Search Results (488)

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems. Full article

This study examined whether different body fat percentages (BF%) may influence performance, physiological responses, and fatigue in professional soccer players during a simulated soccer game protocol on a treadmill. Twenty professional male soccer players were categorized in higher (HBF%) and lower (LBF%) body fat percentage groups [HBF% > 11.5%; n = 11, BF% = 14.2 ± 2, LBM = 65.3 ± 8 kg, age = 22.7 ± 4 years, height = 177 ± 7 cm, weight = 76 ± 9 kg, $\dot{V}$O₂max = 60.1 ± 4.5]; [LBF% < 11.5%, n = 9; BF% = 8.1 ± 1, LBM = 65.9 ± 5 kg, age = 20.1 ± 3 years, height = 179 ± 4 cm, weight = 72 ± 5 kg, $\dot{V}$O₂max = 61.6 ± 4). Players underwent a simulated soccer game protocol on a treadmill. Cardiometabolic and hormonal responses, and fuel oxidation and performance, were evaluated. At baseline, apart from the BF% variable (p < 0.0001), the groups did not differ in any other physiological or physical characteristic (p > 0.05). There were no differences between the groups in any performance or biological parameters evaluated (p > 0.05), except for plasma glucose, which was higher in the HBF% group at rest and during the soccer game protocol (p < 0.05). In conclusion, the theory of a uniform ideal (~10 ± 2%) of BF% in elite soccer is not supported by the present study. This study suggests that when muscle mass and fitness levels of the soccer players are maintained at high levels during the competitive period, BF% represents a highly individualized characteristic rather than a uniform target across players. However, a higher BF% increased resting and exercising blood glucose concentrations, even in highly trained professional soccer players, without concomitant effects on metabolism or fuel oxidation during match play. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Performance variability in MgO-based cements stems partly from poorly characterized dissolution kinetics of commercial lightly burned magnesia (LBM). Existing studies focus on high-purity materials under acidic conditions, but LBM also dissolves in alkaline conditions, where Mg(OH)₂ precipitation prevents reliable sampling at high pH. We validated pH monitoring against ICP-AES for tracking initial LBM dissolution kinetics across pH 2.0–11.0 and temperatures 25–85 °C. Commercial LBM (32 m²/g, 7.5 wt% CaO) exhibited rates one to two orders of magnitude higher than synthetic magnesia (10⁻⁸ to 10⁻¹² mol/cm²·s). X-ray diffraction, electron microscopy with energy-dispersive spectroscopy, and BET analysis revealed enhanced reactivity from poor crystallinity, multiphase composition, and high surface area with textural porosity. Temperature effects peaked at 75 °C before declining due to Mg(OH)₂ passivation. The validated method provides practical guidance for MBC quality control and performance optimization. By providing a rapid, instrument-simple alternative to ICP-AES for reactivity assessment, it lowers the analytical barrier to systematic LBM quality control, supporting the transition of magnesia-based cements from laboratory materials to scalable low-carbon alternatives to Portland cement. Full article

The prediction of drying kinetics in hygroscopic biological materials remains challenging due to the strong coupling between internal moisture diffusion, evolving surface wettability, material deformation and thermolabile bioactive compounds degradation. In this context, periodic temperature variations are inherent to many industrial and solar drying systems, yet most experimental and modeling studies evaluate product quality under constant-temperature conditions. This work provides a demonstration that periodic drying can alter quality degradation pathways in ways that may not be captured by constant-temperature experiments. A coupled non-isothermal lattice Boltzmann method (LBM) model for heat and moisture transport was integrated with a Weibull kinetic formulation to describe the degradation of total carotenoids, total polyphenols, and antioxidant activity in carrot slices. Validation against experimental data across 50–70 °C demonstrates excellent agreement (R² > 0.96 for moisture ratio; quality retention within ±2% of the literature values). Seven drying scenarios were systematically evaluated: constant temperature (60 °C), fast and slow periodic oscillations, high-amplitude cycles, a mixed strategy combining constant initial drying with subsequent oscillations, and two intermittent ON/OFF profiles. Results reveal that while total polyphenol degradation within the present model is constrained to ~13.3% retention under the adopted kinetic parameters, carotenoid and antioxidant retention are highly sensitive to temperature history. The mixed strategy (60 °C for 2 h followed by 50–60 °C oscillations) achieves the highest quality retention (TC: 51.6%, AA: 34.4%) while requiring the lowest energy input (0.512 kJ), outperforming constant drying (TC: 48.8%, AA: 32.9%, 0.563 kJ). Conversely, high-amplitude intermittent drying (70/25 °C) accelerates carotenoid degradation (TC: 46.7%) despite shorter drying time (8.81 h), and low-amplitude intermittent cycling (65/55 °C) yields the poorest mean quality (31.4%) with the highest energy consumption (0.583 kJ). The framework reveals that oscillation frequency critically determines quality outcomes: slow cycles (8 h period) marginally improve retention, while fast cycles (2 h) offer no benefit over constant drying. These findings provide quantitative insights toward the design of drying strategies, demonstrating that optimal strategies must account for the coupling between temperature history and moisture-dependent vulnerability, with the mixed strategy emerging as the best-performing strategy among the tested scenarios. Full article

Selective laser melting (SLM) is a complex additive manufacturing process involving rapid laser–material interaction, steep thermal gradients, and phase change phenomena. In this work, a two-dimensional thermal model based on the lattice Boltzmann method (LBM) is developed to simulate the SLM process of AlSi10Mg and 316L stainless steel (316L SS) alloys. The model captures the laser–material interaction, layer-by-layer deposition, phase change behavior, and heat transfer mechanisms, including conduction and convection. Experimental observations of melt pool width and depth were also performed on the microstructures of the two SLM alloys in order to compare the results with the numerical predictions. For the AlSi10Mg alloy, good agreement is obtained, with relative errors of 19.13% in melt pool width and 7.58% in depth, accurately capturing melt pool penetration and remelting behavior. In contrast, moderate deviations are observed for 316L SS, indicating a higher sensitivity to thermophysical properties and suggesting that further model refinement is required. Overall, the results demonstrate the capability of the LBM framework as an efficient and robust tool for analyzing thermal behavior in SLM and for supporting process parameter optimization. Full article

The combined control method of dimples and Gurney flaps has proven effective in enhancing the power coefficient of Vertical Axis Wind Turbines (VAWTs). However, the adaptability of this combined control structure to different airfoil geometries remains unclear. This paper investigates the aerodynamic characteristics of the Toward-Outside Dimple-Gurney Flap (TO-DGF) on three typical airfoils: NACA0021, NACA0012, and S1046. A dynamic flow field prediction model was established using the Lattice Boltzmann Method (LBM) combined with Wall-Modeled Large Eddy Simulation (WMLES). The Taguchi experimental design was employed to analyze the sensitivity of aerodynamic performance to airfoil type, Gurney flap position, and Gurney flap height. The results indicate that the airfoil type is the most critical factor affecting the power coefficient $C_P$, contributing significantly to the performance variance. Specifically, the NACA0021 airfoil demonstrated optimal performance in suppressing dynamic stall. Furthermore, the optimal DGF position varies with the tip speed ratio (TSR): placing the structure at 0.05C and 0.15C from the trailing edge yields the best aerodynamic performance for low (TSR = 1.5) and medium (TSR = 2.4) TSRs, respectively. This study provides a valuable reference for the structural design of high-efficiency VAWT blades within the investigated TSR range. Full article

Dust pollution has emerged as a critical issue in a wide range of industrial applications, creating an urgent demand for effective strategies to mitigate particle deposition. Recent experimental studies have demonstrated that superhydrophobic coatings represent a promising class of self-cleaning materials, primarily attributed to their hierarchical rough structures and intrinsically low surface energy. Nevertheless, the underlying self-cleaning mechanisms of superhydrophobic surfaces have not yet been fully elucidated. This work examines particle deposition on superhydrophobic surfaces featuring stochastic roughness distributions through computational modeling. Surface topographies were generated using Fast Fourier Transform techniques. An integrated lattice Boltzmann–discrete element method (LBM–DEM) framework simulated particle transport in superhydrophobic-coated channels. Particle–fluid coupling was achieved via the immersed moving boundary approach, while particle–surface interactions employed a modified Johnson–Kendall–Roberts (JKR) adhesion model. Parametric studies quantified effects of particle size, interfacial energy, flow Reynolds number, and topographical statistics on deposition dynamics. Experimental validation demonstrates good agreement between numerical predictions and measurements. Smaller particles exhibit a lower tendency to deposit on superhydrophobic surfaces, whereas increasing surface energy significantly enhances particle deposition due to stronger adhesion forces and the suppression of particle resuspension. In addition, higher Reynolds numbers effectively reduce particle deposition. The revealed self-cleaning mechanisms provide theoretical guidance for the design of high-performance self-cleaning coatings, and the identified effects of particle and surface parameters offer practical insights for anti-pollution engineering applications. Full article

Particle deposition, rebound, and adhesion on rough surfaces play a crucial role in a wide range of powder handling, aerosol transport, and fouling-related processes. However, the underlying mechanisms governing single-particle interactions with rough surfaces, particularly those with complex surface morphologies, remain insufficiently understood. In this work, the deposition and elastic rebound behavior of an individual particle impacting superhydrophobic surfaces with ribbed and randomly distributed roughness structures are systematically investigated through a combined experimental and numerical approach. A coupled Lattice Boltzmann Method (LBM) and Discrete Particle Model (DPM) was developed, in which a new particle–surface contact model is proposed to account for adhesion, elastic deformation, and localized roughness effects through multi-node interactions. Randomly distributed rough surfaces are reconstructed using a Fast Fourier Transform (FFT)-based method, and single-particle impact experiments are conducted to validate the numerical predictions. Good agreement is achieved between simulated and measured values, with a relative error for the maximum rebound height of only 5.9% and a peak velocity deviation prior to impact of approximately 5.4%. Parametric analyses demonstrate that particle diameter, Young’s modulus, surface energy, surface roughness morphology, and flow Reynolds number all influence particle deposition outcomes. Larger particles exhibit significantly higher rebound heights due to increased stored elastic energy; specifically, when particle size increases from 20 μm to 100 μm, the maximum rebound height increases by a factor of 2.1. In contrast, smaller particles are more prone to adhesion after repeated impacts. The rebound height of particles decreases as surface energy increases. When surface energy rises from 0.01 J/m² to 0.05 J/m², rebound height drops from 53.65% to 38.66%. At 0.5 J/m², particles adhere immediately. Compared with ribbed surfaces, randomly distributed rough surfaces promote particle rebound by reducing effective contact area and inducing complex impact orientations. Particle rebound behavior is primarily governed by particle diameter, while material properties such as Young’s modulus and surface energy exhibit secondary and nonlinear effects. The proposed model provides a validated and transferable framework for analyzing particle–surface interactions on rough surfaces and offers physical insights relevant to the control of particle deposition in powder and particulate systems. Full article

Cardiovascular disease has become the leading cause of death worldwide, underscoring the urgent need for widespread cardiac monitoring, while the Electrocardiogram (ECG) remains the diagnostic gold standard, the complexity of its acquisition limits its long-term feasibility. In contrast, Photoplethysmography (PPG), ubiquitous in wearable devices, is increasingly adopted due to its accessibility. However, synthesizing ECG from PPG poses an intrinsically ill-posed inverse problem. Existing purely data-driven paradigms often neglect underlying biophysical mechanisms, resulting in a lack of physical constraints and interpretability, which renders them prone to generating non-physiological hallucinations. To address this, we propose PhysDiff-LBM, a novel physics-aware framework that incorporates Lattice Boltzmann hemodynamic constraints into a conditional diffusion model. Employing a dual-stream architecture, our framework captures high-frequency morphological details via a cross-attention-guided diffusion model with region-wise adaptability. Synergistically, we physically regularize the ECG synthesis by leveraging the mesoscopic streaming and collision operators of LBM. By forcing the synthesized waveform gradients to evolve consistently with hemodynamic momentum, this mechanism constrains the model to strictly adhere to the fluid dynamic conservation laws governing pulse wave propagation. Experimental results demonstrate that our method achieves superior signal fidelity and exhibits significant advantages in downstream clinical applications. Full article

Objectives: Hormonal changes during the postmenopausal period of life predispose women to changes in fat tissue distribution and the risk of insulin resistance, and may lead to deterioration of bone metabolism. Physical activity plays a significant role in improving metabolic health and may inhibit bone mass decrease. The purpose of this study was to analyze the relationships between bone health, body composition, maximal oxygen uptake (VO₂max), and carbohydrate metabolic indices in non-diabetic postmenopausal women. Methods: Fifty-seven postmenopausal women were included in the study (64.9 ± 4.8 years). The areal bone mineral density (aBMD) of femoral neck and L1–L4, femur strength index (FSI), total fat (FM), lean body mass (LBM), VO₂max, serum insulin, and glucose concentrations were determined. The insulin resistance index (HOMA-IR) was also calculated. The main statistical analyses were performed using hierarchical multiple linear regression models. Results: Body mass index (BMI), FM and LBM positively correlated with aBMD results (p ≤ 0.01) and FM negatively with FSI levels (p < 0.05). VO₂max showed a positive association with FSI and this relationship was confirmed in hierarchical multiple regression analysis (p < 0.05). Regression analysis revealed that the base model including age and BMI explained the variance in the femoral neck aBMD (p ≤ 0.01) and L1–L4 aBMD (p ≤ 0.01), respectively. In the case of the femoral neck aBMD model, adjustment for VO₂max increased the explained variance. Alternative models with carbohydrate metabolic indicators did not increase the explained variance. Conclusion: Our results suggest that aerobic capacity may be related to the level of femur bone strength. Somatic characteristics and carbohydrate metabolic status appear to play a role in the correlations between femur bone health and VO₂max. Full article

Spontaneous combustion of coal piles threatens the production and transportation safety of coal mining, which is attracting more and more attention. To understand the underlying physics, conducting pore-scale research on the spontaneous combustion of coal piles is critical. To enable pore-scale research, a pore-scale model of the spontaneous combustion of a coal pile is described, and governing equations are introduced. To understand the competition between airflow, heat–mass transfer, and oxidation reaction, the lattice Boltzmann method (LBM) is utilized, which offers distinct advantages in handling complex pore geometries, multi-physics coupling, and reactive transport at the pore scale. The present model integrates, for the first time in a pore-scale LB framework, airflow driven by thermal buoyancy, convective heat and mass transfer, and Arrhenius-type oxidation kinetics within a realistic coal pile geometry. After the numerical method is validated, the effects of inflowing air velocity, inflowing air temperature, oxygen concentration, and coal particle size are discussed. With an increase in inflowing air velocity, convective heat transfer is enhanced, and the coal pile maximum temperature decreases monotonically. According to the Arrhenius equation, with an increase in the inflowing air temperature and oxygen concentration, the oxidation reaction is accelerated, and the coal pile maximum temperature increases. When the size of the coal particle increases, the oxidation reactive area decreases, and the coal pile maximum temperature decreases, while the steady temperature is not affected. Full article

Body composition, bone mineral density, and cardiopulmonary exercise testing (CPET) are commonly used to assess aerobic fitness in athletes, but their interrelationships remain unclear. This study compared these parameters by sex and examined their associations in elite athletes. Our study included 145 youth water polo players (age: 15.7 ± 1.6 years; male: 75). Body composition was measured by DEXA, and treadmill CPET was performed using a sport-specific protocol. We analysed the correlations between the following factors by multivariate linear regression: lean body mass (LBM, LBM_index); body fat mass (BFM); percent body fat (PBF); bone mineral content (BMC); lumbar, femoral, and radial bone mineral density (LBMD, FNBMD, FTBMD, RBMD); exercise time; absolute and relative maximal oxygen uptake (VO_2absmax, VO_2relmax); maximal ventilation (VE_max). Exercise time was found to be negatively correlated with BFM, while VO_2relmax was found to be negatively correlated with BFM and PBF. VO_2absmax was found to be positively correlated with BFM, LBM, BMC, FNBMD, and RBMD. VE_max was found to be positively correlated with LBM and LBM_index. In males, VO_2absmax and VE_max were found to be positively correlated with LBMD and FTBMD. Correlations between bone density and CPET proved to be stronger in males. Our results indicate that body composition and bone density parameters influence CPET parameters, and their complex evaluation can support personalized diagnostics and athletes’ health. Full article

Inorganic soil stabilizer represents a promising option to tackle the issues that arise from the land-filling of engineering waste spoil (EWS) due to its economic and environmental benefits. This paper investigates the mechanical and microscopic properties of magnesium oxysulfate soil stabilizer (MOSS) pastes. In addition, the mechanical properties of a novel type of engineering waste spoil-based specimens (EWSS), fabricated using MOSS to solidify high-content EWS, were systematically investigated. The results show that a larger mean particle size and BJH adsorption average pore diameter of LBM were found to accelerate the heat flow rate of the hydration reaction. Specifically, the peak heat flow and total hydration heat attained 30.25 mW/g and 283.9 J/g, respectively, under the condition of an MgO-to-MgSO₄ molar ratio of 9: 1 with LBM2 as the raw material. Furthermore, when the weight ratio of EWS rose from 5% to 80%, the compressive and flexural strengths of EWSS pastes at the 28-day curing age were reduced by 47.0% and 53.3% relative to the reference MOSS pastes. Full article

With the electrification of automotive powertrains, aerodynamic noise has emerged as the primary factor affecting vehicle comfort. This systematic review, adhering to PRISMA 2020 guidelines, bridges the gap between biological fluid mechanics and automotive engineering by synthesizing recent advances in aerodynamic mechanisms and bionic control strategies. Based on a comprehensive search of Web of Science, ScienceDirect, SAE Mobilus, and Google Scholar for the literature published between 2016 and 2025, 90 eligible studies were analyzed to provide a rigorous evidence-based synthesis. The review details complex flow phenomena, such as turbulent separation and vortex shedding across key regions like A-pillars and mirrors, drawing parallels to bio-inspired fluid–structure interactions. Numerical prediction methods, including large eddy simulation (LES), detached eddy simulation (DES), and lattice boltzmann method (LBM), are critically examined for their efficacy in resolving both conventional and bionic flow structures. A significant focus is placed on bio-inspired mitigation technologies, where quantitative findings demonstrate substantial noise suppression: specifically, the reviewed data shows that bionic riblet surfaces on tires can reduce noise levels by up to 5.18 dB, while beetle-head-inspired protuberances on exterior mirrors can achieve reductions of up to 10 dB. Finally, this work suggests future research directions in integrated fluid–acoustic–structural simulation frameworks and self-adaptive bionic systems, providing a robust reference for developing high-performance, low-noise vehicles inspired by natural organisms. Full article