Search Results (1,319)

Extracting high-quality surface wave dispersion curves from crosscorrelation functions (CCFs) of ambient noise data is critical for seismic velocity inversion and subsurface structure interpretation. However, the non-uniform spatial distribution of noise sources may introduce spurious noise into CCFs, significantly reducing the signal-to-noise ratio (SNR) of empirical Green’s functions (EGFs) and degrading the accuracy of dispersion measurement and velocity inversion. To mitigate this issue, this study aims to develop an effective denoising approach that enhances the quality of CCFs and facilitates more reliable surface wave extraction. We propose a fractional Laplacian-based adaptive progressive denoising (FLAPD) method that leverages local gradient information and a fractional Laplacian mask to estimate noise variance and construct a fractional bilateral kernel for iterative noise removal. We applied the proposed method to the CCFs from 79 long-period seismic stations in Shandong, China. The results demonstrate that the denoising method enhanced the data quality substantially, increasing the number of reliable dispersion curves from 1094 to 2196, and allowing an increased number of temporal sampling points to be retrieved from previously low-SNR curves. This helps to expand the spatial coverage and results in a more accurate velocity inversion result than that without denoising. This study provides a robust denoising solution for ambient noise tomography in regions with complex noise source distributions. Full article

Most existing depth-averaged granular flow theories assume that dry, cohesionless granular materials are incompressible, with the void ratio among grains remaining spatially and temporally invariant. However, recent large-scale experiments showed that the pore space among grains varies both spatially and temporally. This study, therefore, incorporates the effects of granular dilatancy to perform analytical and numerical investigations of granular flows down inclined planes. A high-resolution shock-capturing scheme is employed to numerically solve the compressible depth-averaged equations for temporal and spatial evolution of the flow thickness and depth-averaged velocity, as well as depth-averaged volume fraction. Additionally, a traveling wave solution is constructed. The comparison between analytical and numerical solutions confirms the accuracy of the numerical solution and also reveals that the gradient of the solids volume fraction, induced by granular dilatancy, results in a gentler slope of the granular front, in agreement with experimental observations. Furthermore, this numerical framework is applied to investigate granular flows transitioning from an inclined plane onto a horizontal run-out pad. The numerical solution shows that the incorporation of granular dilatancy causes the shock wave to propagate upstream more rapidly. As a result, the position and morphology of the mass deposit exhibit closer alignment with experimental data. Full article

This study presents an assessment scheme for haptic interaction systems based on Hamiltonian energy prediction, which contributes to procedures applied to neurorehabilitation. It focuses on robotic systems involving human participation in the control loop, where uncertainty may compromise both stability and task performance. To address this, a regression-based model is proposed to predict total mechanical energy using the robot’s position and velocity signals during active interaction. Synthetic data generated via TimeGAN are used to enhance model generalization. Advanced machine learning techniques—particularly Gradient Boosting—demonstrate outstanding accuracy, achieving an MSE of $0.628\times {10}^{-10}$ and $R^2=0.999976$. These results validate the use of synthetic data and passive-mode-trained models for assessing motor performance in active settings. The method is applied to a patient diagnosed with Guillain-Barré Syndrome, using the Hamiltonian function to estimate energy during interaction and objectively assess motor performance changes. The results obtained show that our proposal is of great relevance since it solves a current field of opportunity in the area. Full article

For runoff-generated debris flow continuum mechanics-based early warning models, the computational unit must satisfy the homogeneity assumption of continuum mechanics. Although traditional grid cells meet the homogeneity assumption as computational units, they segment channel geomorphological functional reaches, weaken the clustered mobilization of sediment sources, and constrain efficiency due to grid-by-grid calculations. To address these limitations, we construct a Froude number ($Fr$) calculation model constrained by key factors such as the channel cross-sectional geometry and topographic parameters. The absolute deviation of $Fr$ is used as a criterion for homogeneity within the computational unit. By combining critical shear stress theory and velocity perturbation, physical thresholds for the criteria are derived. A physical model-based method for automatically delineating homogeneous channel units (${CU}_j$) is proposed, ensuring that the geometric features and hydrodynamic parameters within ${CU}_j$ are homogeneous, while ensuring heterogeneity between adjacent ${CU}_j$. Comprehensive multi-scale validation in Yeniu Gully, a typical debris flow catchment in Wenchuan County, demonstrates that parameters such as longitudinal gradient, cross-sectional area, flow depth, and shear stress remain relatively homogeneous within each ${CU}_j$ but differ significantly between adjacent ${CU}_j$. Furthermore, the proposed method can stably characterize key channel geomorphological functional units, such as bends, confluences, abrupt width changes, longitudinal gradient changes, erosion segments, and deposition segments. Sensitivity analysis demonstrates that the method satisfies both robustness and universality under various conditions of rainfall intensity, runoff coefficient, and Manning’s roughness coefficient. Even under the most unfavorable extreme conditions, the accuracy of ${CU}_j$ delineation exceeds 88.64%, indicating high reliability and suitability for deployment in various debris flow catchments. The proposed framework for defining ${CU}_j$ resolves the conflict in traditional computational units between the “continuum model homogeneity requirement” and “geomorphological functional unit continuity,” providing a more rational and efficient computational environment for runoff-generated debris flow continuum mechanics-based early warning models. Full article

We use deep Physics-Informed Neural Networks (PINNs) to simulate stratified forced convection in plane Couette flow. This process is critical for atmospheric boundary layers (ABLs) and oceanic thermoclines under global warming. The buoyancy-augmented energy equation is solved under two boundary conditions: Isolated-Flux (single-wall heating) and Flux–Flux (symmetric dual-wall heating). Stratification is parameterized by the Richardson number $(R_i\in [-1,1\left]\right),$ representing $\pm 2 °\mathrm{C}$ thermal perturbations. We employ a decoupled model (linear velocity profile) valid for low-Re, shear-dominated flow. Consequently, this approach does not capture the full coupled dynamics where buoyancy modifies the velocity field, limiting the results to the laminar regime. Novel contribution: This is the first deep PINN to robustly converge in stiff, buoyancy-coupled flows ($\mid R_i\mid \le 1$) using residual connections, adaptive collocation, and curriculum learning—overcoming standard PINN divergence (errors $>28\%$). The model is validated against analytical ($R_i=0$) and RK4 numerical ($R_i\ne 0$) solutions, achieving $L_2$ errors $\le 0.009\%$ and $L_{\mathrm{\infty }}$ errors $\le 0.023\%$. Results show that stable stratification $(R_i>0)$ suppresses convective transport, significantly reduces local Nusselt number ($Nu$) by up to $100\%$ (driving $Nu$ towards zero at both boundaries), and induces sign reversals and gradient inversions in thermally developing regions. Conversely, destabilizing buoyancy $(R_i<0)$ enhances vertical mixing, resulting in an asymmetric response: $Nu$ increases markedly (by up to $140\%$) at the lower wall but decreases at the upper wall compared to neutral forced convection. At $5–10\times$ lower computational cost than DNS or RK4, this mesh-free PINN framework offers a scalable and energy-efficient tool for subgrid-scale parameterization in general circulation models (GCMs), supporting SDG 13 (Climate Action). Full article

The Aeolian Archipelago, located in the southern margin of the Tyrrhenian Sea, is a key area to investigate the interplay between regional active fault systems and volcanic activity, making it a focal point for geodynamic studies. In particular, Salina Island lies at the intersection of two major tectonic structures: the Sisifo–Alicudi fault system in the western sector and the Aeolian–Tindari–Letojanni fault system in the central sector both exert a significant influence on the region’s deformation patterns. Detecting these signals requires high-quality GNSS data, yet the performance of newly installed stations in tectonic environments must be rigorously assessed. Between June 2023 and February 2024, a new continuous local GNSS network, which consists of five stations, Salin@Net, was established, on Salina Island. The central scientific objective of this study is to verify whether the new GNSS network achieves the data quality necessary for reliable geodetic monitoring and to evaluate its potential to resolve strain gradients in the area. We performed an extensive performance analysis of Salin@net GNSS stations, analyzing data quality, encompassing assessments of multipath effect, signal-to-noise ratio, observation continuity, and cycle slip occurrences, alongside GNSS position time series. These metrics were compared against the ISAL-RING station and benchmarked International GNSS Service (IGS) standards. Results show that the newly installed stations consistently meet the required standards, delivering robust and reliable measurements that are comparable to those of the RING GNSS continuous network. Positioning time series, processed in the ITRF14, indicate that the precision of the derived velocity estimates is comparable to that of standard continuous stations, although longer time spans are required to better constrain linear velocity estimates. Finally, spherical wavelet analysis demonstrates that the geometry of Salin@net significantly improves the spatial resolution of the strain field across the Aeolian–Tindari–Letojanni fault system and enhances resolution along the Sisifo–Alicudi fault, underscoring the role of dense, small-aperture GNSS networks in tectonic environment. Full article

To address the issue of restricted grout diffusion caused by seepage effects during grouting in sandy soil layers, this study proposes an optimised grouting method for water-based polyurethane-cement composite grout (WPU-CS) under vacuum-pressure synergy. By establishing a porous medium flow model based on the mass conservation equation and linear filtration law, the influence mechanism of cement particle seepage effects was quantitatively characterised. An orthogonal test (L₉(3⁴)) optimised the grout composition, determining the optimal parameter combination as the following: water-to-cement ratio 1.5:1, polyurethane-to-cement ratio 5~10%, magnesium aluminium silicate content 1%, and hydroxypropyl methylcellulose content 0.15%. Vacuum permeation grouting tests demonstrated that compared to pure cement slurry, WPU-CS reduced filter cake thickness by 80%, significantly suppressing the leaching effect (the volume fraction $\delta$ of cement particles exhibited exponential decay with increasing distance $r$ from the grouting end, and the slurry front velocity gradually decreased). Concurrently, the porosity $\varphi$ in the grouted zone showed a gradient distribution (with more pronounced porosity reduction near the grouting end). When vacuum pressure increased from −10 kPa to −30 kPa, slurry diffusion distance rose from 11 cm to 18 cm (63.6% increase). When grouting pressure increased from 20 kPa to 60 kPa, diffusion distance increased from 8 cm to 20 cm (150% increase). The study confirms that synergistic control using WPU-CS with moderate grouting pressure and high vacuum effectively balances seepage suppression and soil stability, providing an innovative solution for efficient sandy soil reinforcement. Full article

Microchannels can create disturbed flow patterns by altering pressure gradients, shear forces, and flow symmetry, which are essential in the design of microfluidic devices and, hence, blood-contacting devices. The effect of asymmetric stenosis on pressure, wall shear stress, and velocity in rectangular and circular microchannels with same operating conditions was analyzed in this study using three-dimensional (3D) steady laminar computational fluid dynamics (CFD) simulations. Asymmetric flow patterns induced by asymmetric stenosis are of particular importance and remain underexplored, especially in the context of multiple constrictions. This is, to our knowledge, is the first systematic CFD comparison of multiple asymmetric stenoses in circular microchannels directly contrasted with rectangular and single-stenosis cases under identical settings. Several parameters, such as wall shear stress (WSS), pressure, and velocity distributions, were analyzed in various stenotic and non-stenotic geometries. These microchannel models, while not reflecting real blood vessels themselves nor exhibiting wall compliance, pulsatility, or non-Newtonian rheology, replicate important mechanical characteristics of stenosis-mediated flow disturbance. Single and multiple asymmetric stenoses create flow patterns that are similar to those of vascular pathologies. For this reason, these channels should be considered as simplified device-scale models of vascular phenomena as opposed to realistic, in vitro vascular models. The results showed that asymmetric stenosis creates asymmetric velocity peaks and elevated WSS, which are more evident in the case of circular configurations with double asymmetric stenosis. The findings will help design microfluidic devices that mimic unstable flow characteristics that occur in stenotic conditions, and assist in testing clinical devices. In this study, two fabrication-ready microchannel designs under fixed operating conditions (identical inlet velocity and fluid properties) that reflect common microfluidic use were compared. Consequently, all pressure, velocity, and WSS outcomes are interpreted as device-scale responses under fixed velocity, rather than a fundamental isolation of cross-section shape, which would require matched hydraulic diameters or flow rates. This study is explicitly device-oriented, representing a fixed operating point rather than a strict geometric isolation. Accordingly, the results are also expressed with dimensionless loss coefficients (Ktot and Klocal) to enable scale-independent, device-level comparison. Full article

Flow regimes of vertical upflow for slightly cohesive Geldart A powders at high solids mass flux ($G_s$$\gtrsim$ 500 kg/m²s) are not fully resolved. In particular, Dense Suspension Upflow (DSU) as a distinct flow regime and its transition boundaries are not broadly accepted. Furthermore, the locus of the pressure gradient minimum, which is the broadly accepted dense–dilute transition at low $G_s,$ requires validation at high $G_s$. In our recent work, by adapting the phase map of Wirth and by Eulerian modeling, DSU was defined as a distinct flow regime with gross upflow of solids and with granular temperature at the wall greater than that in the bulk. This study has further validated the definition of DSU and its transition boundaries by extending the modeling to areas not fully explored in the earlier work. Furthermore, this study has identified (a) the possibility of a phase of DSU between fast fluidization and turbulent regime at all $G_s$; and (b) the need to review the suitability of the locus of the pressure gradient minimum as the dense–dilute transition at high $G_s$. Additionally, our work has demonstrated (a) a new provisional correlation that the upper transport velocity for Geldart A powders is significantly greater than hitherto predicted; and (b) the slip velocity in the transport regimes increases with $G_s$ to peak within fast fluidization and falls thereafter to attain low multiples of the terminal settling velocity within DSU. Full article

Beer fermentation is a critical process that directly influences product quality and flavor. However, traditional fermentation practices often rely on empirical methods, leading to prolonged production cycles and inconsistent product quality. This study presents a multiphysics-coupled simulation model that integrates computational fluid dynamics (CFD) with fermentation reaction kinetics to address challenges in temperature control and monitoring in large-scale fermenters. The model incorporates the Navier–Stokes equations for fluid flow, energy equations for heat transfer, fermentation kinetics for sugar metabolism, and a yeast cell proliferation model based on population balance theory. The model is validated through experiments at both lab scale (0.3 m³) and industrial scale (375 m³). Statistical analysis shows excellent agreement, with coefficients of determination (R²) for alcohol and sugar content reaching up to 0.99 and 0.96 at the lab scale, and 0.93 and 0.85 at the industrial scale, respectively. Key quantitative results from the industrial-scale validation demonstrate that the model accurately predicts the primary fermentation dynamics: within a 100 h period, alcohol concentration increased from 0% to approximately 6%, while sugar content decreased from 13 °P to 2 °P, closely matching experimental data. Crucially, the simulation captures a significant temperature overshoot at approximately 48 h, where the peak temperature at the top of the fermenter reached 16.01 °C (a 3 °C overshoot above process requirements). This pronounced vertical temperature gradient, arising from symmetry-breaking thermal conditions on the fermenter walls, was found to induce strong, asymmetric natural convection with flow velocities up to 13.2 mm·s⁻¹, revealing spatial heterogeneities that are critical for optimizing fermenter design. At the lab scale, the simulation also accurately captures the observed quadratic temperature rise, further confirming the model’s robustness. This study provides a theoretical foundation for optimizing cooling jacket configurations and control strategies, ultimately improving fermentation efficiency and ensuring consistent product quality. Full article

This article presents the effects of novel electron beam hardening (EBH) process parameters in terms of residual stresses (RSs) and microstructure modification in as-received C45 cylindrical specimens. The EBH was performed using continuous irradiation with power in the range of $720–2070\mathrm{W}$ on an Evobeam µEBW Cube 400 machine. A distinctive feature of the novel surface hardening process is the linear scanning mode in the axial direction of the treated cylindrical surface, which makes it suitable for machining shafts and axles. Using a one-factor-at-a-time technique, the individual effects of the electron beam current ${\mathrm{I}}_{\mathrm{b}}$, workpiece peripheral velocity ${\mathrm{v}}_{\mathrm{p}}$, scanning frequency (SF), and focal length (FL) on the RSs and microstructure in surface layers were evaluated. The X-ray diffraction results, scanning electron microscopy (SEM) images, and phase analyses confirmed the significant potential of the EBH process for forming compressive RSs due to martensitic transformation in the surface zone and gradient microstructure in terms of structure and phase composition. The measured maximum compressive axial and hoop RSs of $-289.5$ and $-345\mathrm{M}\mathrm{P}\mathrm{a}$, respectively, and compressive zone at a depth of approximately 0.3 mm correlate with the phase transformation region at a depth of approximately 0.2 mm. Based on the results for RSs and microstructure modification, the limitations with respect to the suitable operating parameter values were established. After excluding these operating parameter values, the following suitable ranges of the operating parameters were determined: ${\mathrm{I}}_{\mathrm{b}}\in \left(16,36\right)\mathrm{m}\mathrm{A},{\mathrm{v}}_{\mathrm{p}}\in \left(18,45\right)\mathrm{m}\mathrm{m}/\mathrm{s}$, $\mathrm{S}\mathrm{F}\in (5000,\mathrm{20,000})\mathrm{H}\mathrm{z}$, and $\mathrm{F}\mathrm{L}\in (+5,-5)\mathrm{m}\mathrm{m}$. The specified ranges are the basis for conducting a planned experiment on the novel EBH process. Full article

This work explores the thermal management of cylindrical lithium-ion batteries used in electric vehicles by introducing a combined cooling approach that couples phase-change materials (PCM) with a liquid-based cooling loop. Fluent-based numerical simulations are conducted to first examine the effects of battery spacing and ambient temperature on PCM cooling performance, from which the optimal spacing is identified. Building on this foundation, a coupled PCM–liquid cooling model is developed to evaluate the impacts of liquid-channel inlet configuration, coolant flow velocity, and inlet temperature. Results show that varying the inlet position has little impact when the outlet is fixed. Increasing coolant velocity lowers the peak cell temperature and the extent of PCM melting but enlarges the temperature difference, reaching 5.03 °C at 0.0075 m/s, which exceeds the recommended safety threshold. Lowering the coolant inlet temperature further decreases the peak temperature but also deteriorates temperature uniformity. To simultaneously suppress both the maximum temperature (T_maxi) and temperature gradient, two structural optimization schemes are proposed. Among them, distributing liquid cooling plates evenly above and below the battery pack achieves the best overall performance. The findings demonstrate the strong cooling potential of the PCM–liquid hybrid system and offer theoretical support for the design and optimization of advanced battery thermal management systems. Full article

The riblet surface is a passive turbulence drag reduction technology with promising aerospace application prospects. To investigate the drag reduction effects of riblets under flow conditions more representative of actual aircraft surfaces, this study establishes an adverse pressure gradient environment at moderate-to-high Reynolds numbers. Symmetrically arranged two testing plates with riblets’ surface and smooth surface, hot-wire anemometry is employed to measure the skin friction drag of both plates to get a direct measurement of the drag reduction rate. And the drag reduction mechanism is analyzed through burst events detection and coherent structure’s inclination angle. The measurement results indicate that the adverse pressure gradient itself leads to a reduction in wall friction, and the turbulent boundary layer velocity profile deviates from the standard logarithmic law, rendering the Clauser chart method unsuitable for estimating the friction velocity. The adverse pressure gradient contributes positively to the drag reduction rate of riblets, while the increase in Reynolds number in this experiment has no substantial effect. For the near wall structures, their asymmetrical movement of ejection and sweep and investigated by VITA. The significant decrease in burst frequency and increase in coherent structure inclination angle in the turbulent boundary layer over the riblet surface are identified as the primary reasons for reduced wall friction, with these changes being particularly pronounced under adverse pressure gradient conditions. Full article

The Neptunea cumingii (N. cumingii) fishing industry has long relied on expensive and perishable skate (Raja porosa) meat as bait. The unknown chemical attraction mechanism has hindered the development of artificial alternatives. This study employed untargeted metabolomics to analyze the chemical composition of skate meat and combined quantitative behavioral analysis to identify four key attractant compounds. These compounds were taurine, glutamate (Glu), inositol, and lactate. A standardized behavioral assessment system was established using the three parameters of response time, displacement distance, and movement velocity. This system enabled precise quantification of attraction efficacy. Concentration-gradient experiments determined the optimal concentration for all four compounds as 0.1 M. Taurine exhibited the strongest single-compound activity. It reduced response time by 50% and increased displacement distance by 164.5%. The mixture of four compounds at 0.1 M produced significant synergistic effects. The mixture achieved a comprehensive score of 93.6. This score approached that of natural skate meat at 94.8. All behavioral parameters improved by over 69% compared to the best single compound. These findings reveal the key attractant components in skate meat. They provide a scientific basis for developing efficient and stable artificial attractants. This research holds substantial value for promoting sustainable development of the N. cumingii fishing industry. Full article

A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space, which can easily lead to gradient vanishing and premature convergence. Compared with traditional heuristic algorithms relying on a population-based parallel search, such as GA, GWO, DE, etc., the Besiege and Conquer Algorithm (BCA) employs a one-spot update strategy that provides a certain level of global optimization capability but exhibits clear limitations in search flexibility. Specifically, it lacks fast detection, fast adaptation, and fast convergence. First, the fixed sinusoidal amplitude limits the accuracy of fast detection in complex regions. Second, the combination of a random location and fixed perturbation range limits the fast adaptation of global convergence. Finally, the lack of a hierarchical adjustment under a single parameter (BCB) hinders the dynamic transition from exploration to exploitation, resulting in slow convergence. To address these limitations, this paper proposes a Flexible Besiege and Conquer Algorithm (FBCA), which improves search flexibility and convergence capability through three new mechanisms: (1) the sine-guided soft asymmetric Gaussian perturbation mechanism enhances local micro-exploration, thereby achieving a fast detection response near the global optimum; (2) the exponentially modulated spiral perturbation mechanism adopts an exponential spiral factor for fast adaptation of global convergence; and (3) the nonlinear cognitive coefficient-driven velocity update mechanism improves the convergence performance, realizing a more balanced exploration–exploitation process. In the IEEE CEC 2017 benchmark function test, FBCA ranked first in the comprehensive comparison with 12 state-of-the-art algorithms, with a win rate of 62% over BCA in 100-dimensional problems. It also achieved the best performance in six MLP optimization problems, showing excellent convergence accuracy and robustness, proving its excellent global optimization ability in complex nonlinear MLP optimization training. It demonstrates its application value and potential in optimizing neural networks and deep learning models. Full article