Search Results (252)

Large-eddy simulation (LES) of wind farms is often limited by the computational cost required to represent many turbine rows and to obtain statistically converged wake and power statistics. Here, we present LES of an extended wind-farm configuration using the PALM model system, where cyclic lateral boundary conditions are employed to emulate interior-farm interaction in an idealized neutral boundary layer. The setup consists of nine identical horizontal-axis wind turbines arranged in a staggered array within the computational domain. Time-averaged hub-height fields show coherent wake corridors with a mean inflow-speed reduction of $23.7$% (array-mean across turbines) relative to an undisturbed background wind speed, and peak wake deficits reaching $71.4$% in the near-wake region. Turbulence levels increase markedly in the wake shear layers, with hub-height turbulence intensity enhanced by $32.2$% in the rotor region compared to background conditions; correspondingly, the peak hub-height SGS-TKE increases by a factor of 6.74 relative to background. Vertically averaged profiles indicate a momentum deficit within the turbine layer and gradual recovery aloft; the streamwise turbulent momentum flux remains predominantly negative, demonstrating the downward transport of higher-momentum air from above as a key recovery mechanism. Turbine rotor-power statistics show an initial adjustment followed by a quasi-stationary regime, with a farm-mean rotor power of $1.93$ MW and persistent inter-turbine variability characterized by a mean coefficient of variation of $61.2$%. Overall, the results demonstrate that the proposed extended-farm LES approach enables computationally efficient quantification of wake dynamics, vertical momentum transport, and their impact on power variability under idealized neutral wind-farm conditions. Full article

Highway merging and platoon formation are critical scenarios in heavy-duty vehicle aerodynamics. This study presents a transient computational fluid dynamics (CFD) analysis of two trucks undergoing a merging maneuver and subsequent platoon formation. A three-dimensional unsteady Reynolds-Averaged Navier–Stokes (uRANS) approach with the SST k–ω turbulence model is employed under zero-crosswind and yawed inflow conditions. The present work provides a time-resolved characterization of truck–truck aerodynamic interactions during dynamic spacing evolution, enabling the capture of unsteady wake effects that are not accessible in steady-state formulations commonly used in cooperative driving studies. Unlike previous steady analyses, the approach resolves transient wake development, vortex shedding, and their direct impact on instantaneous aerodynamic loads. Results identify three interaction regimes: weak interaction, strong wake interaction during wake impingement, and wake recovery at larger spacing. Under zero-crosswind conditions, significant drag reduction is observed, confirming platooning benefits. However, crosswind conditions substantially reduce this benefit and increase lateral loads due to asymmetric pressure distribution and wake deflection. A non-linear spacing–drag relationship is observed, governed by wake evolution and shear-layer interaction. These findings provide quantitative insight into transient aerodynamic interactions and highlight the importance of accounting for unsteady and crosswind effects in platoon performance assessment. Full article

The aerodynamic behaviour of buildings equipped with porous outer envelopes is governed by the interaction between millimetre-scale geometric features and building-scale flow structures. Explicitly resolving these scales in numerical simulations is computationally prohibitive, making homogenised porous-medium formulations a practical alternative. Among them, the Darcy–Forchheimer (D–F) model is widely adopted; however, the reliability of building-scale predictions critically depends on how its resistance coefficients are identified and validated. This study proposes and assesses a consistent procedure for the determination and application of D–F coefficients for porous screens used in double-skin façade systems. Porous elements are first characterised at the element scale through an analytical derivation based on aerodynamic force coefficients, from fully resolved CFD simulations of representative periodic modules. The resulting D–F coefficients are cross-compared and validated against available wind tunnel data at local Reynolds numbers $R{e}_H>3000$. Secondly, the calibrated homogenised model is applied to a building-scale double-skin façade configuration. The porous layer is represented as a finite-thickness porous region governed by the identified D–F parameters and analysed through unsteady Reynolds-averaged Navier–Stokes simulations. The model’s capability to reproduce global aerodynamic loads, local pressure distributions, and wake characteristics is evaluated against experimental data. The results demonstrate that a properly calibrated D–F formulation provides an accurate and computationally efficient representation of porous façade systems, bridging element-scale characterisation and structural-scale aerodynamic performance. Full article

Accurate prediction of wake interactions in twin vertical-axis hydroturbine (VAHT) arrays is important for dense tidal-farm layout assessment but remains computationally expensive when based directly on Computational Fluid Dynamics (CFD) reference simulations. While simplified analytical models offer speed, they fail to capture the non-axisymmetric wake characteristics of VAHT arrays, and standard Physics-Informed Neural Networks (PINNs) often struggle with convergence in small-sample, high-dimensional flow settings. To address this challenge, this study proposes a Physics-Informed POD-PINN framework for predicting configuration-wise time-averaged wake fields. The hybrid architecture combines Proper Orthogonal Decomposition (POD) for dimensionality reduction with a dual-branch neural network: a global POD branch captures dominant flow structures, while a lightweight spatial correction branch acts as a continuity-informed regularization on the predicted field. Trained on CFD-generated reference data covering diverse longitudinal and lateral spacing configurations, the model learns to map geometric parameters to a three-component wake field represented on a regularized 3D grid. Results show that the proposed framework achieves the lowest mean streamwise error among the tested surrogate models while maintaining millisecond-level inference speed. This study provides an efficient and physics-aware surrogate tool for repeated wake-field evaluation in twin-hydroturbine configuration exploration. Full article

This paper introduces a policy-search-based reinforcement learning algorithm aimed at generating optimal set-points of wind turbines in wind farms. The proposed approach addresses the problem of multivariable optimization in systems where the objective function is unknown or difficult to model. The algorithm is a model-free framework and relies solely on measured performance of the system. Namely, it does not require gradient information of the objective function or an explicit model of the aerodynamic interaction between wind turbines. The proposed scheme utilizes stochastic policy perturbations to explore the search space and update the policy parameters directly based on the observed reward signal. In this way, the algorithm progressively drives the control variables toward optimal operating conditions. The proposed policy-search reinforcement learning framework is analyzed to establish its connection with gradient-free optimization methods. The proposed method is applied to wind farm power optimization, where multiple turbine control variables must be adjusted in the presence of wake interactions cooperatively. The performance of the proposed approach is evaluated through extensive simulations under both steady-state and time-varying wind conditions. The proposed algorithm is compared with an extremum-seeking control method that was previously suggested for the same problem. The results demonstrate that the proposed approach is able to effectively maximize power production in wind farms while maintaining a simple and model-free optimization structure. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

This study examines the electrohydrodynamic (EHD) behavior of air bubbles rising in deionized water under a non-uniform electric field, with particular emphasis on the influence of applied voltage (0.5–3.0 kV) and gas flow rates of 30 and 40 mL ${\min }^{-1}$ (corresponding to Reynolds numbers of $R{e}_g=107$–142) on bubble dynamics. High-speed imaging reveals bubbles with equivalent diameters in the range of $d_{eq}\approx 0.8$–3.5 mm, enabling a detailed characterization of their deformation, trajectory, and interfacial response under coupled hydrodynamic and electric stresses. At $R{e}_g=107$, bubbles exhibited stable vertical trajectories with negligible lateral displacement, whereas at $R{e}_g=142$, inertial and wake effects induced deviations. Increasing $B{o}_E$ reduced lateral displacement, restoring alignment with the electric field. Bubble rise velocities increased by ∼20–30% with applied voltage due to polarization-driven EHD forces. A transition from hydrodynamically dominated to EHD-dominated regimes was identified. While polarization forces govern the initial bubble motion under a strong electric field, bubbles progressively transition downstream to a hydrodynamic regime as the electric field weakens, reducing the influence of polarization effects. These findings provide quantitative insight into coupled hydrodynamic–electrohydrodynamic interactions and support the development of predictive models for controlling bubble trajectories, with implications for electrically tunable multiphase and microfluidic systems. Full article

For AUVs operating under large-rudder-angle yaw maneuvering conditions, linearized hydrodynamic-derivative models often fail to accurately capture strongly nonlinear flow effects, and the applicability of control parameters becomes limited. To address these issues, this paper proposes a CFD-in-the-loop control simulation and parameter optimization framework for large-rudder-angle yaw maneuvers. Based on a coupled hull–propeller–rudder solution method, an unsteady CFD motion simulation model is developed that simultaneously accounts for propeller wake, rudder inflow, and hull-flow interaction, thereby enabling a strongly coupled solution of flow-field evolution and the six-degree-of-freedom motion of the vehicle. On this basis, a CFD-in-the-loop closed-loop control simulation framework is established by integrating the controller, actuator dynamic model, virtual sensors, and CFD motion simulation module into a unified framework, thereby realizing closed-loop computation of control input, flow response, motion update, and state feedback. Furthermore, under the same controller structure and parameter settings, the large-rudder-angle yaw responses predicted by the linearized hydrodynamic-derivative model and the CFD-in-the-loop simulation framework are compared and analyzed. This comparison reveals the dependence of control parameters on the underlying dynamic model and highlights their limited applicability under strongly nonlinear operating conditions. Finally, to address the high computational cost of CFD-in-the-loop simulations, a surrogate-model-based control parameter optimization method is developed to improve parameter tuning efficiency and enhance closed-loop control performance. The results show that the proposed CFD-in-the-loop control simulation framework can effectively characterize the nonlinear hydrodynamic effects arising during large-rudder-angle maneuvers, and provides a more physically consistent basis for control parameter optimization, analysis, and design. Full article

The prediction of dynamic responses of offshore wind turbine foundations under wind-wave-current multi-field coupled loads is the cornerstone of safety in offshore wind power engineering. The currently widely adopted equivalent load application method, while computationally efficient, simplifies loads into concentrated forces applied at the pile top and tower top, neglecting fluid-structure dynamic interaction mechanisms, which leads to deviations in response predictions. To overcome this limitation, this paper proposes a high-precision bidirectional fluid-structure interaction numerical framework. The fluid domain employs computational fluid dynamics (CFD) to construct an air-seawater two-phase flow model, utilizing the standard k-ε turbulence model and nonlinear wave theory to accurately simulate complex marine environments. The solid domain establishes a wind turbine-stratified seabed system via the finite element method (FEM), describing soil-rock mechanical properties based on the Mohr-Coulomb constitutive model. Comparative studies indicate that the equivalent static method significantly underestimates the displacement response of pile foundations, particularly under the extreme shutdown conditions examined in this study. This value should be interpreted as a case-specific observation rather than a universal deviation, and the discrepancy may vary with sea state, wind speed, current velocity, and wind–wave misalignment, thereby leading to non-conservative estimates of stress distribution. In contrast, the fluid-structure interaction method can reveal key physical processes such as local flow acceleration and wake–interference effects around the tower and the parked rotor under shutdown conditions, and the nonlinear interaction and resistance-increasing mechanisms between waves and currents. This model provides a reliable tool for safety assessment and damage evolution analysis of wind turbine foundations under extreme marine conditions, promoting the transformation of offshore wind power structure design from empirical formulas to mechanism-driven approaches. Full article

Micro air vehicles (MAVs) operating at low Reynolds numbers face aerodynamic and structural challenges that differ significantly from those encountered by conventional aircrafts. Nature provides effective solutions to these constraints, as insects, birds, and bats demonstrate highly efficient flight through integrated interactions between morphology, kinematics, and unsteady aerodynamic mechanisms. This review examines how biological flight principles can inform the design of next-generation MAVs. The study first analyzes biological flight strategies across insects, birds, and bats, with emphasis on scaling laws and physiological adaptations relevant to small-scale flight. It then reviews key unsteady aerodynamic phenomena governing low-Reynolds-number flight, including leading-edge vortex stability, wing–wake interactions, tandem-wing effects, and ground influence, as well as current modeling approaches ranging from quasi-steady methods to high-fidelity Navier–Stokes simulations. Building on these principles, the paper discusses biomimetic design strategies for MAV wings, structural–aerodynamic coupling, and actuation technologies used to replicate flapping flight. Existing MAV demonstrators inspired by biological flyers are analyzed, including concepts relevant to planetary exploration environments. Finally, the review identifies current technological limitations and research gaps in materials, actuation, aerodynamic modeling, and system integration. By synthesizing insights from biology and engineering, this work highlights key directions for the development of efficient, adaptable biomimetic MAV platforms capable of operating in complex environments. Full article

The interaction between circular piers and turbulent open-channel flow generates complex three-dimensional structures, including horseshoe vortices at the pier base and wake vortices downstream. These structures increase vertical velocities, pressure fluctuations, and shear stresses, contributing to erosion and structural instability. Although these phenomena have been widely studied, limited attention has been given to surface geometric modifications as a flow-control strategy. This study employs Large Eddy Simulation (LES) to evaluate the influence of a hexagonal dimple pattern on circular piles in a free-surface channel. The dimples were defined by varying diameter, depth, and spacing to reduce vertical velocity and alter vortex formation. The computational domain represents a 0.40 m wide, 12 m long, and 1.2 m high rectangular channel, with an inlet mass flow of 9.4 kg/s and 0.10 m water depth. Model validation against particle image velocimetry (PIV) data showed 99% correlation, confirming numerical accuracy. Results demonstrate that textured surfaces modify flow dynamics by enhancing kinetic energy dissipation and generating micro-vortices that weaken dominant structures. The optimal configuration (6 mm diameter, 2 mm depth, 1 mm spacing) reduced downward vertical velocity by 42% and wake vortex shedding frequency by 24%, indicating improved hydraulic stability and erosion mitigation potential. Full article

To address the demand for a micro-power supply for vehicle suspension control, a novel harvester is proposed to recover vortex-induced vibration energy in the wake of a shock absorber. A suspension dynamic model was established to simulate the spring compression process and identify the wind-shielding condition. The spring-shock absorber assembly was then simplified as a stepped cylinder with two cross-sections. Flow-field analysis showed that the size, shape, and rising angle of the wake vortices were affected by the bluff-body geometry, Reynolds number, and boundary conditions. The downwash motion was found to directly influence vortex development, and two new vortex-connection modes were identified. These results provided guidance for harvester optimization. A two-way fluid–structure interaction model was developed to describe the electromechanical conversion behavior of the proposed harvester under flow excitation. Numerical results showed that the output voltage increased with vehicle speed. An average peak voltage of 1.82 V was obtained when the piezoelectric patches were installed two larger-cylinder diameters downstream. The optimal patch length was 120 mm, and further increasing the length did not significantly improve the harvesting performance. Finally, a full-scale prototype was tested, and the measured voltage agreed well with the simulation results. The proposed harvester can therefore serve as a potential micro-power source for low-power suspension electronics. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

Canine cognitive dysfunction (CCD) is a common age-related neurodegenerative disorder in dogs that shares several pathological and clinical features with human Alzheimer’s disease (AD). In both species, β-amyloid (Aβ) accumulates within the brain parenchyma and cerebral vessel walls and is associated with synaptic loss, oxidative stress, mitochondrial dysfunction, and chronic neuroinflammation, ultimately leading to progressive cognitive decline. Increasing evidence indicates that impairment of brain clearance mechanisms, particularly the glymphatic system, represents a central pathogenic mechanism in both CCD and AD. The glymphatic system is a glia-dependent perivascular network involved in the clearance of Aβ and other metabolic waste products from the brain. Its function declines with aging, vascular disease, and astrocytic alterations, including changes in aquaporin-4 distribution. Reduced glymphatic and periarterial drainage promotes the retention and aggregation of Aβ and tau proteins. Compared with AD, tau pathology in CCD is generally less extensive, supporting the interpretation of CCD as an Aβ-predominant condition and a partial pathological analog of Alzheimer’s disease. Clinically, CCD is characterized by a constellation of behavioral changes including, disorientation, altered social interactions, sleep–wake cycle disturbances, a loss of housetraining, changes in activity levels, and increased anxiety, commonly summarized by the DISHAA acronym. Overall, CCD represents a valuable spontaneous large-animal model for investigating neurodegenerative mechanisms and clearance-related therapeutic targets relevant to both veterinary and human medicine. Full article

This study compares the performance of two wind farm sites located in Northern Europe: an onshore site and an offshore area in the eastern Baltic Sea region. This study investigates the optimisation of wind farm performance within a fixed project area by maximising annual energy production (AEP) and increasing energy density. Three wake-loss scenarios (≤10%, ≤15%, and ≤20%) were examined to assess the sensitivity of layout optimisation to aerodynamic interaction constraints. Several layout configurations were analysed to reduce wake losses and enhance overall energy output. Wind conditions were assessed using NORA3 reanalysis data, and wake interactions were modelled using the Jensen wake model to estimate AEP. Both wind farms were further compared across key criteria, including cost, power generation efficiency, installation and maintenance requirements, and site availability. Offshore wind farms achieve 1.5–1.7 times higher energy density under similar spatial conditions. However, offshore levelised cost of energy (LCOE) remains roughly 25% higher due to higher capital and infrastructure costs, while onshore LCOE demonstrates better economic performance, driven by lower CAPEX and O&M expenses. The findings highlight the trade-offs between cost efficiency and wake-driven energy performance for onshore and offshore wind development in the eastern Baltic Sea region. Full article