Search Results (454)

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article

The urgent global need for sustainable infrastructure drives the demand for high-value buildings and waste removal. This paper studies the feasibility of using recycled foam concrete aggregate (FCA) as a substitute for natural coarse aggregate (NCA) in concrete and studies its impact on rheology, mechanical degradation, shear resistance, and the full-range replacement ratio (0–100). The experimental results show that the monotonic change in the workability of fresh concrete determines the lubrication threshold at 60% replacement, which is driven by the volume proportion effect. Beyond this value, capillary suction dominates, and the viscosity rises rapidly. From a mechanical perspective, the porous structure of FCA is conducive to “internal curing” so that moisture is released from the drying interface, but it also becomes a source of defects that change the fault topology. Specifically, the critical transition of the shear failure mode shifts from the debonding of the interface to the crushing of the cross-particle aggregate. At this time, the shear capacity decreases substantially, experiencing a reduction of 71.8% when completely replaced. There is a strong correlation between ultrasonic pulse velocity (UPV), rebound number, and compressive strength, and a multivariate nonlinear regression model (R² > 0.85) with non-destructive strength prediction is ultimately obtained. Based on the balance between mechanical capacity and resource cyclability, an optimal alternative zone of 20% to 40% is proposed. This work not only provides a mechanism for multi-scale coupling between pore structure and structural properties but also provides a data-driven method for the safety assessment of lightweight recycled aggregate concrete (RAC). Full article

In the present work, the elastic modulus of several types of polymer nanocomposites has been analyzed with a semiempirical model which takes into consideration agglomerate formation and their impact on the nanocomposites’ mechanical performance. The nanocomposites under investigation were either hybrids with a combination of graphene oxide (GO) with multi-walled carbon nanotubes (MWCNTs) or carbon nanofibers (CNFs) at various loadings, or monofillers with varying nanoparticle sizes, at a constant nanofiller loading. In addition, the effect of the type of polymeric matrix on the same nanofiller combinations has been examined. The basic assumption of two phases, namely a matrix with finely dispersed nanoparticles coexisting with agglomerates, was analyzed. The elastic stiffness of the first phase was calculated by the Mori–Tanaka model, and hereafter a semiempirical model was utilized for the estimation of the agglomerates’ stiffness. Within the context of this model, it was shown that the agglomerates’ volume fraction, combined with the nanoparticles’ density, namely the nanoparticles’ volume fraction in the agglomerates and consequently the inclusions’/agglomerates’ enhanced modulus, may cause a substantial improvement in the Young’s modulus, which cannot be explained by conventional mechanical models. These results apply to both nanocomposite types, hybrids at various nanofiller loadings and monofillers with varying particle sizes. Full article

Porous stones are widely used in historical constructions and represent a major component of built cultural heritage. Their conservation commonly depends on multiple single-function products, such as consolidants, hydrophobic agents, biocides, or cleaning agents, which are often toxic and environmentally burdensome. This study performs an environmental assessment of a novel multi-function product designed for the sustainable conservation of porous stones and compares it with other conservation treatment alternatives. This product integrates green chemistry and nanotechnology through a water-based alkoxysilane modified with layered double hydroxide (LDH) particles. Laboratory and field tests on Portuguese monuments demonstrated suitable technical performance, including high substrate compatibility, effective consolidation depth, durable hydrophobicity, biocidal effect, and minimal visual alteration. To evaluate its environmental performance, a life cycle assessment (LCA) was carried out, from cradle-to-grave. The system boundaries encompassed production, application, and transportation stages, with 1 m² of treated sandstone surface as the functional unit. LCA was performed using CML-IA and ReCiPe methodologies in the SimaPro software. The results revealed the extent of environmental impacts of the novel product, addressing the multi-function strategy compared with conventional products and treatment scenarios. They identified critical life cycle stages for improvement to further enhance environmental performance across scenarios, particularly the influence of perfluorodecyltrimethoxysilane on the environmental burden of the novel product. Overall, this study demonstrates the value of LCA as a design and decision support tool for developing sustainable, multifunctional materials for cultural heritage conservation. Full article

Mine water treatment remains a long-term challenge due to high suspended solids, wide particle size distributions, and inflow variability, all of which stress solid–liquid separation systems. Hydrocyclones and filtration often fail not from insufficient capacity, but from the inability to handle dynamic influent behavior. This review integrates existing studies and reinterprets mine water treatment as a system performance issue, focusing on maintaining operability under fluctuating conditions. Evidence shows that high-solids mine water behaves as a concentrated multiphase flow, where particle interactions and flow changes lead to gradual shifts in separation behavior. For example, hydrocyclone efficiency ranges from 85 to 95%, and pressure drop increases by 0.5–5 kPa/h under continuous operation. Wear, clogging, and flow redistribution develop together, impacting the operational window of integrated treatment units. Key gaps remain in system performance under fluctuating loads and reliable performance under high-solids loading. The complexity of these interactions often leads to significant operational risk and performance variability in real-world conditions. Future research should focus on dynamic control strategies, multi-stage pre-separation, and advanced filtration designs to enhance system performance, long-term stability, and adaptability in real mining environments. Emerging technologies and new system configurations may further improve efficiency and reduce operational failure risks under extreme conditions. Full article

Atmospheric laser beam propagation is typically perturbed by the dual influences of aerosol particle systems and atmospheric turbulence. This joint perturbation induces intensity fluctuations in the transmitted optical field, which significantly degrades the performance of laser-based systems. This study integrates and improves upon existing simulation algorithms, establishing a coupled model that combines the Monte Carlo method and multi-phase screens. The model accurately characterizes optical field evolution and reveals that the impacts of scattering and turbulence on the scintillation index (SI) are not simply additive: turbulence perturbation enhances intensity fluctuations, leading to an increase in SI; however, as the energy proportion of scattered light rises, its statistical stationarity begins to dominate the optical field characteristics, stabilizing SI. Based on radiative transfer and Mie scattering theories, an analytical formula for single-scattering SI is derived, enabling direct calculation from fundamental parameters. Furthermore, a composite SI expression is established using the scattered-to-transmitted light intensity ratio. To address model deviations along the dimensions of visibility and turbulence strength, a sinusoidal compensation model and a logarithmic compensation model are proposed, respectively. Validation results verify the complementary and competitive mechanisms of scattering and turbulence in modulating intensity fluctuations. This research provides efficient theoretical tools and practical references for simulating and optimizing laser transmission in complex atmospheric environments. Full article

Soil salinity severely threatens global ecosystems and agriculture, making accurate monitoring an ongoing priority. Currently, efficiently utilizing multi-source datasets to enhance monitoring accuracy while minimizing computational resources remains a critical challenge. This study evaluated several modeling strategies, including full-dataset modeling, variance inflation factor (VIF), Boruta, particle swarm optimization, ant colony optimization and recursive feature elimination (RFE), and validated results across diverse regions (Almaty, Kazakhstan; Shandong, China). We further validated the results using multiple algorithms, including linear regression, partial least squares regression, extreme gradient boosting, k-nearest neighbor and random forest (RF), with topsoil (0–20 cm) electrical conductivity inverted via the optimal method. Results indicate that input feature numbers substantially impact model performance: regional-scale feature selection is indispensable, with RFE outperforming full-dataset modeling (R² improves by up to 0.28, while RMSE decreases by 2.21 dS m⁻¹) and VIF performing the worst. Transferability is also demonstrated in Almaty and Shandong. Additionally, the RF algorithm shows superior performance in soil salinity mapping (overall accuracy = 0.73; kappa coefficient = 0.65). And, the RFE and SHAP results highlight CRSI, BI, and MSAVI2 as particularly important predictors for estimating soil salinity in our study area. Collectively, this study highlights the critical importance of feature optimization and interpretability in soil attribute mapping through the integration of multi-source remote sensing data. Full article

Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space and total costs. Methods: This study formulates the Shipping Box Configuration Problem (SBCP), which aims to determine an optimal set of box types and dimensions for multi-product orders. To solve this problem, we propose a Particle Swarm Optimization (PSO)-based heuristic that dynamically designs box configuration rather than selecting from predefined sizes. Results: The proposed method is evaluated using real order data from two South Korean e-commerce companies with different product characteristics and existing box configurations. Computational results show that the PSO-based approach reduces total packaging and shipping costs and improves space utilization compared to current box configurations. The analysis also indicates that increasing the number of box types and reducing safety ratios generally lead to cost savings, although these effects must be balanced against operational complexity. Conclusions: The results suggest that adaptive box configuration design can improve both economic efficiency and environmental performance, providing practical guidance for e-commerce logistics managers seeking to optimize packaging strategies under operational constraints. Full article

To address the feasible issues in water treatment facilities such as low particle removal and overuse of chemical in flocculation–sedimentation treatment of complex sediment-laden particles in snowmelt and high-intensity rainfall water, this research presents a new multi-layered separation tank. Combining a multi-layer structural design and a synergistic enhancement mechanism flocculation–centrifugation, it is possible to engineer the tank to achieve improvement in the coexistence of the sediment and water. This study methodically examines the impact of the agitator speed, agitator height, and the number of blades on the flow field qualities and the effectiveness of the agitator in removing particles in the multi-layer separation tank. Computational fluid dynamics (CFD) simulation validation in comparison with hydro-calculations and laboratory experiments are used in a combined method. The findings show that there is strong agreement between numerical representation and experimental values in determining the optimal conditions of operation and the exact rate of dosage of polyaluminum chloride (PAC) and polyacrylamide (PAM). At these optimized conditions, the system achieves at a 75.25 percent removal rate of particles whose size ranges are 20–50 μm and turbidity of the effluent decreases to 10.6 NTU in 30 min of settling time. The proposed technology is more efficient than conventional coagulation processes in that effluent turbidity is reduced by 22.1% with same dosages of chemical additive indicating a higher performance of the proposed technology. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Reactive powder concrete (RPC) delivers outstanding mechanical performance and durability; however, it is commonly hindered by high cement consumption, elevated embodied carbon emissions, and high material costs. To mitigate these drawbacks, this study develops a low-carbon, cost-effective RPC incorporating high-volume class-F fly ash, a reduced silica fume dosage, conventional river sand, and an optimized steel fiber system. A systematic mix design framework, combining particle packing density with paste rheology optimization, was employed to balance workability, strength, and durability. The optimized mixtures were evaluated for compressive, splitting tensile, and flexural strength, as well as durability-related metrics, including water absorption rate and resistance to chloride penetration. Environmental impact and cost-effectiveness were further quantified via embodied carbon accounting and strength-normalized performance indices. The results show that well-designed high-volume fly ash RPC can achieve compressive strengths above 130 MPa while maintaining excellent impermeability, alongside substantial reductions in both material cost and carbon footprint relative to conventional RPC. In addition, mixed-size steel fibers further enhance mechanical performance through multi-scale crack bridging. Overall, this work provides a practical route to decouple ultra-high performance from high environmental burden, supporting the sustainable deployment of RPC in infrastructure engineering. 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

Microplastics (MP) are transported through rivers, acting as major conduits to oceans, yet standard transport models often fail to capture polymer-specific dynamics like settling and removal. This study proposes two novel analytical frameworks to address this: a modified Advection–Dispersion Equation (ADE) incorporating first-order sinking and removal, and a multi-phase model accounting for hydrodynamic–particle coupling. We derived exact closed-form solutions for a finite pulse input and validated the baseline model against established results. Our results demonstrate that the conventional ADE significantly overestimates peak MP concentrations, while the modified ADE reveals a “stretching” effect that extends the duration of ecosystem exposure. Our analysis indicates that sinking is the primary driver of mass loss to sediments, with higher sinking rates reducing aqueous concentrations by approximately 50% compared to non-settling scenarios. However, removal employs negligible influence during the initial pulse phase but shows cumulative impact over long transport distances. The study highlights the critical need to incorporate sediment accumulation terms into risk assessments, as ignoring sinking leads to underestimating benthic pollution and overestimating marine flux. Additionally, the multi-phase formulation provides a theoretical basis for modeling dense plastic spills where particles alter flow momentum. Full article

Time difference of arrival (TDOA) localization enables high-accuracy positioning by analyzing arrival-time differences of target signals at distributed radar nodes, whose performance strongly depends on radar node topology. However, existing studies tend to focus more on improving localization accuracy, while overlooking the impact of radar geometric layout and surveillance coverage on localization performance. To this end, this paper proposes a topology optimization method for a distributed radar system based on an improved non-dominated sorting multi-objective particle swarm optimization (NS-MOPSO) algorithm. A geometric localization model is developed for a distributed TDOA radar system. Based on this model, three optimization objectives are formulated, including minimizing geometric dilution of precision (GDOP), maximizing target coverage, and improving the geometric balance of node placement. These three objective functions are incorporated into the NS-MOPSO framework to achieve a more reasonable radar geometric distribution. To enhance the optimization performance, a series of strategies are adopted, such as non-dominated sorting for Pareto-based solution selection, an improved crowding-distance scheme to encourage balanced multi-objective optimization, and Gaussian mutation to increase solution diversity and reduce the risk of premature convergence. To validate the proposed method, both simulation studies and real-world experiments were conducted under different node deployment scenarios. The results show that the optimized topology achieves a 6.4% reduction in RMSPE and a 4.3% increase in the proportion of high-quality localization regions compared with the best-performing comparative method, while also demonstrating faster convergence and improved stability. These findings confirm the effectiveness and robustness of the proposed approach in enhancing localization accuracy, expanding effective coverage, and improving overall system performance. Full article

Superhydrophilic micro/nano-porous media have extensive applications in electronic thermal management and energy storage systems. Predicting two-phase pressure drop in complex porous structures is of great importance for system design and optimization while remaining highly challenging. This study systematically investigates the two-phase flow resistance characteristics of bi-porous microstructures with multiple particle sizes and porosities under varying boiling regimes. Experimentally, porous samples were fabricated via vacuum sintering using nickel powders and pore-forming agents (CaCl₂), which exhibit superhydrophilicity and enhanced wicking characteristics. A visualized experimental platform was constructed to investigate the impact of pore size combinations, flow velocities, and boiling states on pressure drop. The dataset obtained through multi-factor saturated boiling experiments was further used to derive a semi-empirical model for the two-phase flow pressure drop based on the classic Kozeny-Carman (K-C) and Akagi-Chisholm (A-C) correlations. Results show that the pore size combinations and boiling states have a significant impact on the resistance performance. The proposed model achieves an average prediction deviation below 15.7%, confirming its reliability and its effectiveness as a design framework for optimizing high-capillary-force porous wicks in advanced thermal management systems. Full article