Search Results (408)

Currently, research on the hydrodynamic characteristics of artificial reef deployment still faces challenges such as insufficient environmental coupling, but accurate simulation of the deployment process holds significant engineering importance for optimizing deployment efficiency and ensuring reef stability. This study employs the Smoothed Particle Hydrodynamics (SPH) method to establish a 3D numerical model, focusing on the influence of key parameters—inflow velocity and water entry angle—on the hydrodynamic characteristics of cubic artificial reef deployment. The results indicate that under flow velocities of 0.4–0.5 m/s, pressure fluctuations are relatively minor, with peak pressure gradients below 15 kPa/m, exhibiting a gradual trend, while particle concentration remains high, and drag gradually increases. At flow velocities of 0.6–0.8 m/s, the maximum pressure at the bottom reaches up to 35 kPa, with low-pressure areas at the tail dropping to −10 kPa; particle concentration decreases compared to conditions at 0.4–0.5 m/s; settling time extends from 8.4 s to 12 s, representing a 42% increase. Under different water entry angles, drag varies nonlinearly with the angle, reaching its maximum at 20° and its minimum at 25°, with a reduction of approximately 47% compared to the maximum. The anti-sliding safety factor and anti-overturning safety factor are used to assess the stability of the cubic reef placed on the seabed. Across different inflow velocities, the anti-sliding safety factor of the cubic artificial reef significantly exceeds 1.2, whereas the anti-overturning safety factor is below 1.2 at 0.4 m/s but exceeds 1.2 at velocities of 0.5 m/s and above, indicating that the reef maintains stability under the majority of these flow conditions. Our findings provide a scientific basis for the deployment process, site selection, and geometric design of cubic artificial reefs, offering valuable insights for the precise deployment and structural optimization of artificial reefs in marine ranching construction. Full article

The impact and solidification of multiple molten droplets on a cold substrate critically influence the quality and performance of thermally sprayed coatings. We present a Smoothed Particle Hydrodynamics (SPH) model that couples fluid-solid interaction, wetting, heat transfer and phase change to simulate multi-droplet impact and freezing. The model is validated against benchmark cases, including the Young–Laplace relation, wetting dynamics, single-droplet impact and the Stefan solidification problem, showing good agreement. Using the validated model, we investigate two droplets—either centrally or off-centrally—impacting on a cold surface. Simulations reveal two distinct solidification patterns: convex pattern (CVP), which results in a mountain-like splat morphology, and concave pattern (CCP), which leads to a valley-like shape. The criterion for the two patterns is explored with two dimensionless numbers, the Reynolds number $Re$ and the Stefan number $Ste$. When $Re\le 17.8$, droplets tend to solidify in CVP; at higher Reynolds numbers $Re\ge 18.8$, they tend to solidify in CCP. The transition between the two patterns is primarily governed by $Re$, with $Ste$ exerting a secondary influence. For example, when droplets have $Re=9.9$ and $Ste=5.9$, they tend to solidify in a convex pattern, whereas at $Re=19.8$ and $Ste=5.9$, they tend to solidify in a concave pattern. Also, the solidification state of the first droplet greatly influences the subsequent spreading and solidification of the second droplet. A parametric study on CCP cases with varying vertical and horizontal offsets shows that larger vertical offsets accelerate solidification and reduce the maximum spreading factor. For small vertical distances, the solidification time increases with horizontal offset by more than 29%; for large vertical distances the change is minor. These results clarify how droplet interactions govern coating morphology and thermal evolution during thermal spraying. Full article

This numerical study demonstrates the existence of a critical injection momentum threshold necessary for stable liquid film formation, highlighting that either excessive or insufficient momentum degrades cooling performance. This optimization is critical for maximizing cooling effectiveness from short injection holes in high-performance propulsion systems. By comparing Smoothed Particle Hydrodynamics (SPH) and Volume of Fluid (VOF) methods, we find that the SPH method predicts a thicker, more continuous coolant film due to its superior mass conservation during interface breakup. A key design insight emerges: cooling performance peaks at a distinct, critical coolant momentum. Insufficient momentum leads to poor coverage, while excess momentum causes film separation and is counter-productive. The identified configuration—defined by a precise combination of flow rate, pressure, and geometry—promotes immediate and stable film formation. The robustness of this finding is confirmed by the agreement between the two numerical methods on film thickness and the captured physical evolution of the film from a pronounced wave to a damped state. Full article

In multi-level overtopping wave energy converters (OWEC), the inlet slot governs overtopping losses and the distribution of inflow among reservoirs, making it a critical design feature for maximizing hydraulic efficiency. This study defines the relative slot width as $\lambda$ (=w/$L_{slop}$) and investigates its influence on the performance of an SSG-based multi-level OWEC using DualSPHysics, an open-source weakly compressible smoothed particle hydrodynamics (WCSPH) solver, in a two-dimensional recirculating numerical wave tank under regular-wave conditions. Hydraulic efficiency is evaluated as the ratio of the overtopping-stored potential-energy flux to the incident wave energy flux per unit width. The results show a nonlinear dependence of reservoir-level contributions on $\lambda$, and an intermediate $\lambda$ provides a balanced contribution across upper, middle, and lower reservoirs, yielding the maximum overall efficiency. To extend the analysis beyond a single design wave, a global-state performance map in the period–height space is constructed and combined with the target-sea spectral characteristics, indicating that the optimal geometry maintains relatively robust efficiency in the dominant spectral band while revealing efficiency limitations associated with insufficient overtopping at small waves and saturation at large waves. The proposed approach provides quantitative guidance for slot design and site-relevant performance screening of multi-level OWEC. Full article

Crossland Tankers is a leading manufacturer of bulk-load road tankers in Northern Ireland. These tankers transport up to forty thousand litres of liquid over long distances across diverse road conditions. Liquid sloshing within the tank has a significant impact on driveability and the tanker’s lifespan. This study introduces a novel Multiphysics model combining Smooth Particle Hydrodynamics (SPH) and Finite Element Analysis (FEA) to simulate fluid–structure interactions in a full-scale road tanker, validated with real-world road test data. The model reveals high-stress zones under braking and turning, with peak stresses at critical chassis locations, offering design insights for weight reduction and enhanced safety. Results demonstrate the approach’s effectiveness in optimising tanker design, reducing prototyping costs, and improving longevity, providing a valuable computational tool for industry applications. Full article

Background and Objectives: Implant-associated complications, including foreign-body responses and infection risk, remain major concerns in reconstructive and aesthetic breast surgery. Antimicrobial polymer coatings have been proposed as potential preventive strategies, but early-stage development requires simple and ethically refined in vivo models. This pilot study aimed to (i) establish a practical workflow for applying PLGA–vancomycin coatings onto PTFE substrates used as experimental analogs for smooth silicone implants, and (ii) develop a small-animal implantation protocol for short-term evaluation of surgical feasibility and local tissue tolerability. Materials and Methods: PLGA microparticles and PLGA–vancomycin microparticles were prepared using a double-emulsion solvent-evaporation method and applied onto PTFE discs. Particle size and polydispersity were assessed based on dynamic light scattering (DLS), and surface charge was measured via zeta potential. A bilateral subcutaneous implantation model was used in four Wistar rats, each receiving a PTFE disc coated with PLGA-only on one side and a disc coated with PLGA–vancomycin on the other. Animals were monitored for postoperative recovery, wound appearance, and general condition. After four weeks, implants and surrounding tissues were harvested for macroscopic and preliminary histological evaluation. Results: Both PLGA-only and PLGA–vancomycin microparticles showed submicron mean hydrodynamic diameters and moderately polydisperse distributions typical for double-emulsion formulations. All animals recovered normally, maintained stable body weight, and exhibited no macroscopic signs of adverse reactions. Preliminary histology showed early fibrous capsule formation with mild inflammatory infiltrate around both types of coated implants, without qualitative differences observed in this pilot setting. Conclusions: This preliminary study demonstrates the feasibility of applying PLGA-only and PLGA–vancomycin coatings onto PTFE implant analogs and establishes a reproducible, minimal-use rat model for short-term evaluation of local tissue tolerability. The protocol provides a practical foundation for future work on coating stability, drug-release kinetics, antibacterial activity, and long-term tissue responses on medical-grade silicone substrates. Full article

Traditional position-based fluid (PBF) methods often suffer from interpolation inaccuracies and limited computational efficiency due to their fixed smoothing length. To address these limitations, this paper proposes an adaptive smoothing length model and implements full-pipeline parallel acceleration on GPUs. By incorporating both local neighbor count and density variation, the model dynamically adjusts particle smoothing length. This adaptation effectively mitigates two issues: surface distortion due to insufficient interpolation in sparse regions, and performance degradation caused by computational redundancy in dense regions. To resolve neighbor search asymmetry introduced by dynamic smoothing lengths, we designed a symmetry handling technique based on maximum smoothing length and an efficient spatial hashing search algorithm. Experimental results across multiple scenarios (including dam break and droplet impact) demonstrate that our method maintains simulation stability comparable to the fixed smoothing length approach while improving computational efficiency and enhancing local particle distribution uniformity. The improved uniformity is evidenced by a significant reduction in the variance of neighbor particle counts. Visually, the method yields more natural results for dynamic details such as splashing and fragmentation, thereby ensuring the visual realism of the simulations. Full article

This study presents a comprehensive smoothed particle hydrodynamics (SPH) framework developed to investigate the thermomechanical response and failure behavior of cylindrical 18650-type lithium-ion battery cans under explosion conditions. The model captures the coupled evolution of gas pressure, temperature, and particle dynamics, as well as the resulting deformation and fracture of the metallic enclosure. Parametric analyses were conducted to evaluate the influence of the internal gas domain geometry, can wall thickness, and initial pressure on the structural response, along with the subsequent post-explosion interaction between the escaping gas and external protective coverage. The results demonstrate the strong dependence of failure initiation on gas confinement geometry and highlight the existence of transient thermodynamic asymmetries within the gas domain that govern the impulse transferred to the can wall. The proposed modeling approach provides a physically consistent means of reproducing the key stages of battery explosion—from internal pressurization to external gas impact—and offers valuable insights for designing safer and more resilient energy storage enclosures. Full article

Dam break problem-induced floods can trigger hazardous dike overtopping, demanding predictions that are fast, accurate, and interpretable. We pursue two objectives: (i) introducing a new alpha evolution (AE) optimization scheme to improve tree-model predictive accuracy, and (ii) developing a cluster-wise modeling strategy in which regimes are defined by wave characteristics. Using a dataset generated via physical model experiments and smoothed particle hydrodynamics (SPH) numerical simulations, we first group samples via hierarchical clustering (HC) on the Froude number $\left(\mathrm{Fr}\right)$, wave nonlinearity $\left(R\right)$, and relative distance to the dike $\left(D\right)$. We then benchmark CatBoost, XGBoost, and ExtraTrees within each cluster and select the best-performing CatBoost as the baseline, after which we train standard CatBoost and its AE-optimized variant. Under random train–test splits, AE-CatBoost achieves the strongest generalization for predicting relative run-up distance $H_m$ (testing dataset $R^2=0.9803$, $\mathrm{RMSE}=0.0599$), outperforming particle swarm optimization (PSO) and grid search (GS)-tuned CatBoost. We further perform TreeSHAP analyses on AE-CatBoost for global, local, and interaction attributions. SHAP analysis yields physics-consistent explanations: D dominates, followed by H and L, with a weaker positive effect of $\mathrm{Fr}$ and minimal influence of R; $H\times D$ is the strongest interaction pair. Overall, AE optimization combined with HC-based cluster-wise modeling produces accurate, interpretable overtopping predictions and provides a practical route toward field deployment. Full article

We present an intelligent, non-intrusive framework to enhance the performance of Symmetric Smoothed Particle Hydrodynamics (SSPH) for elliptic partial differential equations, focusing on the linear and nonlinear Poisson equations. Classical Smoothed Particle Hydrodynamics methods, while meshfree, suffer from discretization errors due to kernel truncation and irregular particle distributions. To address this, we employ a machine-learning-based residual correction, where a neural network learns the difference between the SSPH solution and a reference solution. The predicted residuals are added to the SSPH solution, yielding a corrected approximation with significantly reduced errors. The method preserves numerical stability and consistency while systematically reducing errors. Numerical results demonstrate that the proposed approach outperforms standard SSPH. Full article

With the growing requirements for multi-particle process simulation, improving computational accuracy, efficiency, and scalability has become a critical challenge. This study generally focused on comprehensive analyses of existing numerical methods for simulating particle–substrate interactions in gas–thermal spraying (including gas–dynamic spraying processes), covering both single-particle and multi-particle models to develop practical recommendations for the optimization of modern coating spraying processes. First of all, this paper systematically analyzes the key limitations of current approaches, including their inability to handle high deformations effectively or high computational complexity and their insufficient accuracy in dynamic scenarios. A comparative evaluation of four numerical methods (Lagrangian, Arbitrary Lagrangian–Eulerian (ALE), Coupled Eulerian–Lagrangian (CEL), and Smoothed Particle Hydrodynamics (SPH)) revealed their strengths and weaknesses in modeling of real gas–thermal spraying processes. Furthermore, this study identifies the limitations of the widely used Johnson–Cook (JC) constitutive model under extreme conditions. The authors considered the Zerilli–Armstrong (ZA), Mechanical Threshold Stress (MTS), and Preston–Tonks–Wallace (PTW) models as more realistic alternatives to the Jonson–Cook model. Finally, comparative analyses of theoretical and realistic deformation and defect-generation processes in gas–thermal coatings emphasize the critical need for fundamental changes in the simulation strategy for modern gas–thermal spraying processes. Full article

Smoothed particle hydrodynamics (SPHs) is a Lagrangian meshless method with distinct strengths in managing unstable and complex interface behaviors. This study develops an integrated multiphase SPH framework by merging multiple algorithms and techniques to enhance stability and accuracy. The multiphase model is validated by several benchmark examples, including square droplet deformation, single bubble rising, and two bubbles rising. The selection of numerical parameters for multiphase simulations is also discussed. The validated model is then applied to simulate oil–water–gas bubbly flows. Interface behaviors, such as coalescence, fragmentation, deformation, etc., are reproduced, which helps to take into account multiphysics interactions in industrial processes. The rising processes of many oil droplets for oil–water separation are first simulated, showing the advantages and stability of the SPH model in dealing with complex interface behaviors. To fully explore the potential of the model, the model is further extended to the field of wax removal. The melting process of the wax layer due to heat conduction is simulated by coupling the thermodynamic model and the phase change model. Interesting behaviors such as wax layer cracking, droplet detachment, and thermally driven flow instabilities are captured, providing insights into wax deposition mitigation strategies. This study provides an effective numerical model for bubbly flows in petroleum engineering and lays a research foundation for extending the application of the SPH method in other engineering fields, such as multiphase reactor design and environmental fluid dynamics. Full article

A variety of Offshore Floating Photovoltaics (OFPVs) applications rely on the capacity of their floating support structures displacing in the shape of surface waves to reduce extreme wave-induced loads exerted on their floating-mooring system. This wave-adaptive displacement behaviour is typically realized through two principal design approaches, either by employing slender and continuously deformable structures composed of highly elastic materials or by decomposing the structure into multiple floating rigid pontoons interconnected via flexible connectors. The hydrodynamic behaviour of these structures is commonly analyzed in the literature using potential flow theory, to characterize wave loading, whereas in order to deploy such OFPV prototypes in realistic marine environments, a high-fidelity numerical fluid–structure interaction model is required. Thus, a versatile three-dimensional numerical scheme is herein presented that is capable of handling non-linear fluid-flexible structure interactions for Very Flexible Floating Structures (VFFSs): Multibody Dynamics (MBD) for modularized floating structures and floating-mooring line interactions. In the present study, this is achieved by employing the Smoothed Particles Hydrodynamics (SPH) fluid model of DualSPHysics, coupled both with the MBD module of Project Chrono and the MoorDyn+ lumped-mass mooring model. The SPH-MBD coupling enables modelling of large and geometrically non-linear displacements of VFFS within an Applied Element Method (AEM) plate formulation, as well as rigid body dynamics of modularized configurations. Meanwhile, the SPH-MoorDyn+ captures the fully coupled three-dimensional response of floating-mooring and floating-floating dynamics, as it is employed to model both moorings and flexible interconnectors between bodies. The coupled SPH-based numerical scheme is herein validated against physical experiments, capturing the hydroelastic response of VFFS, rigid body hydrodynamics, mooring line dynamics, and flexible connector behaviour under wave loading. The demonstrated numerical methodology represents the first validated Computational Fluid Dynamics (CFD) application of moored VFFS in three-dimensional domains, while its robustness is further confirmed using modular floating systems, enabling OFPV engineers to comparatively assess these two types of wave-adaptive designs in a unified numerical framework. Full article

Recent studies have demonstrated the potential of hyperbolic paraboloid (hypar), a doubly curved geometry, in coastal engineering applications. Predicting pressure distribution, critical for subsequent finite element analysis, on such novel three-dimensional structures require Computational Fluid Dynamics (CFD) simulations, which are computationally intensive. To address this challenge, the current study develops an artificial neural network (ANN) surrogate to predict pressure distributions on hypar free-surface breakwaters (FSBWs) under solitary wave loading. Using Smoothed Particle Hydrodynamics (SPH) as the CFD tool, simulations generate the supervised learning dataset, where inputs are the hypar warping $R_n$, breakwater draft $d_r$, and wave height $H$. The targets consist of two $30\times 30$ pressure maps at wave arrival (hydrostatic) and peak, together with the wave rise time $\left\{\mathit{P}\right(t_0)$, $\mathit{P}\left(t_{peak}\right)$, $\mathsf{\Delta }t=t_{peak}-t_0\}$. Three architectures, FNN, CNN, and DeepONet, are trained with homoscedastic uncertainty loss weighting, each at two parameter sizes ($~50\mathrm{k}$ and $~500\mathrm{k}$). Results for training and testing show that all models achieve low errors, with models with ~50k parameters found to be sufficient, and scaling to ~500k yields some generalization improvement. Further reducing the parameters (~5k) degrades accuracy for all models, with DeepONet proven most robust to parameter size reduction. Overall, this study introduces a novel SPH-ANN workflow for predicting wave pressures on hypar FSBWs, where inference on new samples occurs in a few milliseconds per sample, delivering orders-of-magnitude speedups relative to running new SPH simulations. This computational efficiency enables rapid design iteration and optimization of hypar FSBWs, facilitating their potential deployment in coastal defense. Full article

Subsoiling is an important technology in conservation tillage. The disturbance characteristics of paddy soil were simulated by smoothed particle hydrodynamics (SPH) in this paper in order to explore the optimal tillage depth of paddy soil in a rice–wheat rotation area. Firstly, a subsoiling experiment with five tillage depths was carried out by a self-made multi-functional in situ test-rig facility. Then, a three-layer-soil subsoiling model of a cultivated layer, plow pan, and subsoil layer was established based on the SPH method. Finally, the soil disturbance characteristics were analyzed from macroscopic and microscopic perspectives. The results showed that the average draft force in simulation was consistently lower than in the field, with a maximum error of 18.71%, and the field draft force fluctuated greatly. The soil block above the tine was not lifted up as a big block but broken into many small soil blocks and then lifted up, resulting in different displacements of the soil particles, but the relative position was unchanged from top to bottom. The particle displacements were concentrated above the tine, the stress was concentrated around the tine, while the velocity and acceleration were closely attached to the subsoiler. A “mole cavity” at 25 and 30 cm tillage depths existed at the bottom of the disturbance, which was consistent with the finding in the field. The disturbance area and specific draft were maximum and minimum at 20 cm tillage depth, respectively. These findings suggest that the optimal tillage depth was 20 cm for the rice–wheat rotation area. The results of the analysis provide a theoretical basis for the optimal design of subsequent subsoiling. Full article