions of positive divergence are interpreted as active generators of variability, while sink regions function as stabilizing zones that dampen fluctuations. Full article

Operationalizing Earth Observation (EO)-based Machine Learning (ML) algorithms (or GeoML) for ingestion in environmental Digital Twins remains a challenging task due to the complexities associated with balancing real-time inference with cost, data, and infrastructure requirements. In the field of GHG monitoring, most GeoML models of land use CO₂ fluxes remain at the proof-of-concept stage, limiting their use in policy and land management for net-zero goals. In this study, we develop and demonstrate a Digital Twin-ready framework to operationalize a pre-trained Random Forest model that estimates the Net Ecosystem Exchange of CO₂ (NEE) from drained peatlands into a biweekly, field-scale CO₂ flux monitoring system using EO and weather data. The system achieves an average response time of 6.12 s, retains 98% accuracy of the underlying model, and predicts the NEE of CO₂ with an R² of 0.76 and NRMSE of 8%. It is characterized by hybrid data ingestion (combining non-time-critical and real-time retrieval), automated biweekly data updates, efficient storage, and a user-friendly front-end. The underlying framework, which is part of an operational Digital Twin under the UK Research & Innovation AI for Net Zero project consortium, is built using open source tools for data access and processing (including the Copernicus Data Space Ecosystem OpenEO API and Open-Meteo API), automation (Jenkins), and GUI development (Leaflet, NiceGIU, etc.). The applicability of the system is demonstrated through running real-world use-cases relevant to farmers and policymakers concerned with the management of arable peatlands in England. Overall, the lightweight, modular framework presented here integrates seamlessly into Digital Twins and is easily adaptable to other GeoMLs, providing a practical foundation for operational use in environmental monitoring and decision-making. Full article

Rare-earth barium copper oxide (REBCO) high-temperature superconductors, owing to their ability to maintain high critical current density (J_c) under liquid-nitrogen-temperature and high-magnetic-field conditions, are widely regarded as one of the most promising material systems among all superconductors. This review systematically summarizes fabrication strategies for REBCO coated conductors, with a focus on pulsed laser deposition (PLD) for achieving high-quality epitaxial growth with precise composition control. To enhance in-field performance, strategies for introducing artificial pinning centers (APCs) are examined, including rare-earth element doping, substrate surface decoration, and nanoscale secondary phase incorporation. The mechanisms of vortex pinning from different dimensional defects and their synergistic effects are compared. Finally, we suggest potential future directions aimed at further enhancing the superconducting properties. Full article

The line-start permanent-magnet (LSPM) motor combines the direct-on-line starting of induction motors with the high efficiency of permanent-magnet (PM) synchronous motors, but conventional interior PM designs are difficult to manufacture and surface PM (SPM) designs often suffer from limited starting torque and reduced efficiency. This paper investigates consequent-pole SPM designs, in which the number of magnets is reduced by half while maintaining equal magnet volume, enabling simple rotor construction and improved starting performance. A prototype is manufactured and tested, confirming smooth synchronization under load. Efficiency is constrained by the non-sinusoidal flux distribution of the consequent-pole structure. Rotor design strategies enlarging the air gap near the iron poles are analyzed, and a finite element method analysis shows improved torque and efficiency without loss of starting capability. Full article

Discarded NdFeB permanent magnets will become a significant source of rare earth elements (REEs) in the future. Electric vehicle (EV) motors utilize 2–5 kg of NdFeB magnets, and researchers are prioritizing the development of suitable extraction technologies. The objective of our research is to separate metal materials (Al, Cu, Fe and FEEs) from EV motors, based on their melting temperatures. REE magnets that pose the greatest challenge are melted together with the electrical steel of the motor, and the potential for extracting REEs in a selective manner from the molten steel was examined based on their significant oxidation potential using FeO–SiO₂ compounds, which act as an oxidizing slag-forming agent, to test the extraction method. Fayalite (2FeO·SiO₂) is the most easily created and ideal eutectic compound for carrying oxygen (FeO) and forming slag (${\mathrm{S}\mathrm{i}\mathrm{O}}_4^{4^{-}}$), typically generated during copper smelting. In this experiment, copper slag was used and the results were compared to a smelting test, which had previously used a synthesized fayalite flux as a model. The smelting test, utilizing synthesized fayalite flux, yielded a 91% Nd recovery rate. The Nd recovery rate in the smelting test with copper slag hit a high of 64.81%, influenced by the smelting’s holding time. The steel contained 0.08% Nd. Iron was recovered from the copper slag at a rate of 73%. During the smelting test, it was observed that the reaction between Nd₂O₃ and the Al₂O₃ crucible resulted in the formation of a layer on the surface of the crucible, diffusion into the crucible itself, and a subsequent reduction in the efficiency of Nd recovery. Full article

Oceanic whitecaps are caused by wave breaking and are very important in air–sea interactions. Usually, whitecap coverage is considered a key factor in representing the role of whitecaps. However, the accurate identification of whitecap coverage in videos under dynamic marine conditions is a tough task. An EMA-SE-ResUNet deep learning model was proposed in this study to address this challenge. Based on a foundation of residual network (ResNet)-50 as the encoder and U-Net as the decoder, the model incorporated efficient multi-scale attention (EMA) module and squeeze-and-excitation network (SENet) module to improve its performance. By employing a dynamic weight allocation strategy and a channel attention mechanism, the model effectively strengthens the feature representation capability for whitecap edges while suppressing interference from wave textures and illumination noise. The model’s adaptability to complex sea surface scenarios was enhanced through the integration of data augmentation techniques and an optimized joint loss function. By applying the proposed model to a dataset collected by a shipborne camera system deployed during a comprehensive fishery resource survey in the northwest Pacific, the model results outperformed main segmentation algorithms, including U-Net, DeepLabv3+, HRNet, and PSPNet, in key metrics: whitecap intersection over union (IoU_W) = 73.32%, pixel absolute error (PAE) = 0.081%, and whitecap F1-score (F1_W) = 84.60. Compared to the traditional U-Net model, it achieved an absolute improvement of 2.1% in IoU_W while reducing computational load (GFLOPs) by 57.3% and achieving synergistic optimization of accuracy and real-time performance. This study can provide highly reliable technical support for studies on air–sea flux quantification and marine aerosol generation. Full article

Condition monitoring and fault detection in induction motors (IMs) are priorities in the industrial environment to secure safe conditions for the processes and production. Convolutional Neural Networks (CNNs) are gaining interest in these tasks as they allow automatic extraction of features from the inputs, sometimes Time–Frequency Distributions (TFDs) obtained with various transforms, directly into large models for data classification. This work presents a proposal for the application of a widely used texture analysis tool in the medical field, the 2D Discrete Orthonormal Stockwell Transform (2D-DOST), to improve the accuracy of a lightweight CNN when using different TFDs and comparing the results to the use of the TFDs in RGB and grayscale. The results show that the use of the 2D-DOST improves the classification accuracy in a two to five percent range for all motor conditions under study, while having minimal variations to the training times when compared to RGB or grayscale images, opening the possibility for the use of image processing tools on TFDs to improve automatic feature extraction while using small CNN models. Full article

Polymeric reverse osmosis (RO) membranes are critical for producing ultrapure water for hemodialysis process, but once they reach their end-of-life (EoL) stage, mainly due to fouling, they are usually discarded—adding to the growing challenges of medical waste management. This study explores a sustainable alternative by rehabilitating EoL thin-film composite (TFC) membrane and its reuse in recovery of spent dialysate. Using different cleaning agents that included citric acid (CA), EDTA, sodium lauryl sulfate (SLS), and sodium dodecyl sulfate (SDS), the mixture of CA and SLS (1:1) exhibited the most effective combination for balanced flux recovery, salt rejection, and creatinine clearance at lower TMP, achieving 90% conductivity reduction, 46.89 L/m²/h water flux, and 1.24 L/m²/h/bar permeance. FTIR, SEM, and EDX results confirmed the removal of both organic and inorganic foulants, while further process optimization revealed the critical role of cleaning temperature, SLS ratio and pressure on water permeability and improving creatinine removal. Under the optimal operational conditions, 99.89% creatinine removal, while restoring up to 80% hydraulic performance, yielding water flux and permeance of 59.36 L/m²/h and 1.79 L/m²/h/bar, respectively. These findings suggest that reduced dialysate production costs and minimize environmental impact can be significantly, achieved by extending the useful life of dialysate membranes, thereby opening a pathway toward implementing closed-loop water management and circular economy practices at dialysis centers. Full article

The guest room and aisle of electric high-speed maglev train must be shielded from leakage magnetic flux produced by superconducting strong magnetic field. To reduce magnetic leakage, the superconducting magnetic levitation system structure is obtained by extended lagrangian optimization method. The optimized superconducting coil structure has the advantages of reducing magnetic leakage, improving magnetic field utilization, reducing the weight of the magnetic isolation plate and the weight of the maglev train, and enhancing the load-bearing capacity of the maglev train. Based on optimized superconducting coil parameters for high-speed maglev, the magnetic shielding effect at the aisle and the guest room, the magnetic flux density distribution at the magnetic shielding is calculated and analyzed through analytical calculation. The relevant conclusions indicate that the magnetic suspension structure has the advantages of reducing end coil leakage flux and the weight of the high-speed maglev train. Full article

The effects of pretreatment pH value, operating pressure, and concentration factors on the performance of nanofiltration membrane concentration and the recovery of phosphorus-containing wastewater were systematically studied. A novel pretreatment strategy using solid sodium hydroxide was developed to adjust the feed solution pH, achieving optimal solid removal and minimized conductivity at pH = 5. Unlike conventional calcium-based methods, this approach avoids excessive chemical sludge formation and mitigates membrane scaling, enhancing system stability. Experimental results demonstrate that both phosphorus rejection and desalination efficiency are significantly influenced by feed solution pH, operating pressure, and concentration ratio. While increasing pH and pressure improve total phosphorus (TP) rejection and desalination rates, these benefits are accompanied by reduced membrane flux due to elevated osmotic pressure and intensified concentration polarization. The membrane exhibited optimal performance at a feed pH of 5 and an operating pressure of 3 MPa, with sustained flux and enhanced separation efficiency. Under these conditions, when the wastewater was concentrated fivefold at 25 °C, the TP rejection rate and desalination efficiency reached 92.9% and 91.8%, respectively. Full article

Known for its excellent adsorption and molecular sieving properties, CHA-type zeolite is highly effective in separation technologies, including alcohol dehydration and gas separation. Despite their advantages, especially in terms of energy savings, the prolonged synthesis time of zeolite membranes limits their commercial adoption. The remarkably rapid synthesis of CHA membranes was demonstrated using an exceptionally small tubular reactor (ID: 4.0 mm, OD: 6.0 mm, L: 135 mm). The formation of membranes could be observed after 10 min of synthesis, and a membrane with a thickness of 0.65 µm, αH₂O/2-PrOH of 1662, and a total flux of 2.97 kg/(m² h), was produced after 40 min of synthesis in an oil bath. Using the synthesis time of 40 min and longer, membranes with good quality and enhanced reproducibility were produced, as the number of defects was reduced. These findings demonstrate the potential for rapid, scalable CHA membrane production, paving the way for broader industrial applications. Full article

Residual magnetism in the core of a power transformer can lead to an increased inrush current during closing, which may trigger relay protection malfunctions and cause equipment aging. Accurate detection of residual magnetism is crucial for grid safety. Traditional offline detection requires interrupting operation, while online methods are susceptible to interference and have limited accuracy. This paper proposes a method for detecting residual magnetism in power transformers based on the hysteresis area of magnetization. First, the magnetic flux distribution of the transformer is analyzed through finite element simulation, revealing that low-frequency excitation can make the core’s magnetic flux density distribution more uniform, and that the leakage flux and the flux inside the core have similar characteristics, which helps to determine the optimal position for flux detection. Next, the relationship between the small hysteresis loop area and residual magnetism is studied, revealing a monotonic mapping relationship between the normalized area of the negative hysteresis loop under current pulse excitation and the residual magnetism. Finally, experimental verification shows that this method effectively detects residual magnetism under different levels and operational conditions. The method is non-invasive, real-time, and highly resistant to interference, offering a new approach for residual magnetism detection in power transformers. Full article

The present investigation conducts a comparative analysis of the nucleate pool boiling heat transfer performance of two dielectric fluids, a low-GWP hydrofluoroolefin-based fluid (commercially known as Opteon™ SF33, referred to hereafter as SF33) and a perfluoropolyether-based fluid with a high GWP (commercially known as Galden^R HT55, referred to hereafter as HT55) under atmospheric pressure conditions. Pool boiling experiments and visual observations were performed to assess essential performance parameters, such as critical heat flux, heat transfer coefficient, and boiling dynamics. SF33 exhibits enhanced heat transfer performance, achieving markedly higher heat transfer coefficient values at all the heat flux levels and attaining superior critical heat flux relative to HT55. The results show that SF33 provides a consistently higher heat transfer coefficient, reaching approximately 12 W/m²·K at maximum heat flux, compared to only 6 W/m²·K for HT55, representing nearly a 100% improvement. The visual observations indicated that reduced surface tension and increased latent heat of vaporization of SF33 facilitate more frequent bubble nucleation and smaller bubble departure, thereby enhancing its boiling performance. Properties of SF33 render it a superior candidate for high-performance cooling systems in data centers and power electronics. The study concludes that SF33 is a more efficient and adaptable fluid for next-generation cooling systems, providing superior heat dissipation and energy efficiency relative to HT55. Full article