Search Results (713)

This work presents the development and validation of a modular low-power wind turbine emulator (WTE) specifically designed for academic research and distributed generation applications. The primary objective is to provide a flexible and cost-effective test bench capable of replicating the aerodynamic and mechanical performance of a bladed rotor without the need for wind tunnels or specific field conditions. The emulator integrates a 4.5 kW three-phase induction machine as the motor and a 1 kW permanent magnet synchronous generator (PMSG). The system is managed by an ARM Cortex M7 microcontroller, which gives instructions to a Siemens Sinamics Variable Frequency Drive (VFD) that is used for torque vector control, offering superior dynamic response to wind speed variations. The aerodynamic characteristics were previously derived using blade element momentum (BEM) theory and validated using MATLAB/Simulink simulations. Unlike traditional steady-state emulators, this study addresses dynamic behavior through an autonomous control algorithm that reduces mechanical stress and compensates for inertia differences. Experimental tests conducted in a grid-connected scenario using a commercial on-grid inverter showed high correlation between the emulator’s output and the field data of a real EOLOCAL AG1000 turbine. The results confirm the system’s reliability as a platform for evaluating power conversion systems and for future expansions, such as blade pitch control emulation. Full article

This study presents a numerical investigation of the effects of atmospheric icing on the aerodynamic performance and power output of the NREL 5 MW reference wind turbine. In cold climate regions, ice accretion on wind turbine blades significantly alters the airfoil geometry, leading to aerodynamic degradation characterized by increased drag, reduced lift, and substantial power losses. Understanding these effects is therefore essential for reliable performance prediction and efficient turbine operation under icing conditions. To address this problem, numerical simulations were conducted on six representative blade sections using the FENSAP-ICE framework, which integrates flow field calculations, droplet transport, and ice accretion modeling within a unified computational environment. The analyses were performed under different atmospheric icing conditions, considering liquid water content values of 0.22 g/m³ and 0.50 g/m³ and ambient temperatures of −2.5 °C and −10 °C. The median volumetric diameter was fixed at 20 µm, and the icing duration was set to one hour for all cases, allowing for both glaze and rime ice formations to be systematically examined. The results reveal that ice accretion becomes increasingly pronounced toward the blade tip, mainly due to higher relative velocities and increased collection efficiency in the outer sections. Glaze icing conditions produce irregular horn-shaped ice formations and lead to severe aerodynamic degradation, whereas rime ice forms more compact structures near the leading edge and results in comparatively lower performance losses. The degraded aerodynamic coefficients obtained from the iced airfoils were subsequently incorporated into BEM-based power calculations, indicating that total power losses can reach up to 40% under severe icing conditions, with the outer blade sections contributing most significantly to this reduction. Furthermore, an economic assessment based on annual energy losses highlights the substantial impact of atmospheric icing on wind turbine performance and operational costs. Full article

This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management. Full article

Offshore wind energy is an attractive renewable energy source due to its advantages. However, the chaotic marine environment makes the analysis of offshore wind energy extremely difficult. Furthermore, studying the behavior of wind turbines under rare and hazardous natural events such as rogue waves is crucial for the safety and operation of wind turbines and the platforms mounted on them. Therefore, this study numerically investigates the aerodynamic, hydrodynamic, and structural properties of the National Renewable Energy Laboratory (NREL) 5 MW wind turbines under the effect of the Draupner wave, the first marine rogue wave ever recorded. To this end, the geometric and structural information of the NREL 5 MW wind turbines mounted on monopile and jacket platforms is explained. The characteristics of the Draupner wave and the variations in its wave height time series are investigated. The recorded wave height time series values are imported into the QBlade program, and the dynamics of NREL 5MW monopile and jacket wind turbines are simulated. Based on the simulation data, the aerodynamic, hydrodynamic, and structural properties of these structures are examined and analyzed. The results demonstrate that Draupner waves have a significant effect on the aerodynamic, hydrodynamic, and structural parameters of the wind turbines. These parameters are observed to reach their highest values, particularly between the 250th and 280th seconds, when the Draupner wave height reaches its peak. Our findings indicate that the jacket structure experienced higher total forces due to its larger wetted surface area and geometric complexity, while the monopile foundation showed higher inertial loading in the X-direction because of its larger added mass. Additionally, we observed that total aerodynamic power generation is significantly affected by the passage of the Draupner rogue wave. We discuss our findings and their limitations. This numerical study is intended to be a milestone for researchers working on the structural health of offshore wind turbines and platforms under the effect of rogue waves. Full article

The common view is that, in boundary lubrication, the load is transmitted solely through directly contacting asperities due to the extremely limited lubricant availability or lacking hydrodynamic force generation. The asperities may transmit force via their boundary layers or a thin liquid lubricant film in between. Hypothesizing that the latter mechanism dominates, a friction simulation model was developed for the boundary lubrication regime to investigate whether the contact shear force, and consequently the friction coefficient, are exclusively governed by the shearing of this thin lubricant film between the contacting asperities. In the very thin films at the asperity contacts, the extremely high pressures suggest that the limiting shear stress regime prevails. This means that the shear stress between two asperities sliding relative to each other is equal to the limiting shear stress corresponding to the local pressure. The model is applied to calculate the friction coefficient of a lubricated two-disc tribological contact before and after a wear experiment. It comprises a contact model, based on the Boundary Element Method (BEM), to determine the pressure distribution at the asperity level; a limiting shear stress model to evaluate the corresponding shear stress as a function of pressure; and a friction model to compute the overall coefficient of friction. Two base oils are considered in the analysis, a mineral oil and a synthetic oil, both unadditivated. The calculated coefficients of friction are compared with experimental results, the limitations of the modeling approach are discussed, and an updated model is proposed for the specific case of two contacting steel bodies lubricated with additive-free oil. Full article

Circadian rhythm regulates several physiological functions, and influences endocrine and metabolic responses in mammals, with cortisol acting as important modulator of this mechanism. Cortisol secretion is affected by both internal and external factors and is intensified under stress conditions. The response to surgical stress is consistently observed after surgical procedures, such as ovariohysterectomy (OVH). Therefore, this study aimed to evaluate the influence of the circadian rhythm on the surgical stress response following elective OVH in healthy bitches. Twenty patients weighing between 10 and 20 kg were hospitalized 48 h before surgery and remained hospitalized for 48 h postoperative. The animals were randomly allocated into two groups and underwent OVH either in the morning (6–8 h—a.m., GAM) or at night (18–20 h—p.m., GPM). Surgical procedures were standardized with respect to the surgical team, technique applied and duration; this was carried out in order to induce a comparable level of surgical stress. Physical parameters (systolic blood pressure, heart rate, respiratory rate and rectal temperature) and laboratorial analyses (cortisol, leukogram, protein thiols, no protein thiols, vitamin C, ferric reducing ability of plasma and thiobarbituric acid reactive substances) were assessed immediately before surgery and at 2, 4, 6, 12, 24 and 48 h, as well as 14 days postoperatively. No significant changes in cortisol profile were detected. However, significant alteration in the respiratory rate, rectal temperature, time to first urine, and lipid peroxidation were observed in the GPM group, suggesting that surgeries performed at night induce greater disturbances in homeostasis than those performed in the morning. Full article

In the analysis and design of wind turbines, aeroelastic analysis is required that considers elastic structure and control in addition to aerodynamic characteristics. In recent years, with the increase in size and reduction in the cost of wind turbines, problems have emerged that cannot be addressed with the standard analysis methods. The accuracy of the Blade Element and Momentum (BEM) theory, which is the most common aerodynamic analysis and design theory, is reduced in conditions where three-dimensional effects such as radial flow are not negligible. Furthermore, full-model Computational Fluid Dynamics (CFD), which is commonly used for complex aerodynamic problems, is not applicable for the design calculation of wind turbines considering itscomputational demands. To address these challenges, the Actuator Line Model (ALM) can be utilized as practical load analysis methods that account for structural elasticity, control, and fluctuating winds—offering a level of fidelity between both approaches. However, the conventional ALM does not account for the stall delay (SD) observed in the inboard section of rotor. In this study, an ALM based on CFD is developed by incorporating Snel’s stall delay model, which was developed for BEM. Additionally, the use of the NREL 5 MW reference wind turbine rotor results in the load distribution of the inboard section of this developed model to be comparable to that of the full-model CFD; however, a similar observation is not made for the conventional BEM. Full article

The development of anisotropic gold nanostructures supporting localized surface plasmon (LSP) resonances in the near-infrared (NIR) biological window is of great interest for diagnostic and therapeutic nanotechnologies. Here, we report gold open-shell nanoparticles (AuOSNs), a symmetry-broken nanoshell architecture exhibiting strong NIR surface-enhanced Raman scattering (SERS) activity. AuOSNs were fabricated via a surfactant-free strategy combining bottom-up silica sphere assembly with a simple top-down gold deposition process, without using highly cytotoxic surfactants such as cetyltrimethylammonium bromide (CTAB). Boundary element method (BEM) simulations revealed that the asymmetric open-shell geometry induces NIR LSP resonances with pronounced electromagnetic field localization near the opening edges, depending on excitation configuration. Consistent with these predictions, extinction spectra of AuOSNs dispersed in water showed an LSP resonance peak at ~793 nm, close to the 785 nm excitation wavelength for SERS. In aqueous dispersion, AuOSNs modified with 4-mercaptobenzoic acid (4-MBA) exhibited strong SERS activity with enhancement factors of ~10⁶. Furthermore, polyethylene glycol (PEG)-modified MBA/AuOSNs showed negligible cytotoxicity in vitro. SERS imaging confirmed that PEG/MBA/AuOSNs enable visualization of HeLa cells via characteristic MBA SERS signals. These results demonstrate that surfactant-free AuOSNs provide a biocompatible platform for NIR-excited SERS sensing and cellular imaging, highlighting their potential in plasmonic bioimaging applications. Full article

The expansion of urban rail transit has exacerbated environmental issues related to low-frequency noise (LFN), yet the impact of geometric symmetry breaking on structure-borne noise remains underexplored. This study aims to quantify the mechanism by which cross-sectional asymmetry influences the vibro-acoustic coupling of viaducts. A 2.5D Hybrid Finite Element-Boundary Element Method (FEM-BEM) was employed to model a parametric box girder under eccentric track loading, and the numerical framework was validated against analytical benchmarks. The “Modal Symmetry Index” (MSI) and “Acoustic Asymmetry Indicator” (AAI) were defined to evaluate the effects of the asymmetry parameter ($\alpha$) on sound field distribution. Numerical results reveal a nonlinear “V-shaped” relationship between geometric asymmetry and acoustic directivity. While severe asymmetry ($\alpha >0.15$) exacerbates noise deflection via flexural–torsional coupling, a critical “self-balance zone” exists. Specifically, moderate asymmetry ($\alpha \approx 0.07$) effectively neutralizes load eccentricity, reducing the AAI from 1.5 dB (in strictly symmetric designs) to nearly 0 dB. Robustness analysis under right-side loading conditions further confirms a “reverse deflection” phenomenon, verifying that the proposed self-balance design minimizes directional sensitivity. These findings challenge the traditional assumption that geometric symmetry is acoustically optimal. A “competition–compensation” mechanism is identified, suggesting that deliberate, slight geometric asymmetry can serve as an effective passive noise control strategy for viaducts. Full article

Accurate and rapid prediction of sensor responses is critical for real-time measurement systems, digital twin implementations, and sensor design optimization. Traditional numerical methods such as Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) provide high-fidelity solutions but suffer from prohibitive computational costs, limiting their applicability in time-sensitive applications. This paper presents a novel framework utilizing Fourier Neural Operators (FNO) as surrogate models for fast multi-physics sensor response prediction across thermal, acoustic, and flow measurement domains. Unlike conventional neural networks that learn finite-dimensional mappings, FNO learns operators between infinite-dimensional function spaces by parameterizing the integral kernel in Fourier space, enabling resolution-invariant predictions with remarkable computational efficiency. We demonstrate the framework’s efficacy through three comprehensive case studies: (1) thermal sensor response prediction achieving $R^2>0.98$ with 8300× speedup over FEM, (2) acoustic sensor array modeling with mean absolute error below 0.5 dB and 4000× speedup over BEM, and (3) flow sensor characterization with velocity field prediction accuracy exceeding 97% and 31,000× speedup over CFD. The proposed FNO-based surrogate models are trained on simulation datasets generated from high-fidelity numerical solvers and validated against simulation holdout data for all three case studies, with additional experimental validation conducted for the thermal sensor case. Results indicate that FNO architectures effectively capture the underlying physics governing sensor behavior while reducing inference time from minutes to milliseconds. The framework enables real-time sensor calibration, uncertainty quantification, and design optimization, opening new possibilities for intelligent measurement systems and Industry 4.0 applications. We also investigate the spectral characteristics of FNO predictions, addressing the inherent low-frequency bias through a hybrid architecture combining FNO with local convolutional layers. The primary contributions of this work include: (1) the first systematic application of FNO-based surrogate modeling specifically tailored for sensor response prediction across multiple physics domains, (2) a novel H-FNO architecture that combines spectral operators with local convolutions to mitigate spectral bias in sensor applications, and (3) comprehensive validation including both simulation and experimental data for practical deployment. This work establishes FNO as a powerful tool for accelerating sensor simulation and advancing the field of AI-enhanced instrumentation and measurement. Full article

Orthotropic materials are increasingly employed in advanced thermal systems due to their direction-dependent heat transfer characteristics. Accurate numerical modeling of heat conduction in such media remains challenging, particularly for 3D geometries with nonlinear boundary conditions and internal heat generation. In this study, conventional boundary element method (BEM) and isogeometric boundary element method (IGABEM) formulations are developed and compared for steady-state orthotropic heat conduction problems. A coordinate transformation is adopted to map the anisotropic governing equation onto an equivalent isotropic form, enabling the use of classical Laplace fundamental solutions. Volumetric heat generation is incorporated via the radial integration method (RIM), preserving the boundary-only discretization, while nonlinear Robin boundary conditions are treated using variable condensation and a Newton–Raphson iterative scheme. The performance of both methods is evaluated using a hollow ellipsoidal benchmark problem with available analytical solutions. The results demonstrate that IGABEM provides higher accuracy and smoother convergence than conventional BEM, particularly for higher-order discretizations, which is owing to its exact geometric representation and higher continuity. Although IGABEM involves additional computational overhead due to NURBS evaluations, both methods exhibit similar quadratic scaling with respect to the degrees of freedom. Full article

The horizontal-axis tidal turbine is a representative device for harnessing ocean tidal energy, and the structural optimization of its blades is crucial for enhancing the power capture efficiency. In this work, the twist and chord distributions of the blade are determined using an improved Blade Element Momentum (BEM) approach, in which tip and hub loss factors are employed to enhance the modeling accuracy, and these results are employed to construct a parametric model of the original rotor. Due to its simplified assumptions and inability to capture three-dimensional flow effects, computational fluid dynamics (CFD) simulations were carried out to evaluate the hydrodynamic performance and flow analysis of the designed rotor. Further, the orthogonal test method was used to optimize the hydraulic performance of the rotor. Three optimization parameters, namely hub diameter, airfoil type, and maximum airfoil thickness, were set with three levels. Based on the orthogonal design scheme, nine rotor configurations were generated, and their energy capture characteristics and flow fields were subsequently evaluated through numerical simulations. The analysis indicates that the choice of airfoil exerts the strongest impact on the rotor’s energy capture efficiency, while the influences of maximum airfoil thickness and hub diameter follow in descending order. Consequently, the optimized rotor adopts a NACA63-415 airfoil with a reduced maximum thickness of 0.9 T₀ and an intermediate hub diameter of 15%R, achieving a power coefficient of 0.445 at the design tip-speed ratio of 4, corresponding to a 3.08% improvement compared with the original design. Flow field analysis demonstrates that the optimized geometry promotes a more uniform spanwise pressure distribution and effectively suppresses flow separation, thereby enhancing the overall hydrodynamic efficiency. Full article

This paper presents the experimental validation of a computational kinematic model for a passive Stewart–Gough platform equipped with modified Cardan joints. The introduced joint geometry significantly increases structural stiffness but invalidates the standard spherical joint assumption commonly used in hexapod kinematic formulations. To address this, we develop an efficient numerical optimization-based framework that solves both the direct and inverse kinematics without relying on simplified joint models. Furthermore, to enable autonomous and absolute pose measurement, each cylindrical leg joint of the platform is equipped with a LinACE™ absolute linear encoder. This sensor integration transforms the passive mechanism into an IoT-enabled reconfigurable fixture capable of internally sensing, tracking, and recalling its own configuration. The direct kinematics are computed iteratively using a homogeneous transformation formulation and benchmarked against analytical models derived for ideal spherical joints. Experimental results demonstrate sub-millimeter accuracy in pose estimation, confirming the validity of the proposed kinematic model and highlighting the suitability of the sensor-equipped hexapod for industrial flexible fixturing applications. Full article

This paper outlines the design and testing of a horizontal-axis tidal turbine (HATT) at a scale of 1:20, employing numerical simulations and experimental validation. The design employed an in-house code based on the Blade Element Momentum (BEM) theory. As reliable lift and drag coefficients for this scale are not present in the literature due to the low Reynolds number of the airfoil, Computational Fluid Dynamics (CFD) simulations were conducted to generate accurate polar diagrams for the NACA 4412 airfoil. The turbine was then 3D-printed and the rotor tested in a subsonic wind tunnel at various fixed rotational speeds to determine the power coefficient. Fluid dynamic similarity was achieved by matching the Reynolds number and tip-speed ratio in air to their values in water. Three-dimensional CFD simulations were also performed, yielding turbine efficiency results that agreed fairly well with the experimental data. However, both the experimental and numerical simulation results indicated a higher power coefficient than that predicted by BEM theory. The CFD results revealed the presence of radial velocity components and vortex structures that could reduce flow separation. The BEM model does not capture these phenomena, which explains why the power coefficient detected by experiments and numerical simulations is larger than that predicted by the BEM theory. Full article

The Planting Oysters to Strengthen the Foundation (POSF) method, as a construction technique for coastal stone structures in the Northern Song Dynasty of China (1059), has been preserved to this day. Exploring its long-term reinforcement mechanism can provide theoretical support and practical guidance for the protection and sustainable development of world marine cultural heritage. This article uses Crustacean Ash Triad Clay (CATC) from Shihu Ancient Wharf in Quanzhou as a case study and conducts a systematic investigation using XRD, Raman, SEM-EDS, FTIR, and 16S rRNA high-throughput sequencing. The results show that CATC has a core skeleton of 94.6% quartz, with potassium feldspar, dolomite, and metal compounds as auxiliary components; that its 19.04% porosity provides enrichment space for positively charged ions and tide-borne microorganisms; that electrostatic adsorption between barnacle adhesive and the material achieves physical reinforcement; and that microbial metabolism promotes dolomite formation, producing chemical reinforcement. Thus, the ternary coupling of Biology–Environment–Materials forms a BEM long-term reinforcement mechanism suitable for low-carbon construction in the ocean. Full article