Search Results (544)

This study examines the impact of fluid excitation forces on the dynamic response of high-temperature molten salt circulating primary pump rotor systems. Unsteady simulations were conducted in ANSYS CFX to characterize pressure pulsation and radial forces across all impeller stages. Critical speeds and vibration modes were subsequently analyzed using SAMCEF to evaluate transient responses under varying flow rates. Key findings: Numerical performance predictions align with experimental data within a 5% error margin. The first-stage impeller exhibits a pressure-pulsation frequency of twice the rotational frequency (2 f_R), while the fifth-stage impeller oscillates at the guide-vane passing frequency (f_DPF). Under rated conditions, the radial force on the first stage is significantly larger than on the other stages. As the flow rate varies, the radial forces on the first and fifth stages change in opposite directions due to rotor–stator interaction. The rotor system’s critical speed (1894.5 r/min) exceeds the operating speed, eliminating resonance risk. Without radial forces, impeller displacements follow elliptical trajectories with maximum amplitude at the fifth stage. When radial forces are included, displacements become irregular, and shaft constraints cause peak displacement at the fourth stage. These findings provide useful insight for the design and analysis of molten salt primary pump rotor systems. Full article

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

In freeform optical metrology, wavefront fitting over non-circular apertures is hindered by the loss of Zernike polynomial orthogonality and severe sampling grid distortion inherent in standard conformal mappings. To address the resulting numerical instability and fitting bias, we propose a unified framework curve-shortening flow (CSF)-guided progressive quasi-conformal mapping (CSF-QCM), which integrates geometric boundary evolution with topology-aware parameterization. CSF-QCM first smooths complex boundaries via curve-shortening flow, then solves a sparse Laplacian system for harmonic interior coordinates, thereby establishing a stable diffeomorphism between physical and canonical domains. For doubly connected apertures, it preserves topology by computing the conformal modulus via Dirichlet energy minimization and simultaneously mapping both boundaries. Benchmarked against state-of-the-art methods (e.g., Fornberg, Schwarz–Christoffel, and Ricci flow) on representative irregular apertures, CSF-QCM suppresses area distortion and restores discrete orthogonality of the Zernike basis, reducing the Gram matrix condition number from >900 to <8. This enables high-precision reconstruction with RMS residuals as low as $3\times {10}^{-3}\lambda$ and up to 92% lower fitting errors than baselines. The framework provides a unified, computationally efficient, and numerically stable solution for wavefront reconstruction in complex off-axis and freeform optical systems. Full article

As global energy demand rises, developing unconventional oil and gas resources has become a strategic priority, with horizontal well technology playing a key role. However, wellbore instability during drilling often leads to irregular geometries, such as enlargement or elliptical deformation, causing issues like increased friction and stuck-pipe incidents. Most studies rely on idealized, regular wellbore models, leaving a gap in understanding cuttings transport in irregular wellbore conditions. To address this limitation, this study employs a coupled CFD-DEM approach to investigate cuttings transport in enlarged wellbores by modeling the two-way interactions between drilling fluid and cuttings. The study analyzes the impact of various factors, including drilling-fluid flow rate, drill pipe rotational speed, rheological parameters, wellbore enlargement ratio, and ellipticity, on wellbore cleaning efficiency. The result indicates that increasing the flow rate in conventional wellbores reduces cuttings volume by 75%, while in wellbores with a 0.7 enlargement ratio, the same flow rate only reduces it by 37.8%, highlighting the limitations of geometric complexity. In conventional wellbores, increasing drill pipe rotation reduces cuttings volume by 42.6%, but in enlarged wellbores, only a 13% reduction is observed, indicating that rotation alone is insufficient in large wellbores. Optimizing drilling fluid rheology, such as by increasing the consistency coefficient from 0.3 to 1.2, reduces cuttings volume by 58.78%, while increasing the flow behavior index from 0.4 to 0.7 results in a 38.17% reduction. Although higher enlargement ratios worsen cuttings deposition, a moderate increase in ellipticity improves annular velocity and enhances transport efficiency. This study offers valuable insights for optimizing drilling parameters in irregular wellbores. Full article

To clarify how flow-channel configuration and wall spacing govern the hydrodynamic performance of an oscillating-hydrofoil biomimetic pumping device, this study conducted a systematic numerical investigation under confined-flow conditions. Using a finite-volume solver with an overset-grid technique, we compared pumping performance across three channel configurations and a range of channel–wall distances. The results showed that bidirectional-channel confinement suppresses wake deflection and irregular vorticity evolution, enabling symmetric and periodic vortex organization and thereby improving pumping efficiency by approximately 33.6% relative to the single-channel case and by 62.7% relative to the no-channel condition. Wall spacing exhibited a distinctly non-monotonic influence on performance, revealing two high-performance regimes: under extreme confinement (gap ratio $h/c=$ 1.4), the device attains peak pumping and thrust efficiencies of 19.9% and 30.7%, respectively, associated with a strongly guided jet-like transport mode; and under moderate spacing ($h/c=$ 2.2–2.6), both efficiencies remain high due to an improved balance between directional momentum transport and reduced vortex-evolution losses. These findings identify key confinement-driven mechanisms and provide practical guidance for optimizing flow-channel design in ultralow-head oscillating-hydrofoil pumping applications. Full article

Tubular pump systems (TPSs) represent a critical class of large-scale turbomachinery for low-head water transport, where mechanical reliability is often challenged by complex internal flow dynamics. Pressure pulsations in pump systems induce vibrations that adversely affect performance, emphasizing the need for effective control mechanisms to ensure stable operation. In tubular pumps, unsteady pressure pulsations are typically driven by rotor–stator interactions; however, the behavior of these pulsations in the absence of impeller rotation remains poorly understood. In this study, a novel comparative investigation is conducted to elucidate the effect of impeller rotation on pressure pulsations characteristic by examining two scenarios: normal impeller operation at rated speed and a completely stationary (zero-speed) impeller condition. Experiments were performed on a model low-head tubular pump, measuring dynamic pressures at four key locations across a range of flow rates. Time–frequency analysis using the continuous wavelet transform (CWT) and the wavelet coherence transform (WTC) was applied to delineate the unsteady pressure features. The results demonstrate that under normal rotation, pressure pulsations are dominated by pronounced periodic components at the impeller’s rotational frequency and its harmonics, with the strongest fluctuation amplitudes observed near the impeller outlet region. In contrast, with the impeller held stationary, these distinct periodic peaks vanish, replaced by broadband, irregular fluctuations. Crucially, WTC analysis revealed that significant coherence between the two operational states was confined to low frequencies (≈16.7–50 Hz), particularly at the impeller inlet, highlighting the presence of low-frequency dynamics likely associated with system-scale hydraulic compliance or inlet flow non-uniformity, independent of impeller rotation. These findings confirm the pivotal role of impeller rotation in generating periodic pressure pulsations while providing new insight into the underlying unsteady flow mechanisms in tubular pumps. Full article

Research shows that neurodegenerative processes do not develop from a single “broken” biochemistry process; rather, they develop when a complex multi-physics environment gradually loses its ability to stabilize the neuron via a collective action between the protein, ion, field and fluid dynamics of the neuron. The use of new technologies such as quantum-informed molecular simulation (QIMS), dielectric nanoscale mapping, fluid dynamics of the cell, and imaging of perivascular flow are allowing researchers to understand how the collective interactions among proteins, membranes and their electrical properties, along with fluid dynamics within the cell, form a highly interconnected dynamic system. These systems require fine control over the energetic, mechanical and electrical interactions that maintain their coherence. When there is even a small change in the protein conformations, the electric properties of the membrane, or the viscosity of the cell’s interior, it can cause changes in the high dimensional space in which the system operates to lose some of its stabilizing curvature and become prone to instability well before structural pathologies become apparent. AI has allowed researchers to create digital twin models using combined physical data from multiple scales and to predict the trajectory of the neural system toward instability by identifying signs of early deformation. Preliminary studies suggest that deviations in the ergodicity of metabolic–mechanical systems, contraction of dissipative bandwidth, and fragmentation of attractor basins could be indicators of vulnerability. This study will attempt to combine all of the current research into a cohesive view of the role of progressive loss of multi-physics coherence in neurodegenerative disease. Through integration of protein energetics, electrodynamic drift, and hydrodynamic irregularities, as well as predictive modeling utilizing AI, the authors will provide mechanistic insights and discuss potential approaches to early detection, targeted stabilization, and precision-guided interventions based on neurophysics. Full article

Sewage pumps often handle complex multiphase flows containing rigid solid particles and flexible fibrous debris. These fibers can deform, entangle, and alter the flow, leading to clogging and the uneven erosion of pump components. In this study, we use coupled CFD–DEM simulations (validated by experiments) to analyze how short flexible fibers move within a model sewage pump and how they influence pump erosion. We show that fibers injected near the inlet center tend to remain in the impeller region longer, especially as fiber diameter increases, causing greater contact with the impeller surface. When fibers coexist with sand-like particles, fibers become trapped near the impeller inlet and deflect incoming particles, creating additional collisions and irregular erosion patterns. In general, fibers alone induce minimal erosion, but their interaction with particles substantially amplifies impeller wear, producing more random pitting as fiber concentration rises. These findings highlight how fiber–particle interactions must be considered for reliable pump operation and design. Full article

With the growing heterogeneity of public transport systems, accurate representation of passenger service processes at bus stops shared by multiple operators has become increasingly important. This study develops and validates a microscopic model of passenger boarding and alighting at bus stops characterized by unstructured service patterns, diverse vehicle fleets, and irregular stopping positions. The approach focuses on individual passenger movements, enabling modeling of walking times from different waiting positions and assessing how passenger distribution and bus stopping positions affect total dwell time. Variables describing the boarding and alighting process, including waiting position, vehicle stopping position, individual boarding and alighting times, and passenger walking speed, were modeled as random variables following theoretical distributions (beta, logistic, log-normal, and normal). Bayesian estimation and bootstrap methods were applied to assess parameter stability and model fit. Field studies were conducted in two Polish cities (Kraków and Kielce) at 18 high-interference bus stop locations. Results indicate that the proposed probabilistic modeling approach enhances the accuracy of passenger flow representation and supports analysis of the effects of passenger dispersion and bus stopping position on service efficiency. The developed model can be used in microsimulation of bus stop operations, transport infrastructure design, and decision-making by transport management authorities. Full article

Functional steel surfaces engineered through tailored micro-nano structures are increasingly vital for various applications such as high-performance aerospace components, energy conversion systems and defense equipment. Femtosecond laser filament processing is a recently proposed remote fabrication technique, showing the capability of fabricating micro-nano structures on irregular and large-area surfaces without the need of tight focusing. Nevertheless, the mechanisms underlying the formation of filament-induced structures remain not fully understood. Here we systematically investigate the formation mechanisms of filament-induced micro-nano structures on stainless steel surfaces by processing stainless steel in three manners: point, line, and area. We clarify the decisive role of the unique core–reservoir energy distribution of the filament in the formation of filament-induced micro-nano structures, and reveal that ablation, molten metal flow, and metal vapor condensation jointly drive the structure evolution through a dynamic interplay of competition and coupling, giving rise to the sequential morphological transitions of surface structures, from laser-induced periodic surface structures to ripple-like, crater-like, honeycomb-like, and ultimately taro-leaf-like structures. Our work not only clarifies the mechanisms of femtosecond laser filament processed morphological structures on steels but also provides insights onto intelligent manufacturing and design of advanced functional steel materials. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

This study presents a numerical comparison between ANSYS AQWA (2023 R2) and the OrcaFlex package (OrcaWave + OrcaFlex) for a 10 × 10 × 2 m rectangular floating structure. The hydrodynamic coefficients and displacement/load RAOs obtained from the two solvers exhibit nearly identical behavior, with deviations below 1% across all six motion modes. Under irregular wave conditions (Hs = 7 m, Tp = 8 s, 0° heading) and three mooring line lengths (145, 150, and 155 m), both solvers produced comparable mean surge motions and mean mooring tensions. However, OrcaFlex predicted 40–50% higher peak tensions due to its fully dynamic representation of slack–taut transitions and snap loading effects, whereas AQWA’s quasi-static catenary formulation filtered out these short-duration peaks. These findings confirm that although the two solvers are highly consistent in frequency-domain hydrodynamics, their time-domain predictions diverge when nonlinear mooring behavior becomes dominant. The study provides a transparent and reproducible benchmarking framework for cross-validation of potential-flow-based tools used in floating offshore structure design. Full article

Agricultural inputs based on microalgae have been successfully tested at different stages of the crop cycle, from sowing to harvest, to enhance crop performance. In this study, biomass from Nannochloropsis gaditana and Thalassiosira sp. was obtained to evaluate its effect on wheat seed germination under two temperature conditions. Microalgal biomass was produced under controlled conditions (neutral pH, air flow of 1 L·min⁻¹, and a dilution rate of 0.2 day⁻¹). The biomass was characterized for its lipid, carbohydrate, protein, and ash content. Subsequently, its effect on germination, as well as on glucose and amylose content in wheat seedlings, was assessed. Four biomass concentrations were tested (0.0 [distilled water], 0.5, 1.0, and 1.5 g·L⁻¹) at two incubation temperatures (25 and 35 °C). Results showed that Thalassiosira sp. lightly promoted the germination rate more than N. gaditana. Germination parameters were negatively affected by high temperature, but treatments with Thalassiosira sp. alleviated this effect, showing values comparable to those obtained at the optimal temperature. Vigor parameters were improved compared with the control in both temperatures. Glucose and amylose contents exhibited irregular but consistent patterns. However, at a temperature of 35 °C, a slight conversion of starch to glucose could be observed. Overall, microalgal biomass did not significantly improve germination or its time variables, but it could exert a protective effect against high-temperature stress, particularly in the case of Thalassiosira sp. Full article

In this study, a three-dimensional computational fluid dynamic (CFD) model was constructed and validated against experimental data. The oxygen injection methods—specifically the primary air flow and secondary air flow—were investigated. The results demonstrate that primary air flow is the dominant factor in combustion. An increase of primary air from an φ of 0.20 to 0.75 lead to a rise in combustion peak temperature from 892.17 K to 1321.02 K, while simultaneously expending the flame combustion zone and enhancing the conversion of C₁₀H₈ and CH₄. Conversely, increasing the secondary air flow from 1 L/min to 7 L/min reduced the centrally measured temperatures form 886.09 K to 856.07 K due to irregular flow patterns, which expanded the central low-temperature region. While secondary air flow promoted more uniform reactant conversion and slightly suppressed intermediate products (e.g., soot, C₆H₆), its overall effect was secondary to that of the primary air. This research reveals a critical design insight: using primary air injection to introduce oxygen into the reactor is a reasonable approach. The findings provide valuable guidance for optimizing partial oxidation burner design and operating conditions to maximize tar conversion while maintaining reactor integrity. The study also establishes a rigorously validated CFD framework for analyzing complex reacting flows in tar thermochemical conversion reactors. Full article

Cellulite, or edematous-fibro-sclerotic panniculopathy (EFSP), is a multifactorial condition affecting most postpubertal women, characterized by surface irregularities with significant psychosocial impact. Its pathogenesis involves adipocyte metabolism, fibrous septa, microvascular dysfunction, extracellular matrix (ECM) remodeling, oxidative stress, and low-grade inflammation. Topical therapies remain among the most accessible approaches, acting on specific biological pathways. Osmotic and vSSasomodulatory formulations reduce edema and improve microcirculation, while methylxanthines such as caffeine and aminophylline promote lipolysis and enhance cutaneous blood flow. Retinoids mainly target the ECM, stimulating neocollagenesis and dermal thickening, with greater efficacy in early EFSP. Botanicals, including Centella asiatica, Rosmarinus officinalis, and Ginkgo biloba, provide antioxidant, anti-inflammatory, and venotonic effects. Randomized controlled trials consistently report modest but reproducible benefits: localized circumference reductions and improved elasticity, echogenicity, and orange-peel scores, all with excellent tolerability. Recent innovations, such as lipid nanoparticles, ultradeformable vesicles, and microneedle-assisted delivery, aim to enhance penetration, stability, and sustained bioactivity of established actives. Nonetheless, most studies are small, short-term, and heterogeneous, with limited ability to isolate the role of individual components or control for massage-related effects. Artificial intelligence offers opportunities to standardize outcome measures, optimize formulations, and personalize protocols. Overall, topical therapies are best positioned as safe, biologically active adjuncts within multimodal cellulite management. Full article