Search Results (179)

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability. Full article

Quantum spin transport in integrable systems reveals a rich nonequilibrium phenomena that challenges the conventional hydrodynamic framework. Recent advances in ultracold atom experiments with state preparation and single-site addressing have enabled the understanding of this anomalous behavior. Particularly, the full universality characterization of exotic initial states, as well as their measurement representation, remain unknown. By employing tensor network and contrast methods, we systematically investigate spin transport in the quantum XXZ spin chain and extract dynamical scaling exponents emerging from two paradigmatic and experimentally attainable initial states, i.e., multi-periodic domain-wall (MPDW) and spin-helix (SH) states. Our results using different values of anisotropic parameters $\Delta \in [0,1.2]$ demonstrate the evident impeded transport and the difference between the two states with increasing $\Delta$ values. Large-scale and consistent simulations confirm the contrast method as a viable scaling extraction approach for exotic states with periodicity within experimentally accessible timescales. Our work establishes a foundation for studying initial memory and the corresponding relations of emergent transport behavior in nonequilibrium quantum systems, opening avenues for the identification of their unique universality classes. Full article

The random thermal switching of domain walls (DWs) in perpendicularly magnetized anisotropy nanowires (PMA) poses a significant challenge for the reliability of spintronic storage devices. In this work, we study the thermal nucleation and dynamics of DWs in PMA nanowires using micromagnetic simulations. The focus is on the effect of device temperature, with attention to uniaxial anisotropy energy (K_u), saturation magnetization (M_s), and nanowire geometry. The results show that larger K_u or M_s reduces DW thermal switching, thereby enhancing DW thermal stability and increasing the DW nucleation temperature (T_n). A wider or thicker nanowire also lowers the probability of thermally induced DW creation, further improving stability. In addition, DW velocity rises with temperature, showing a thermally assisted motion. These results provide useful guidance for designing PMA-based memory devices with improved resistance to thermal fluctuations. Full article

Quantum walks are, at present, an active field of study in mathematics, with important applications in quantum information and statistical physics. In this paper, we determine the influence of basic chaotic features on the walker behavior. For this purpose, we consider an extremely simple model consisting of alternating one-dimensional walks along the two spatial coordinates in bidimensional closed domains (hard wall billiards). The chaotic or regular behavior induced by the boundary shape in the deterministic classical motion translates into chaotic signatures for the quantized problem, resulting in sharp differences in the spectral statistics and morphology of the eigenfunctions of the quantum walker. Indeed, we found, for the Bunimovich stadium—a chaotic billiard—level statistics described by a Brody distribution with parameter $\delta \simeq 0.1$. This indicates a weak level repulsion, and also enhanced eigenfunction localization, with an average participation ratio $(\mathrm{PR})\simeq 1150$ compared to the rectangular billiard (regular) case, where the average $\mathrm{PR}\simeq 1500$. Furthermore, scarring on unstable periodic orbits is observed. The fact that our simple model exhibits such key signatures of quantum chaos, e.g., non-Poissonian level statistics and scarring, that are sensitive to the underlying classical dynamics in the free particle billiard system is utterly surprising, especially when taking into account that quantum walks are diffusive models, which are not direct quantizations of a Hamiltonian. Full article

This study develops an incompressible two-phase flow solver based on the open-source OpenFOAM platform, employing the volume-of-fluid (VOF) method to track the gas–liquid interface and utilizing the MULES algorithm to suppress numerical diffusion. This study provides a comprehensive investigation of the spreading dynamics of droplet pairs near walls, along with the presentation of a corresponding mathematical model. The numerical model is validated through a two-dimensional axisymmetric computational domain, demonstrating grid independence and confirming its reliability by comparing simulation results with experimental data in predicting drConfirmedoplet collision, spreading, and deformation dynamics. The study particularly investigates the influence of surface wettability on droplet impact dynamics, revealing that increased contact angle enhances droplet retraction height, leading to complete rebound on superhydrophobic surfaces. Finally, a mathematical model is presented to describe the relationship between spreading length, contact angle, and Weber number, and the study proves its accuracy. Analysis under logarithmic coordinates reveals that the contact angle exerts a significant influence on spreading length, while a constant contact angle condition yields a slight monotonic increase in spreading length with the Weber number. These findings provide an effective numerical and mathematical tool for analyzing the spreading dynamics of droplet pairs. Full article

Nanotechnology has become a transformative field in modern science and engineering, offering innovative approaches to enhance conventional thermal and fluid systems. Heat and mass transfer phenomena, particularly fluid motion across various geometries, play a crucial role in industrial and engineering processes. The inclusion of nanoparticles in base fluids significantly improves thermal conductivity and enables advanced phase-change technologies. The current work examines Powell–Eyring nanofluid’s heat transmission properties on a stretched Riga plate, considering the effects of magnetic fields, porosity, Darcy–Forchheimer flow, thermal radiation, and activation energy. Using the proper similarity transformations, the pertinent governing boundary-layer equations are converted into a set of ordinary differential equations (ODEs), which are then solved using the boundary value problem fourth-order collocation (BVP4C) technique in the MATLAB program. Tables and graphs are used to display the outcomes. Due to their significance in the industrial domain, the Nusselt number and skin friction are also evaluated. The velocity of the nanofluid is shown to decline with a boost in the Hartmann number, porosity, and Darcy–Forchheimer parameter values. Moreover, its energy curves are increased by boosting the values of thermal radiation and the Biot number. A stronger Hartmann number M decelerates the flow (thickening the momentum boundary layer), whereas increasing the Riga forcing parameter Q can locally enhance the near-wall velocity due to wall-parallel Lorentz forcing. Visual comparisons and numerical simulations are used to validate the results, confirming the durability and reliability of the suggested approach. By using a systematic design technique that includes training, testing, and validation, the fluid dynamics problem is solved. The model’s performance and generalization across many circumstances are assessed. In this work, an artificial neural network (ANN) architecture comprising two hidden layers is employed. The model is trained with the Levenberg–Marquardt scheme on reliable numerical datasets, enabling enhanced prediction capability and computational efficiency. The ANN demonstrates exceptional accuracy, with regression coefficients $R\approx 1.0$ and the best validation mean squared errors of $8.52\times {10}^{-10}$, $7.91\times {10}^{-9}$, and $1.59\times {10}^{-8}$ for the Powell–Eyring, heat radiation, and thermophoresis models, respectively. The ANN-predicted velocity, temperature, and concentration profiles show good agreement with numerical findings, with only minor differences in insignificant areas, establishing the ANN as a credible surrogate for quick parametric assessment and refinement in magnetohydrodynamic (MHD) nanofluid heat transfer systems. Full article

Over the last decades, Computational Fluid Dynamics (CFD) has become a fundamental tool for the design of gas turbine combustors, partly making up for the costs and duration issues related to the experimental tests involving high-pressure reactive processes. Nevertheless, high-fidelity simulations of reactive flows remain computationally expensive, particularly for conjugate heat transfer (CHT) analyses aimed at predicting liner metal temperatures and characterising wall heat losses. This work investigates the robustness of a cost-effective numerical setup for CHT simulations, focusing on the prediction of cold-side thermal loads in industrial combustor liners under realistic operating conditions. The proposed approach is tested using both Reynolds-Averaged Navier–Stokes (RANS) and unsteady Stress-Blended Eddy Simulation (SBES) turbulence models for the combustor flame tube, coupled via a time desynchronisation strategy with transient heat conduction in the solid domain. Cold-side heat transfer is modelled using a 1D correlation-based tool, runtime coupled with the CHT simulation to account for cooling-induced thermal loads without explicitly resolving complex cooling passages. The methodology is applied to a single periodic sector of the NovaLT^TM16 annular combustor, developed by Baker Hughes and operating under high-pressure conditions with natural gas. Validation against experimental data demonstrates the methodology’s ability to predict liner metal temperatures accurately, account for modifications in cooling geometries, and support design-phase evaluations efficiently. Overall, the proposed approach offers a robust trade-off between computational cost and predictive accuracy, making it suitable for practical engineering applications. Full article

This study develops a coupled Smoothed Particle Hydrodynamics (SPH) and the Discrete Element Method (DEM) framework to explore the sedimentation behavior of densely arranged particles in vertical pipes. An unresolved SPH-DEM model is proposed, which integrates porosity-dependent fluid governing equations through local averaging techniques to connect pore-scale interactions with macroscopic flow characteristics. Validated against single-particle settling experiments, the model accurately captures transient acceleration, drag equilibrium, and rebound dynamics. Systematic simulations reveal that particle number, arrangement patterns, and fluid domain geometry play critical roles in regulating collective settling: Increasing particle count induces nonlinear terminal velocity reduction. Systems of 16 particles show 50% lower velocity than single-particle cases due to enhanced shielding and energy dissipation. Particle configuration (compact layouts 4 × 8 vs. elongated arrangements 8 × 4) dictates hydrodynamic resistance, compact layouts facilitate faster settling by reducing cross-sectional blockage, while elongated arrangements amplify lateral resistance. The width of the fluid domain exerts threshold effects: narrow boundaries (0.03 m) intensify wall-induced drag and suppress vortices, whereas wider domains promote symmetric vortices that enhance stability. Additionally, critical transitions in multi-row/column systems are identified, where stress-chain redistribution and fluid-permeation thresholds govern particle detachment and velocity stratification. These findings deepen the understanding of granular–fluid interactions in confined spaces and provide a predictive tool for optimizing particle management in industrial processes such as wellbore cleaning and hydraulic fracturing. Full article

Endolysins, phage-derived enzymes capable of lysing bacterial cell walls, hold significant promise as novel antimicrobials against resistant Gram-positive and Gram-negative pathogens. In this study, we undertook an integrative approach combining extensive in silico analyses and experimental validation to characterize the novel endolysin LysPALS22. Initially, sixteen endolysin sequences were selected based on documented lytic activity and enzymatic diversity, and subjected to multiple sequence alignment and phylogenetic analysis, which revealed highly conserved catalytic and binding domains, particularly localized to the N-terminal region, underscoring their functional importance. Building upon these sequence insights, we generated three-dimensional structural models using Swiss-Model, EBI-EMBL, and AlphaFold Colab, where comparative evaluation via Ramachandran plots and ERRAT scores identified the Swiss-Model prediction as the highest quality structure, featuring over 90% residues in favored conformations and superior atomic interaction profiles. Leveraging this validated model, molecular docking studies were conducted in PyRx with AutoDock Vina, performing blind docking of key peptidoglycan-derived ligands such as N-Acetylmuramic Acid-L-Alanine, which exhibited the strongest binding affinity (−7.3 kcal/mol), with stable hydrogen bonding to catalytic residues ASP46 and TYR61, indicating precise substrate recognition. Visualization of docking poses using Discovery Studio further confirmed critical hydrophobic and polar interactions stabilizing ligand binding. Subsequent molecular dynamics simulations validated the stability of the LysPALS22–NAM-LA complex, showing minimal structural fluctuations, persistent hydrogen bonding, and favorable interaction energies throughout the 100 ns trajectory. Parallel to computational analyses, LysPALS22 was heterologously expressed in Escherichia coli (E. coli) and Pichia pastoris (P. pastoris), where SDS-PAGE and bicinchoninic acid assays validated successful protein production; notably, the P. pastoris-expressed enzyme displayed an increased molecular weight (~45 kDa) consistent with glycosylation, and achieved higher volumetric yields (1.56 ± 0.31 mg/mL) compared to E. coli (1.31 ± 0.16 mg/mL), reflecting advantages of yeast expression for large-scale production. Collectively, these findings provide a robust structural and functional foundation for LysPALS22, highlighting its conserved enzymatic features, specific ligand interactions, and successful recombinant expression, thereby setting the stage for future in vivo antimicrobial efficacy studies and rational engineering efforts aimed at combating multidrug-resistant Gram-negative infections. Full article

Flexible pressure sensors have shown promise applications in scenarios such as robotic tactile sensing due to their excellent sensitivity and linearity. However, the realization of flexible pressure sensors with both static and dynamic response capabilities still face significant challenges due to the properties of the sensing materials themselves. In this study, we propose a flexible pressure sensor that integrates piezoelectric and ionic capacitance mechanisms for full-domain response detection of dynamic and static forces: a “sandwich” sensing structure is constructed by printing a mixture of multi-walled carbon nanotubes (MWCNTs) onto the surface of the upper and lower electrodes, and sandwiching a polyvinylidene fluoride (PVDF) thin film between the electrodes. The device exhibits a sensitivity of 0.13 kPa⁻¹ in the pressure range of 0–150 kPa. The sensor has a rapid dynamic response (response time 19 ms/12 ms) with a sensitivity of 0.49 mV kPa⁻¹ based on the piezoelectric mechanism and a linearity of 0.9981 based on the ionic capacitance mechanism. The device maintains good response stability under the ball impact test, further validating its potential application in static/dynamic composite force monitoring scenarios. Full article

A methane-air premixed gas explosion is one of the most destructive disasters in the process of coal mining, and the dynamic coupling between the shock wave triggered by the explosion and the surrounding rock of the roadway can lead to the destabilization of the surrounding rock structure, the destruction of equipment, and casualties. The aim of this study is to systematically reveal the propagation characteristics of the blast wave, the spatial and temporal evolution of the wall load, and the damage mechanism of the surrounding rock by establishing a two-way fluid-solid coupling numerical model. Based on the Ansys Fluent fluid solver and Transient Structure module, a framework for the co-simulation of the fluid and solid domains has been constructed by adopting the standard $k-\epsilon$ turbulence model, finite-rate/eddy-dissipation (FR/ED) reaction model, and nonlinear finite-element theory, and by introducing a dynamic damage threshold criterion based on the Drucker–Prager and Mohr–Coulomb criteria. It is shown that methane concentration significantly affects the kinetic behavior of explosive shock wave propagation. Under chemical equivalence ratio conditions (9.5% methane), an ideal Chapman–Jouguet blast wave structure was formed, exhibiting the highest energy release efficiency. In contrast, lean ignition (7%) and rich ignition (12%) conditions resulted in lower efficiencies due to incomplete combustion or complex combustion patterns. In addition, the pressure time-history evolution of the tunnel enclosure wall after ignition triggering exhibits significant nonlinear dynamics, which can be divided into three phases: the initiation and turbulence development phase, the quasi-steady propagation phase, and the expansion and dissipation phase. Further analysis reveals that the closed end produces significant stress aggregation due to the interference of multiple reflected waves, while the open end increases the stress fluctuation due to turbulence effects. The spatial and temporal evolution of the strain field also follows a three-stage dynamic pattern: an initial strain-induced stage, a strain accumulation propagation stage, and a residual strain stabilization stage and the displacement is characterized by an initial phase of concentration followed by gradual expansion. This study not only deepens the understanding of methane-air premixed gas explosion and its interaction with the roadway’s surrounding rock, but also provides an important scientific basis and technical support for coal mine safety production. Full article

The interfacial Dzyaloshinskii–Moriya interaction (DMI) plays a pivotal role in stabilising and controlling the motion of chiral spin textures, such as Néel-type bubble domains, in ultrathin magnetic films—an essential feature for next-generation spintronic devices. In this work, we investigate domain wall (DW) dynamics in magnetron-sputtered Ta(3 nm)/Pt(3 nm)/Co(1 nm)/RuO₂(1 nm) [Ru(1 nm)]/Pt(3 nm) multilayers, benchmarking their behaviour against control stacks. Vibrating sample magnetometry (VSM) was employed to determine saturation magnetisation and perpendicular magnetic anisotropy (PMA), while polar magneto-optical Kerr effect (P-MOKE) measurements provided coercivity data. Kerr microscopy visualised the expansion of bubble-shaped domains under combined perpendicular and in-plane magnetic fields, enabling the extraction of effective DMI fields. Brillouin light scattering (BLS) spectroscopy quantified the asymmetric propagation of spin waves, and micromagnetic simulations corroborated the experimental findings. The Pt/Co/RuO₂ system exhibits a Dzyaloshinskii–Moriya interaction (DMI) constant of ≈1.08 mJ/m², slightly higher than the Pt/Co/Ru system (≈1.03 mJ/m²) and much higher than the Pt/Co control (≈0.23 mJ/m²). Correspondingly, domain walls in the RuO₂-capped films show pronounced velocity asymmetry under in-plane fields, whereas the symmetric Pt/Co/Pt shows negligible asymmetry. Despite lower depinning fields in the Ru-capped sample, its domain walls move faster than those in the RuO₂-capped sample, indicating reduced pinning. Our results demonstrate that integrating RuO₂ significantly alters interfacial spin–orbit interactions. Full article

Seepage erosion around sheet pile walls represents a critical failure mechanism in geotechnical engineering, yet the underlying mechanisms governing the onset of erosion remain poorly understood. This study presents a comprehensive multi-scale investigation employing a coupled computational fluid dynamics (CFD)-discrete element method (DEM) to elucidate the onset mechanisms of seepage erosion in gap-graded soils with varying the fines content under different hydraulic gradients. The results demonstrate that increasing the fines content enhances the overall erosion resistance, as evidenced by reduced particle mobilization and eroded mass ratio. Particle tracking analysis reveals that the fines content fundamentally influences the spatial distribution of the erosion. Specimens with low fines content exhibit distributed erosion throughout the domain, while specimens with higher fines content show concentrated erosion around the sheet pile wall and downstream regions. Micromechanical analysis of local contact fabric and contact forces indicates that this spatial heterogeneity stems from the mechanical coordination number and mechanical redundancy, characterized by the reduced magnitudes of these parameters for the region with lower erosion resistance. These findings establish that the fines content governs both global erosion resistance and spatial erosion patterns, providing essential insights for optimizing soil gradation design and advancing fundamental understanding of seepage erosion mechanisms. Full article

Continual reinforcement learning (CRL) agents face significant challenges when encountering distributional shifts. This paper formalizes these shifts into two key scenarios, namely virtual drift (domain switches), where object semantics change (e.g., walls becoming lava), and concept drift (task switches), where the environment’s structure is reconfigured (e.g., moving from object navigation to a door key puzzle). This paper demonstrates that while conventional convolutional neural networks (CNNs) struggle to preserve relational knowledge during these transitions, graph convolutional networks (GCNs) can inherently mitigate catastrophic forgetting by encoding object interactions through explicit topological reasoning. A unified framework is proposed that integrates GCN-based state representation learning with a proximal policy optimization (PPO) agent. The GCN’s message-passing mechanism preserves invariant relational structures, which diminishes performance degradation during abrupt domain switches. Experiments conducted in procedurally generated MiniGrid environments show that the method significantly reduces catastrophic forgetting in domain switch scenarios. While showing comparable mean performance in task switch scenarios, our method demonstrates substantially lower performance variance (Levene’s test, $p<1.0\times {10}^{-10}$), indicating superior learning stability compared to CNN-based methods. By bridging graph representation learning with robust policy optimization in CRL, this research advances the stability of decision-making in dynamic environments and establishes GCNs as a principled alternative to CNNs for applications requiring stable, continual learning. Full article

The presented research examines the performance characteristics of Brazed Plate Heat Exchangers through computational fluid dynamics (CFD), focusing on pressure drop calculations for single-phase flow within full channels of plates featuring dimpled corrugation. This work aims to bridge gaps in the literature, particularly regarding the underexplored behavior near the ports for the studied technology and establishing a framework for future conjugate heat transfer studies. A methodology for the domain generation was developed, integrating a preliminary forming simulation to reproduce the complex plate geometry. Comprehensive sensitivity analyses were conducted to evaluate the influence of different parameters and identify the optimal settings for obtaining reliable results. The findings indicate that the $k-\epsilon$ realizable turbulence model with enhanced wall treatment offers superior accuracy in predicting pressure drops, with errors within ±4.4%. Additionally, leveraging the information derived from CFD, a strategy to estimate contributions from different channel sections without a direct reliance on those simulations was developed, offering practical implications for plate design. Full article