Search Results (8,452)

Canned motor pumps are widely utilized due to their distinct advantage of a completely leakage-free structure. Among them, an integrated impeller–rotor configuration is employed in the axial-flux canned motor pump, resulting in a shorter axial length and higher power density. This novel configuration allows for easy integration into space-constrained systems, such as electric vehicles, aerospace applications, and liquid-cooled servers. However, research on the internal flow characteristics of these pumps remains scarce. To address this gap, the present study investigates the internal flow across various flow rates. Numerical simulations are validated against experimental data. The average error remains below 2%. The pump achieves a peak efficiency of 68.6% at the design condition, but experiences efficiency drops of 15.0 and 25.2 percentage points under 0.5Q_d and 1.5Q_d, respectively. Results demonstrate that flow rates significantly govern internal characteristics. These include pressure, velocity, and entropy distributions, along with vortex structures and pressure fluctuations. Notably, operating at off-design conditions can intensify the internal pressure fluctuations by up to a factor of 29.4. Entropy analysis identifies major losses on blade suction sides and diffusers. These findings provide crucial hydrodynamic guidelines for low-noise thermal management systems in electric vehicles and ensuring high-reliability cooling loops in aerospace and liquid-cooled servers. Full article

Trajectory optimization during the terminal area energy management (TAEM) phase is pivotal for achieving accurate runway alignment and enhancing landing safety in autonomous aircraft operations. In the presence of initial state uncertainties in TAEM phase, conventional pseudo-spectral methods still suffer from robustness limitations and exhibit a strong dependence on the quality of the initial guess. Therefore, this paper proposes the composite African vulture optimization algorithm (CAVOA), a meta-heuristic framework designed to automate trajectory optimization. An in-depth examination of the heading alignment cone (HAC) trajectory model enables effective heading adjustments prior to landing, augmented by a tailored dynamic pressure profile to ensure safe touchdown velocities. By incorporating dynamic opposition learning, intelligent boundary processing, and composite exploration, CAVOA enhances global search efficiency. These enhancements are substantiated through comparisons with benchmark function optimization, Wilcoxon rank sum tests, and convergence analysis. Numerical simulations validate that CAVOA reliably directs autonomous aircraft to predefined touchdown states, demonstrating superior performance in complex aerial environments. Full article

Al-Cu-Fe quasicrystalline coatings were deposited on AISI 321 stainless steel substrates by high-velocity oxy-fuel (HVOF) spraying at oxygen pressures of 3.0, 3.5, and 4.0 bar. The influence of oxygen pressure on the phase composition, microstructure, porosity, corrosion behavior, thermal stability, and microhardness of the coatings was investigated using X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS), ImageJ porosity analysis, electrochemical corrosion testing in 3.5 wt.% NaCl solution, simultaneous thermal analysis (TGA/DSC), and microhardness measurements. XRD analysis revealed the formation of quasicrystalline-related intermetallic phases together with Al, Fe₃Al₁₃, FeAl, Fe₃O₄, CuFe₂O₄, Cu₂O, and CuO phases. The coating deposited at 3.5 bar exhibited the lowest porosity (5.37%), the most homogeneous microstructure, and the largest residual coating thickness after corrosion testing. SEM and EDS analyses indicated that corrosion preferentially initiated at pores, splat boundaries, and phase interfaces, while the coating produced at 3.5 bar demonstrated the most stable surface condition after exposure to a 3.5 wt.% NaCl solution. Thermal analysis showed that all coatings remained stable up to 900 °C. Sample (a) exhibited the lowest mass loss and the highest thermal stability, whereas sample (b) demonstrated the most favorable combination of structural integrity, phase ordering, coating density, corrosion-related performance, and thermal stability. Microhardness values of the coatings ranged from 754 to 778 HV, significantly exceeding that of the AISI 321 substrate. The results demonstrate that oxygen pressure is a critical parameter controlling the microstructure and functional properties of HVOF-sprayed Al-Cu-Fe coatings, with 3.5 bar providing the most balanced set of properties. Full article

This work presents the kinematic analysis of a redundant planar parallel manipulator within the framework of screw theory. The main contribution of this work is the introduction and kinematic modeling of a novel redundant planar parallel manipulator topology composed exclusively of revolute joints. The proposed architecture is motivated by the search for structurally simple mechanisms with favorable analytical properties for screw-theoretic formulation and potential applications in robotic systems requiring compact and efficient planar motion. For completeness, the displacement analysis is included. Thanks to the simple topology of the otherwise complex mechanism, the inverse–forward displacement problem is resolved through straightforward quadratic equations. The velocity input–output relationship is derived without reliance on passive joint rate velocities, and the acceleration input–output equation is obtained independently of passive joint rate accelerations. These simplifications are achieved by exploiting reciprocal line properties. Numerical examples are provided to illustrate the robustness and effectiveness of the proposed kinematic analysis method across the main topics addressed in this contribution. Full article

Icing phenomena on wind turbine blades and components are a major problem, causing downtimes that increase maintenance costs, reducing the blade’s lifespan, or in severe cases, even leading to component damage. A nanofiber-based bi-layer liquid-infused surface (BLIS) coating was prepared and characterized, combining good adhesion to wind turbine blades with low ice adhesion. The BLIS coating was produced by a new method combining electrospinning and a heat treatment step, containing a poly ethyl-2-cyanoacrylate (PECA)-based adhesive layer, a slippery layer of poly vinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) copolymer, and an infiltrated perfluoropolyether lubricant. Thermogravimetric analysis (TGA) was used to ensure the thermal stability of the polymers in the nanofiber coating layers and to optimize the heat treatment process of the layers. Microstructural changes were studied by scanning electron microscopy (SEM) and surface roughness measurements. Contact angle measurements and sliding velocity tests on wind turbine blade segments at icing conditions of 0 °C and +5 °C indicate that the water sliding properties of the BLIS coating were improved compared to uncoated blades. In addition, coated blade segments showed a 50% lower ice adhesion strength than uncoated blades. Full article

Global satellite missions with the capability to measure ocean surface currents are continually being proposed. This new observation type is expected to significantly improve ocean model analysis and forecast skill. The potential impact of assimilating sea surface currents from the proposed wide-swath Ocean Dynamics and Surface Exchange with the Atmosphere (ODYSEA) mission is investigated in this study. An Observing System Simulation Experiment (OSSE) is set up with a 1 km Navy Coastal Ocean Model (NCOM) analysis-forecasting system in the Gulf of America domain over a 4-month time period. When compared to an experiment with only the standard data streams of temperature, salinity, and sea surface height anomaly observations from in situ and satellite platforms assimilated, the inclusion of ODYSEA-like sea surface current observations leads to a 13% and 17% reduction in the domain and time averaged root mean squared error (RMSE) for surface u and v components, respectively, as well as an improvement in the current velocity throughout the upper water column. The assimilation of the sea surface current observations also leads to an improvement in the model sea surface height, although there is a negligible to slight degradation in the temperature and salinity at depth, which is likely due to the explicit geostrophic assumption made within the velocity assimilation methodology. Full article

Shear-wave velocity (Vs) estimation from the Standard Penetration Test (SPT) can support preliminary site characterization when direct geophysical data are limited, but empirical correlations require validation schemes that reflect transferability between sites. This study evaluates Vs prediction using an interval-paired dataset derived from geotechnical investigations of school foundations in Piura, Peru. Its novelty lies in comparing the raw SPT blow count (NSPT) and the overburden- and energy-corrected SPT blow count ((N1)60) on the same strict common sample, using grouped cross-validation by school, thereby emphasizing transferability across sites rather than only internal fit. Five predictive scenarios were tested, from penetration-only formulations to geotechnically enriched specifications. The lowest grouped out-of-fold error among the evaluated models was obtained by a generalized power baseline using (N1)60 and the integral geotechnical predictor set, yielding root mean square error (RMSE) = 80.48 m/s, mean absolute error (MAE) = 60.15 m/s, and coefficient of determination (R2) = 0.338. This moderate R2 indicates limited standalone predictive capacity under transfer to unseen schools; therefore, the model is interpreted as a preliminary transfer-oriented correlation rather than as a substitute for direct Vs measurements or as an independent design equation. In the complementary full-data analysis, the strongest descriptive fit was obtained with Hist Gradient Boosting, whereas the strongest explicit equation corresponded to the log-semi baseline. Overall, the findings show that externally validated transferability, descriptive full-data fit, and equation-based interpretability represent different analytical roles in Vs-SPT modeling. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

Background: Some widely used wildland fire behavior models, like the Fire Area Simulator (FARSITE), propagate fire fronts by computing the front-normal velocity (spread rate) as a function of local inputs and the front-normal direction. Such models are sometimes observed to cause the collapse of crown fires into sharp wedge shapes that eliminate heading fire behavior. Aims: We set out to document this phenomenon and, more generally, understand the relationships between fire shapes and spread rate functions. Methods: The phenomenon is studied both mathematically and through simulation experiments. Non-smooth fire fronts are theorized mathematically by an Eikonal partial differential equation ($H(x,\tau ,D\tau )=1$), where the unknown $\tau \left(x\right)$ is the time-of-arrival function and the Hamiltonian $H(x,t,p)$ is positively homogeneous and possibly non-convex in p; convex analysis is used to study viscosity solutions in constant conditions. Results: We show that a fire spread model preserves the smoothness of fire fronts if and only if it is equivalent to using the Huygens principle. Nontrivially, this is equivalent to a convexity criterion on the inverse spread rate profile, which is then the polar dual of the Huygens wavelet; this corresponds to Hamiltonian–Lagrangian duality. The relevance of smoothness-destroying models to crown fire is debated. Exact analytical formulas are derived for fire growth in constant conditions. Conclusions: Our understanding of fire spread models is improved by solving the spread equations in more general ways than previously known. In particular, the collapse of heading crown fires into sharp shapes is now explained. Smoothness-destroying spread models cannot be simulated by algorithms based on travel time like cellular automata; their general well-definedness remains an open question. Fire modelers can use these findings to guide their search for improved crown fire models, and more generally to verify the accuracy of numerical implementations. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane’s movements. The Neural Network algorithm optimized the crane’s movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane’s structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane’s fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life. Full article

Background and Objectives: Assessing knee extensor (KE) strength is important for detecting muscle weakness in older adults, yet dynamometry is often impractical in community settings. This study examined whether smartphone-derived kinematics during the Five Times Sit-to-Stand Test (FTSST) could predict seated isometric KE strength. Materials and Methods: A cross-sectional study included 105 community-dwelling older adults (68.19 ± 5.85 years). A smartphone application extracted rising time, vertical velocity, and smartphone-estimated relative vertical power during the FTSST. KE strength was measured as maximum voluntary isometric contraction (MVIC) using fixed-frame dynamometry with a Lafayette dynamometer head. Bioelectrical impedance-derived body composition variables were reported descriptively but excluded from the primary prediction models to maintain a transparent movement-based model independent of device-specific body-composition estimates. Hierarchical regression models used smartphone-derived variables and transparent non-BIA covariates. Agreement was examined using Bland–Altman analysis. Results: Smartphone-estimated relative vertical power showed the strongest correlation with MVIC (r = 0.787, p < 0.001). The combined model including sex, age, femur length, and smartphone-estimated relative vertical power explained 71.6% of MVIC variance (adjusted R² = 0.716, SEE = 3.276 kg), outperforming vertical velocity, rising time, and total FTSST time models. Internal validation using repeated 10-fold cross-validation showed CV-R² = 0.701, CV-adjusted R² = 0.689, CV-RMSE = 3.343 kg, and CV-MAE = 2.739 kg. Bland–Altman analysis showed minimal mean bias (0.00 kg), 95% limits of agreement from −6.296 to 6.296 kg, and significant proportional bias (slope = −0.172, p = 0.002), indicating overestimation in weaker individuals and underestimation in stronger individuals. Conclusions: Consistent with our hypothesis, smartphone-estimated relative vertical power was the strongest kinematic predictor of seated isometric KE strength among the evaluated FTSST-derived variables. This approach may support community screening and monitoring, but it should not replace standardized dynamometry for precise individual-level strength quantification. Full article

This study presents a transient computational fluid dynamics (CFD) analysis of dielectric flow behaviour and debris transport in micro-EDM milling, considering the effects of dielectric properties, inter-electrode gap (IEG) size (20–30 µm), and tool rotational speed (400–850 rpm). Four dielectric media, nitrogen gas, deionized water, HEDMA111 EDM oil, and sunflower seed oil, were investigated using a two-dimensional FEM-based model coupled with particle tracking simulations to evaluate debris mobility within the machining region. The results demonstrate that dielectric properties, particularly viscosity, strongly influence hydrodynamic behaviour and particle transport within the IEG. Under the adopted equal mass flow rate condition, nitrogen gas exhibited the highest flow velocities and the fastest debris evacuation due to the combined effects of its low viscosity and the resulting higher inlet velocity. Deionized water and HEDMA111 oil exhibit comparable intermediate behaviour, indicating that moderate viscosity variations within liquid dielectrics do not significantly alter the overall flow regime. In contrast, sunflower seed oil generates the most damped flow conditions, with reduced velocities and prolonged particle residence due to increased viscous resistance. Variations in IEG size produce only minor changes in evacuation efficiency compared with the dominant influence of dielectric properties, while tool rotational speed primarily affects velocity magnitude without altering qualitative transport behaviour. Full article

The automotive panoramic roof exhibits a large-size and thin-wall geometry, with a length-to-thickness ratio approaching the thousand level. This geometric feature makes its forming quality highly sensitive to forming conditions. During the glass molding process, variations in temperature evolution, loading, and cooling parameters may lead to residual stress accumulation and springback deformation, thereby affecting dimensional accuracy and final forming quality. In this study, a full-process finite element model was established and combined with an L16(4^5) orthogonal design to investigate the effects of five key process parameters—heating temperature, holding time, quenching air velocity, quenching air pressure, and quenching time—on the mean residual stress and mean springback displacement in the glass molding process (GMP). The results showed that, within the given parameter ranges, heating temperature, holding time, and quenching time had relatively pronounced effects on the mean residual stress; the mean residual stress was relatively low when the heating temperature was 680 °C, the holding time was 3 s, and the quenching time was 12 s. Heating temperature, quenching air velocity, and quenching time had relatively pronounced effects on the mean springback displacement; the mean springback displacement was relatively low when the heating temperature was 677.5 °C, the quenching air velocity was 13 m/s, and the quenching time was 10 s. Based on the orthogonal analysis, regression models for the mean residual stress and mean springback displacement were further developed, and parameter combinations were screened using the NSGA-III method. Experimental validation showed that the relative error of the mean residual stress was controlled within 15%, indicating that the established model could, to some extent, capture the relationship between process parameters and forming quality indicators, thereby providing guidance for precision forming and process optimization of large-scale thin-walled automotive panoramic roofs. Full article

Existing studies on energy tunnels mainly focus on short-term heat transfer and neglect long-term thermal accumulation. This paper establishes a one-dimensional unsteady heat transfer model using Robin boundary conditions, considering air–lining coupled heat transfer and seasonal tunnel air temperature variations. The model is verified with experimental and numerical results, and the relative error is less than 1%. Simulations of 20-year continuous operation show that the host rock temperature presents obvious periodic fluctuations. The thermal influence zone expands rapidly at the initial operation stage and gradually stabilizes. Sensitivity analysis indicates that thermal conductivity, air flow velocity and circulating fluid velocity significantly affect the long-term thermal performance. Higher thermal conductivity speeds up heat diffusion, higher air velocity strengthens convective heat transfer, and higher fluid velocity improves heat exchange efficiency but increases pumping consumption. The model can accurately predict long-term temperature evolution, providing theoretical support for the design and operation optimization of energy tunnels. Full article